KR101598324B1 - System and method for distributed input distributed output wireless communications - Google Patents

Abstract

A system and method for compensating for frequency and phase offsets in a multiple antenna system (MAS) transmission ("MU-MAS") with multiple users (MUs) is disclosed. The method includes transmitting a training signal from each antenna of a base station to one of a plurality of wireless client devices, generating frequency offset compensation data, receiving the frequency offset compensation data at the base station, Calculating MU-MAS precoder weights based on the frequency offset compensation data to pre-cancel the MU-MAS precoder weights, and calculating a number of MU-MAS precoder weights based on the channel characteristic data.

Description

[0001] SYSTEM AND METHOD FOR DISTRIBUTED INPUT DISTRIBUTED OUTPUT WIRELESS COMMUNICATIONS [0002]

This application is a continuation-in-part application of Serial No. 10 / 902,978, filed July 30, 2004.

The present invention relates generally to the field of communication systems. More particularly, the present invention relates to a system and method for distributed input-distributed output wireless communication using space-time coding techniques.

Spatial multiplexing and space-time coding are known as relatively new advances in wireless technology. One specific type of space-time coding is referred to as MIMO, which is an abbreviation for "Multiple Input Multiple Output" since several antennas are used at each end. By using multiple antennas for transmission and reception, multiple independent radio waves can be transmitted simultaneously within the same frequency range. The following documents provide an overview of MIMO.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL.21, NO. "From the Theory of IEEE", IEEE, Ayman Naguib, Senior Member, IEEE, April 2003: David Gesvert, Member, IEEE, MAnsoor Shafi, Fellow, IEEE, Da-shan Shiu, to Practice: An Overview of MIMO Space-Time Coded Wireless Systems ".

IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 50, NO. "Outdoor MIMO Wireless Channels: Models and Performance Prediction" by David Gesbert, Member, IEEE, Helmut Bolcskei, Member, IEEE, Dhananjay A. Gore, and Arogyaswami J. Paulraj, Fellow,

Basically, the MIMO technique is based on the use of space-distributed antennas to produce a parallel spatial data stream within the common frequency band. The propagation is transmitted in such a way that the individual signals can be demodulated separately at the receiver, even though they are transmitted within the same frequency band, which can result in statistical multiple independent (e.g., effectively separated) communication channels. Thus, in contrast to a standard wireless communication system that has been attempted to prohibit multipath signals (e.g., multiple signals at the same frequency, delayed in time and amplitude and phase changed in phase), MIMO has a higher For example, MIMO technology can be used to reduce power and signal-to-noise ratio (SNR) that conventional non-MIMO systems can only achieve at lower throughputs. ≪ RTI ID = SNR) < / RTI > conditions. This capacity is referred to as "What MIMO Delivers" in the website http://www.cdmatech.com/products/what mimo delivers.jsp of Qualcomm Incorporated (Qualcomm is one of the largest providers of wireless technology) Page: "MIMO is the only multi-antenna technology that increases the spectral capacity by delivering the highest data rate of more than twice the system per channel or MHz spectrum. For further details, for wireless LAN or WiFi Of the fourth generation MIMO technology delivers 315 Mbps or 8.8 Mbps / MHz in the 35 MHz spectrum, which delivers only 54 Mbps or 3.18 Mbps / MHz in the 17 MHz spectrum Is compared to the highest capacity of 802.11a / g (a uniform beam-forming or diversity technique). "

Typically, a MIMO system is faced with implementation limitations consisting of antennas of less than 10 per device (and therefore up to 10 times higher throughput in the network) for several reasons:

1. Physical limit: The MIMO antennas on a given device must each have sufficient separation between them to receive statistically independent signals. The increase in MIMO throughput can be indicated by antennas spaced into a uniform portion of the wavelength, and the efficiency drops rapidly as the antenna is closer, due to the lower MIMO throughput multipliers.

See, for example, the following references:

[1] D.-S. Shiu, G. J. Foschini, M. J. Gans, and J. M. Kahn, " Faidng correlation and its effect on the capacity of multielement antenna systems ", IEEE Trans. Comm., Vol. 48, no. 3, pp. 502-513, March 2000.

[2] V. Pohl, V. Jungnickel, T. Haustein, and C. von Helmolt, "Antenna spacing in MIMO indoor channels ", Proc. IEEE Veh. Technol. Conf., Vol 2, pp. 749-753, May 2002.

[3] M. Stoytchev, H. Safar, A.L. Moustakas, and S. Simon, "Compact antenna arrays for MIMO applications ", Proc. IEEE Antennas and Prop. Symp., Vol. 3, pp. 708-711, July 2001.

[4] A. Forenza and R. W. Heath Jr., "Impact of antenna geometry on MIMO communication in indoor clustered channels ", Proc. IEEE Antennas and Prop. Symp., Vol. 2, pp. 1700-1703, June 2004.

Also, for small antenna spacing, the mutual coupling effect can degrade the performance of MIMO systems.

See, for example, the following references:

[5] M. J. Fakhereddin and K. R. Dandekar, "Combined effect of polarization diversity and mutual coupling on MIMO capacity ", Proc. IEEE Antennas and Prop. Symp., Vol. 2, pp. 495-498, June 2003.

[7] P.N. Fletcher, M. Dean, and A. R. Nix, "Mutual coupling in multielement array antennas and their influence on MIMO channel capacity ", IEEE Electronics Letters, vol. 39, pp. 342-344, February 2003.

[8] V. Jungnickel, V. Pohl, and C. Von Helmolt, " Capacity of MIMO Systems with closely spaced antennas ", IEEE Comm. Lett., Vol. 7, pp. 361-363, August 2003.

[10] J. W. Wallace and M. A. Jensen, " Network theory analysis ", IEEE Trans. Antennas Propagat., Vol. 52, pp. 98-105, January 2004.

[13] C. Waldschmidt, S. Schulteis, and W. Wiesbeck, "Complete RF system model for analysis of compact MIMO arrays ", IEEE Trans. on Veh. Technol., Vol. 53, pp. 579-586, May 2004.

[14] M. L. Morris and M. A. Jensen, "Network model for MIMO systems with coupled antennas and noisy amplifiers ", IEEE Trans. Antennas Propagat., Vol. 53, pp. 545-552, January 2005.

In addition, the antennas must be made smaller, typically with the antennas rushing to each other, which may also affect antenna efficiency.

See, for example, the following references:

[15] H. A. Wheeler, "Small antennas ", IEEE Trans. Antennas Propagat., Vol. AP-23, n. 4, pp. 462-469, July 1975.

[16] J. S. McLean, "A re-examination of the fundamental limits on the radiation Q of electrically small antennas ", IEEE Trans. Antennas Propagat., Vol. 44, n. 5, pp. 672-676, May 1996.

Ultimately, the lower the frequency and the longer the wavelength, the more difficult the physical size of a single MIMO device can be. The most severe example is in the HF band where the MIMO device antennas must be separated by more than 10 meters from each other.

2. Noise limits. Each MIMO receiver / transmitter subsystem generates a certain level of noise. As more and more such subsystems are placed close together, the noise floor is increased. On the other hand, as more different signals need to be distinguished from each other in many antenna MIMO systems, a further increasing low noise floor is required.

3. Cost and power limitations. There are MIMO applications where cost and power consumption are not issues, but in typical wireless products, both cost and power consumption are critical constraints to successful product development. The individual RF subsystems require each MIMO antenna, including individual analog-to-digital (A / D) and digital-to-analog (D / A) converters. Moore's Law (an empirical observation written by Intel co-founder Gordon Moore, the number of transistors on an integrated circuit with a minimum component is twice as much as every 24 months): Source: http://www.inte. Unlike many aspects of digital systems scaled by com / technology / mooreslaw /, such analog-intensive subsystems typically have a certain physical structure size and power conditions, and a linear scale in power and power. Thus, many antenna MIMO devices are very expensive and power consuming compared to single antenna devices.

As a result, most of the completed MIMO systems today are of the order of 2 to 4 antennas, resulting in a 2 to 4 times increase in throughput, and due to the various advantages of the multi-antenna system, . Up to 10 antenna MIMO systems have been considered (especially at shorter wavelengths and closer to the antenna spacing, resulting in higher microwave frequencies), but very unusual except for very specialized and cost-intensive applications.

The virtual antenna arrays

One particular application of MIMO-type technology is a virtual antenna array. This system is presented in a survey presented in the Treaty of Europe in the field of EURO-COST, Scientific and Technical Research, January 15-17, 2003 in Barcelona, Spain: King's College London, UK: Mischa Dohler and Hamid Aghvami's " A step towards MIMO: Virtual Antenna Arrays ".

Virtual antenna arrays, such as those presented in this paper, are used by systems with cooperative wireless devices (such as cell phones) to communicate with each other on their respective communication channels over their main communication channels (For example, if they are GSM cellular phones in the UHF band, this would be a 5 GHz Industrial Science and Medical (ISM) radio band). This is due to the fact that single antenna devices may be used at a higher throughput rate depending on information between several devices in the range of each other, for example, to operate as if they were physically one device with multiple antennas Thereby potentially achieving an increase such as MIMO.

However, in practice these systems are very difficult to implement and have limited use. First, there are at least two different communication paths per device that must be maintained to achieve improved throughput by the second relay link of unspecified usability. In addition, the devices are more expensive, physically larger, and consume more power because they have greater computational demands, at least in the second communication subsystem. In addition, the system relies on very complex real-time coordinates of potentially all devices over various communication links. Eventually, as the use of concurrent channels (e.g., simultaneous phones request transmission using MIMO technology) increases, the computational burden for each device increases (potentially exponentially as channel utilization linearly increases) This can be very impractical for portable devices with stringent power and size constraints.

A system and method for compensating for frequency and phase offset in a multiple antenna system (MAS) transmission ("MU-MAS") with multiple users (MUs) is described. For example, a method according to an embodiment of the present invention transmits a training signal to each of one or more clients that analyze each training signal to generate frequency offset compensation data at each antenna of the base station Receiving the frequency offset compensation data at the base station; Calculating MU-MAS precoder weights based on the frequency offset compensation data to pre-cancel the frequency offset in the transmitter; Precoding training signals using the MU-MAS precoder weights to generate pre-coded training signals for each antenna of the base station; Analyzing each training signal to generate channel characteristic data at each antenna of the base station and transmitting the pre-encoded training signal to each of the plurality of wireless client devices receiving the channel characteristic data at the base station; Calculating a plurality of MU-MAS precoder weights calculated to pre-erase the channel characteristic data based frequency and phase offset and / or cross-user interface; Precoding data using the MU-MAS precoder weights to generate pre-coded data signals for each antenna of the base station; And transmitting the pre-encoded data signals via respective antennas of the base station for each individual client device.

The present invention compensates for frequency and phase offsets in a multi-antenna system (MAS) transmission ("MU-MAS") with multiple users (MUs), resulting in a more compact and low- Can be provided.

BRIEF DESCRIPTION OF THE DRAWINGS The invention may be better understood from the following detailed description, taken in conjunction with the drawings, in which:
Figure 1 shows a conventional MIMO system.
2 illustrates an N-antenna base station communicating with multiple single-antenna client devices.
Figure 3 shows a three-antenna base station communicating with three single-antenna client devices.
Figure 4 illustrates the training signaling technique used in one embodiment of the present invention.
FIG. 5 illustrates channel characteristic data transmitted from a client apparatus to a base station according to an embodiment of the present invention.
6 illustrates a multi-input distributed-output ("MIDO") downlink transmission in accordance with an embodiment of the present invention.
FIG. 7 illustrates a multiple-input multiple output ("MIMO") uplink transmission in accordance with an embodiment of the present invention.
Figure 8 illustrates base station cycling through different groups of clients to allocate throughput according to one embodiment of the present invention.
Figure 9 illustrates grouping of clients based on proximity in accordance with an embodiment of the present invention.
Figure 10 shows an embodiment of the present invention used within an NVIS system.
11 shows an embodiment of a DIDO transmitter having an I / Q compensation function unit.
12 is a DIDO receiver having an I / Q compensation function unit.
FIG. 13 shows an embodiment of DIDO-OFDM systems by I / Q compensation.
Figure 14 illustrates one embodiment of DIDO 2x2 performance with and without I / Q compensation.
Figure 15 illustrates one embodiment of DIDO 2x2 performance with and without I / Q compensation.
16 illustrates one embodiment of a Symbol Error Rate (SER) with or without I / Q compensation for various QAM constellations.
Figure 17 illustrates one embodiment of DIDO 2x2 performance with or without compensation at various user device locations.
FIG. 18 shows an embodiment of a SER according to whether there is I / Q compensation in case of an ideal (iid) channel.
19 illustrates an embodiment of a transmitter framework of adaptive DIDO systems.
20 shows an embodiment of a receiver framework of adaptive DIDO systems.
FIG. 21 illustrates an embodiment of a method of adaptive DIDO-OFDM.
Figure 22 shows an embodiment of an antenna arrangement for DIDO measurements.
23 illustrates embodiments of array configuration for multi-degree DIDO systems.
24 shows the performance of the multi-degree DIDO systems.
Figure 25 illustrates one embodiment of antenna placement for DIDO measurements.
Figure 26 illustrates one embodiment of DIDO 2x2 performance by 4-QAM and 1/2 FEC ratio as a function of user device location.
Figure 27 illustrates one embodiment of antenna placement for DIDO measurements.
FIG. 28 shows how DIDO 8x8 yields SE greater than DIDO 2x2 with lower TX power conditions.
Figure 29 illustrates one embodiment of DIDO 2x2 performance by antenna selection.
Figure 30 shows the average BER performance of various DIDO pre-coding schemes on the iid channels.
31 shows the SNR gain of ASel as a function of the number of additional transmit antennas in the iid channels.
Figure 32 shows SNR thresholds as a function of the number of users (M) for the BD and ASel with 1 and 2 additional antennas in the iid channels.
Figure 33 shows BER versus user average SNR for two users located in the same respective direction, by various angular spread (AS) values.
Figure 34 shows results similar to Figure 33 but with higher degree of separation between users.
Figure 35 shows the SNR threshold as an AS function for the average angles of arrival angles (AOSs) of various values of users.
Figure 36 shows the SNR threshold for a preferred case of five users.
Figure 37 is a comparison of SNR thresholds for BD and ASel with one and two additional antennas for two users.
38 shows a result similar to that of FIG. 37, but in the case of five users.
Figure 39 shows SNR thresholds for the BD system with various AS values.
Figure 40 shows SNR thresholds on spatially correlated channels with AS = 0.1 [deg.] For BD and ASel with one and two additional antennas.
41 is a comparison of SNR thresholds for two or more channel scenarios by AS = 5 [deg.].
Figure 42 is a comparison of SNR thresholds for two or more channel scenarios with AS = 10;
Figures 43-44 show the SNR threshold as a function of the number of users (M) and angular spread (AS) for BD and ASel schemes with one and two additional antennas.
Figure 45 shows a receiver with a frequency offset estimator / compensator.
46 illustrates a DIDO 2 x 2 system model according to an embodiment of the present invention.
Figure 47 illustrates a method according to an embodiment of the present invention.
Figure 48 shows SER results of DIDO 2 x 2 systems with or without frequency offset.
Figure 49 is a comparison of the performance of various DIDO schemes for SNR thresholds.
Figure 50 compares the amount of overhead required for other embodiments of the methods.
FIG. 51 shows a simulation with a small frequency offset of f _max = 2 Hz and no more integer offset correction.
Figure 52 shows the results when turning off the integer offset estimator.

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the underlying principles of the invention.

FIG. 1 shows a conventional MIMO system with transmit antennas 104 and receive antennas 105. Such a system can achieve up to three times the throughput normally achievable in the available channels. There are a number of different approaches for implementing the details of such a MIMO system as described in the public literature on the assignment, and the following discussion describes one such approach.

The channel is "characterized" before the data is transmitted to the MIMO system of FIG. This is accomplished by first transmitting a "training signal" to each of the receivers 105 at the transmit antenna 104 of each of the transmit antennas 104. [ The training signal is converted to analog by a D / A converter (not shown) and then generated by the encoding and modulation subsystem 102, which is then converted from baseband to RF by each transmitter 103. Each receive antenna 105 coupled to the RF receiver 106 receives each training signal and converts it to a baseband. The baseband signal is digitally converted by a D / A converter (not shown), and the signal processing subsystem 107 characterizes the training signal. The characteristics of each signal may include, for example, many factors or other factors including the internal reference to the receiver, the absolute reference, the relative reference, and the phase and amplitude proportional to the characteristic noise. Typically, the characteristics of each signal are defined as a vector that characterizes the phase and amplitude variations of some of the phases of the signal when the signal is transmitted over the channel. For example, in a quadrature amplitude modulation ("QAM") -modulated signal, the characteristic may be a vector of the phase and amplitude offset of some of the multipath images of the signal. As another example, in an orthogonal frequency division multiplexing ("OFDM") -modulated signal, it may be a vector of phase and amplitude offset of some or all of the individual sub-signals in the OFDE spectrum.

The signal processing subsystem 107 stores the channel characteristics received by each receiving antenna 105 and the corresponding receiver 106. After all of the three transmit antennas 104 have completed their training signal transmission, the signal processing subsystem 107 receives each of the three receive signals < RTI ID = 0.0 > And will store the three channel characteristics for antenna 105. Each individual matrix element H _ij is a channel characteristic (typically a vector as described above) of the training signal transmission of the transmit antenna 104i as received by the receive antenna 105j.

At this point, the signal processing subsystem 107 transforms the matrix H 108 to generate H ^-1 and waits for the transmission of real data from the transmit antennas 104. It should be noted that the various conventional MIMO techniques described in the available literature can be used to ensure that the H matrix 108 can be transformed.

In operation, the data input subsystem 100 is provided with a payload of the data to be transmitted. It is then divided into three parts by a splitter 101 before being provided to the encoding and modulation subsystem 102. For example, if the payload is an ASCII bit for "abcdef ", it may be divided by separator 101 into three sub-payloads of ASCII bits for" ad ", & There will be. Each of the three sub-payloads is then provided to the encoding and modulation subsystem 102 separately.

Each of the sub-payloads is individually encoded using an encoding system suitable for both the signal and the statistical independence of the error correcting capacity. This includes, but is not limited to, Reed-Solomon coding, Viterbi coding, and Turbo Codes. Eventually, each of the three encoded sub-payloads is modulated using an appropriate modulation scheme for the channel. Examples of modulation schemes include differential phase shift keying ("DPSK") modulation, 64-QAM modulation, and OFDM. It should be noted that the diversity gain provided by MIMO permits a higher order modulation constellation than can be realized in a single input-single output (SISO) system using the same channel . Thereafter, each encoded and modulated signal is transmitted through its own antenna 104, which is subjected to D / A conversion by a D / A converter (not shown) and RF generation by each transmitter 103.

Assuming that adequate spatial diversity is present among the transmit and receive antennas, each of the receive antennas 105 will receive different combinations of the three transmitted signals from the antennas 104. [ Each signal is received and converted down to baseband by each RF receiver 106 and digitized by an A / D converter (not shown). If y _n is the signal received by the n th receive antenna 105, x _n is the signal transmitted by the n th transmit antenna 104, and N is noise, this can be described by the following three equations have:

y ₁ = x ₁ H ₁₁ + x ₂ H ₁₂ + x ₃ H ₁₃ + N

_{y 2 = x 1 H 21 +} x 2 H 22 + x 3 H 23 + N

y ₃ = x ₁ H ₃₁ + x ₂ H ₃₂ + x ₃ H ₃₃ + N

Given that this is a system of three equations with three unknowns, it is a linear algebra problem for the signal processing subsystem 107 to obtain x ₁ , x _2, and x ₃ (where N is the decryption of the signals Assuming it is low enough to allow):

x ₁ = y ₁ H ^-1 ₁₁ + y ₂ H ^-1 ₁₂ + y ₃ H ^-1 ₁₃

x ₂ = y ₁ H ^-1 ₂₁ + y ₂ H ^-1 ₂₂ + y ₃ H ^-1 ₂₃

x ₃ = y ₁ H ^-1 ₃₁ + y ₂ H ^-1 ₃₂ + y ₃ H ^-1 ₃₃

Once the three transmitted signals x _n are thus obtained, they are then modulated, decoded (e.g., decoded) by the signal processing subsystem 107 to recover three bit streams originally separated by the separator 101 And error-corrected. These bit streams are combined at the combining device 108 and output from the data output 109 as a single data stream. Assuming that the robustness of the system can overcome the noise obstacle, the data output unit 109 will generate the same bit stream introduced into the data input unit 100.

In general, the described conventional system is implemented with four antennas, and even up to ten, but with a large number of antennas (e.g., 25, 100, or 1000), for reasons described in the Background section of the present disclosure.

Typically, there are two methods in this prior art system, and the return path is implemented exactly on the same way, or vice versa, on each side of the communication channel with both transmit and receive subsystems.

2 is a block diagram of a base station 200 configured with a Wide Area Network (WAN) interface 201 (e.g., via the Internet via a T1 or other high speed connection) . For the time being, we use the term "base station" to refer to a given radio station that communicates wirelessly with a client set from a fixed location. Examples of base stations are wireless local area networks (WLANs) or access points in a WAN antenna tower or antenna array. There are a number of client devices 203-207 with each single antenna, which is provided wirelessly at the base station 200. [ For this example, it is easiest to think that such a base station is located in an office environment that is providing client devices 203-207, but this structure is not suitable for indoor and outdoor (where the base station is providing wireless clients) outdoor, which are all applications. For example, the base station may be based on a cellular telephone tower, or a television broadcast tower. In one embodiment, serial number 10 / 817,731, filed April 20, 2004, assigned to the assignee of the present application and incorporated herein by reference, SYSTEM AND METHOD FOR ENHANCING NEAR VERTICAL INCIDENCE SKYWAVE ("NVIS :) " As described in a co-pending application entitled COMMUNICATION USING SPACE-TIME CODING, the base station 200 is located at ground and is directed upward at an HF frequency (e.g., a frequency up to 24 MHz) to allow signals to be scattered in the ionosphere .

Certain details associated with the base station 200 and the client devices 203-207 described above are for illustration purposes only and are not required to perform the underlying principles of the invention. For example, the base station may be connected to various other types of wide area networks via a WAN interface 201 including an application-specific wide area network such as that used for digital video distribution. Likewise, the client devices may be any of a variety of wireless data processing and / or communication devices including, but not limited to, cellular phones, personal digital assistants ("PDAs"), receivers, have.

In one embodiment, antennas 202 of the base station are spatially separated such that each is a spatially unrelated transmit and receive signal, such that the base station was a conventional MIMO transmit handshake. As described in the background art, experiments have been conducted in which an antenna successfully disposed in a lambda / 6 (such as a 1/6 wavelength) is increased in throughput from MIMO, but generally speaking, The more distant the antennas are placed, the better the performance of the system, preferably at least? / 2. Of course, the underlying principles of the present invention are not limited to any particular separation between antennas.

Note that single base station 200 is much better, and the antennas are located farther apart. For example, in the HF spectrum, the antennas may be spaced at least 10 meters (e.g., in the NVIS embodiment mentioned above). If 100 such antennas are used, the antenna array of the base station may occupy tens of square kilometers.

In addition to the spatial diversity technique, an embodiment of the present invention biases the signal to increase the throughput of the system efficiently. The enhancement of channel capacity through polarization is a known technology that has been used by satellite television suppliers for many years. Using polarization, it is possible to have multiple (e.g., three) base stations or user antennas that are very close together or are still not spatially still relevant. Although the conventional RF systems typically have merits only from two dimensional (e.g., x and y) diversity, the structures described herein may have more advantages from the diversity of three dimensional (x, y and z) .

In addition to space and polarization diversity, an embodiment of the present invention uses antennas with near-orthogonal radiation patterns to improve link performance through pattern diversity. Pattern diversity can improve the capacity and error rate characteristics of MIMO systems, and its advantages over other antenna diversity techniques can be seen in the following documents:

[17] L. Dong, H. Ling, and R. W. Heath Jr., "Multiple-input multiple-out wireless communication systems using antenna pattern diversity ", Proc. IEEE Glob. Telecom. Conf., Vol. 1, pp. 997-1001, November 2002.

[18] R. Vaughan, "Switched parasitic elements for antenna diversity ", IEEE Trans. Antennas Propagat., Vol. 47, pp.399-405, February 1999.

[19] P. Mattheijssen, M. H. A. J. Herben, G. Dolmans, and L. Leyten, "Antenna-pattern diversity versus space diversity for use at handhelds", IEEE Trans. on Veh. Technol., Vol. 53, pp.1035-1042, July 2004.

[20] C. B. Dietrich Jr., K. Dietze, J. R. Nealy, and L. Stutzman, "Spatial, polarization, and pattern diversity for wireless handheld terminals", Proc. IEEE Antennas and Prop. Symp., Vol. 49, pp. 1271-1281, September 2001.

[21] A. Forenza and R. W. Heanth, Jr., "Benefit of Pattern Diversity Via 2-element Array of Circular Patch Antennas in Indoor Clustered MIMO Channels", IEEE Trans. on Communications, vol. 54, no. 5, pp. 943-954, May 2006.

Using pattern diversity, it is possible to have a plurality of base stations or user antennas that are very close to each other or spatially still uncorrelated.

FIG. 3 provides additional details of one embodiment of the base station 200 and client devices 306-308 shown in FIG. For simplicity, the base station 300 is shown with only three antennas 305 and three client devices 306-308. However, the embodiments of the invention described herein may be implemented with a plurality of antennas 305 and client devices 306-308 that are virtually unlimited (e.g., limited by available space and noise) .

FIG. 3 is similar to the conventional MIMO structure shown in FIG. 1, where both have three antennas on either side of the communication channel. In the conventional MIMO system, the three antennas on the right side of FIG. 1 are all at a fixed distance from one another, and the signals received from each of the antennas 105 are processed together in the signal processing subsystem 107, to be. 3, three antennas 309 on the right side of the figure are respectively coupled to different client devices 306-308, each of which can be distributed anywhere within the range of the base station 305 have. As such, the signal received by each client device is processed independently of the other two received signals in its encoding, modulation, and signal processing subsystem 311. Thus, compared to a multiple-input (e.g., antennas 105) and multiple-output (e.g., antennas 104) "MIMO" systems, (E.g., antennas 305), distributed output (e.g., antennas 305) systems.

Note that this application uses a variety of techniques to better comply with academia and industry practices than previous applications. Pending application filed on April 20, 2004, SYSTEM AND METHOD FOR ENHANCING NEAR VERTICAL INCIDENCE SKYWAVE ("NVIS") COMMUNICATION USING SPACE-TIME CODING, Serial No. 20 / 817,731, and 2004 The meaning of "input" and "output" (in the context of SIMO, MISO, DIMO, and MIDO) in the application Serial No. 10 / 902,978, filed on July 30, Reversed from the method used. In the previous applications, "input" refers to radio signals input to the receive antennas (eg, antennas 309 in FIG. 3), and "output" refers to transmit antennas (eg, antennas 305) And the like. In academia and wireless commerce, the inverse meanings of "input" and "output" are commonly used, where "input" refers to wireless signals (eg, wireless signals transmitted from antennas 305) Quot; output "refers to radio signals (e.g., radio signals received by antenna 309) output from the channel. This application adopts the above technique, which is the contrary of the earlier mentioned applications in this paragraph. Accordingly, the following terminology equivalents will be drawn among the applications.

10 / 817,731 and 10 / 902,978 Current application

SIMO = MISO

MISO = MISO

DIMO = MISO

MIDO = MISO

The MIDO architecture shown in FIG. 3 achieves a similar capacity increase, such as MIMO, over the SISO system for a given plurality of transmit antennas. However, one difference between MIMO and the particular MIDO embodiment shown in FIG. 3 is that each MIDO client device 306-308 needs only a single receive antenna to achieve the capacity increase provided by the multiple base station antennas , Whereas for MIMO each client device requires at least as many receive antennas as the multiple capacities that it desires to achieve. Given the practical limitations of how often an antenna can be placed in a client device (as described in the background art), it is typically between 4 and 10 antennas (and between 4 and 10 times multiple capacities) MIMO systems. Since the base station 300 typically provides many client devices from a fixed power location, it is implemented to extend it to more than ten antennas, and to isolate the antennas by a suitable distance to achieve spatial diversity . As shown, each antenna includes a transceiver 304 and a coding, modulation and signal processing section 303 of the processing power source. Importantly, in this embodiment, no matter how many base stations 300 are extended, each client device 306-308 would only need one antenna 309, so that each of the individual user client devices 306-308 ), And the cost of the base station 300 may be shared among the mass base stations among the users.

An example of how a MIDO transmission from the base station 300 to the client devices 306-308 can be achieved is shown in Figs. 4-6.

In one embodiment of the invention, the channel is characterized before initiating a MIDO transmission. As with the MIMO system, training signals are transmitted one by one (in the embodiments described herein) by respective antennas 405. [ Although FIG. 4 shows only the first training signal transmission, if there are three antennas 405, there will be a total of three individual transmissions. Each training signal is generated by the encoding, modulation and signal processing subsystem 403, converted to analog via a D / A converter, and transmitted to the RF via each RF transceiver 404. Various other encoding, modulation, and signal processing techniques may be used including but not limited to those described above (e.g., Reed Solomon, Viterbi encoding; QAM, DPSK, QPSK modulation, ...).

Each client device 406-408 receives a training signal via its antenna 409 and converts the training signal to a baseband by the transceiver 410. [ An A / D converter (not shown) converts the signal to digital, where it is processed by a respective encoding, modulation and signal processing subsystem 411. The signal characterization logic 320 then characterizes the resulting signal (e.g., identifying phase and amplitude distortion as described above) and stores the characteristics in memory. This characterization process is similar to conventional MIMO systems where each client device is the main difference in that it only calculates feature vectors for one antenna rather than n antennas. For example, the encoding, modulation, and signal processing subsystem 420 of the client device 406 may be configured to receive notification of the training signal (either simultaneously with manufacture by receiving it in the transmitted message or through another initialization process) Pattern. When the antenna 405 transmits the training signal with such a known pattern, the coding, modulation and signal processing subsystem 420 uses a correlation scheme to find the strongest reception pattern of the training signal, And after storing the amplitude offset, it subtracts the pattern from the received signal. It then finds the second strong reception pattern correlated to the training signal, stores the phase and amplitude offset, and subtracts the second strong pattern from the received signal. This process continues until a predetermined fixed number of phase and amplitude offsets are stored (e. G., 8) or until the detectable training signal pattern falls below a predetermined noise floor. The vector of the phase / amplitude offset becomes the element (H ₁₁ ) of the vector 413. At the same time, the encoding modulation and signal processing subsystems for client devices 407 and 408 implement concurrent processing to generate their vector elements H ₂₁ and H ₃₁ .

The memory in which the characteristics are stored can be volatile memory such as flash memory or hard drive and / or volatile memory such as random access memory (e.g., SDRAM, RDAM). In addition, other client devices may use different types of memory at the same time to store character information (e.g., PDAs can use flash memory while notebook computers can use hard drives). The underlying principles of the present invention are not limited to any particular type of storage mechanism on the various client devices or the base station.

As mentioned above, depending on the scheme used, each client device 406-408 has only one antenna, so each stores only 1x3 rows 413-415 of the H matrix. 4 shows a step after the first training signal transmission in which the 1x3 row 413-415 of the first column stores channel characteristic information at the first of the three base station antennas 405. [ The remaining two columns are stored according to the channel characteristics of the next two training signal transmissions from the remaining two base station antennas. Note that the three training signals for the city are transmitted at separate times. If the three training signal patterns are selected as not correlated with each other, they can be transmitted simultaneously with reduced training time.

As shown in FIG. 5, after all three pilot transmissions have been completed, each client device 506-508 transmits the 1x3 row 513-515 of the stored H matrix back to the base station 500. For simplicity, only one client device 506 is shown to transmit its property information in FIG. A suitable modulation scheme (e.g., DPSK, 64QAM, OFDM) for a channel combined with sufficient error correction coding (e.g., Reed Solomon, Viterbi, and / or Turbo codes) Lt; RTI ID = 0.0 > 515 < / RTI >

Although all three hannes 505 are shown receiving signals in FIG. 5, a single antenna and transceiver of the base station 500 is sufficient to receive each 1x3 row 513-515 transmission . However, using many or all of the antennas 505 and the transceivers 504 to receive each transmission may result in better signal-to-noise ratios (") " than using a single antenna 505 and transceiver 504 under certain conditions "SNR").

As the encoding, modulation and signal processing subsystem 503 of the base station 500 receives the 1x3 row 513-515 from each client device 507-508 it sends it to the 3x3 H matrix 516 ). Together with the client devices, the base station includes, but is not limited to, storing non-volatile mass storage memories (e.g., hard drives) and / or volatile memories (e.g., SDRAM) Various storage technologies can be used. FIG. 5 shows a step in which the base station 500 receives and stores the 1 × 3 row 513 from the client device 509. The 1x3 rows 514 and 515 may be transmitted and stored until the entire H matrix 516 is stored as they are received from the remaining client devices.

Now, one embodiment of MIDO transmission from base station 600 to client devices 606-608 will be described with reference to FIG. Because each client device 606-608 is an independent device, typically each device is receiving a different data transmission. As such, one embodiment of the base station 600 obtains multiple data streams (formatted as a bit stream) from the WAN interface 601 and the WAN interface 601 and sends each individual bit stream Modulator and signal processing subsystem 603 that routes them to a plurality of channels (u ₁ -u ₃ ), respectively. Various known routing techniques may be used by the router 602 for this purpose.

The three bit streams u ₁ -u ₃ shown in FIG. 6 are then routed to the encoding, modulation and signal processing subsystem 603 and are statistically different from the other error correction streams (e.g., Reed Solomon, , Or turbo codes) and modulated using an appropriate modulation scheme for the channel (such as DPSK, 64QAM, or OFDM). 6 further includes signal pre-coding logic 630 for uniquely encoding the signals transmitted from each of the antennas 605 based on the signal characteristic matrix 616. In addition, More precisely, rather than routing each of the three encoded and modulated bit streams with an individual antenna (as implemented in FIG. 1), in one embodiment, the pre-coding logic 630 sends three new bit streams s (u _'1 -u' _3), it is better to doubling the degree of the six three-bit stream to the inverse of the matrix H _{(616) (u 1 -u 3} ) for generating a. The three pre-coded bitstreams are then converted to analog by a D / A converter (not shown) and transmitted to the RF by transceivers 604 and antennas 605.

Before describing how the bitstreams are received by the client devices 606-608, the operations performed by the pre-encoding module 630 will be described. As with the MIMO example from Figure 1 above, the encoding and modulation signals for each of the three source bitstreams will be designated u _n . 6, each u _i includes data from one of the three bitstreams routed by the router 602, and each such bitstream is transmitted to the three client devices 606 -608).

But also, unlike the MIMO Example 2, in the case where each x _i transmitted by the antennas 104, in the embodiment of the invention shown in Figure 6, each u _i is in each client device antenna 609 (And no noise N exists in the channel). To achieve this result, the output of each of the three antennas 605 (each of which we will designate with vi) is a function of u _i and an H matrix characterizing the channel for each client device. In an embodiment Dp, each v _i is calculated by the pre-coding logic 630 in the coding, modulation and signal processing subsystem 603 by being implemented with the following equations:

v ₁ = u ₁ H ^-1 ₁₁ + u ₂ H ^-1 ₁₂ + u ₃ H ^-1 ₁₃

v ₂ = u ₁ H ^-1 ₂₁ + u ₂ H ^-1 ₂₂ + u ₃ H ^-1 ₂₃

v ₃ = u ₁ H ^-1 ₃₁ + u ₂ H ^-1 ₃₂ + u ₃ H ^-1 ₃₃

Therefore, unlike the MIMO, if the signals are calculated for each x _i is the receiver and then converted by the channel, the embodiments of the invention described herein, each v around the transmitter the signals are changed by the channel Unlock _i . Each antenna 609 receives u _i that is already separated from other u _n-1 bit streams that are intended for other antennas 609. Each transceiver 610 and converts each received signal to baseband, where it is A / E converter (not shown) and each of coding, modulation and signal processing, and digitized by the sub-system 611, the projected x _i bits into it Modulates and decodes the stream, and sends the bit stream to the data interface 612 to be used by the client device (e.g., by the application on the client device).

The embodiments of the invention described herein may be implemented using various other encoding and modulation schemes. For example, in an OFDM embodiment, where the frequency spectrum is divided into multiple sub-bands, the techniques described herein may be used to characterize each individual sub-band. However, as mentioned above, the underlying principles of the present invention are not limited to any particular modulation scheme.

If the client devices 606-608 are portable data processing devices, such as PDAs, notebook computers, and / or wireless phones, the channel characteristics may be such that the client devices may move from one location to another It can change often. As such, in one embodiment of the present invention, the channel characteristic matrix 616 at the base station is continuously updated. In one embodiment, the base station 600 periodically sends a new training signal to each client device (e.g., every 250 ms), and each client device determines whether the channel characteristics The channel characteristic vector is continuously transmitted back to the base station 600 so as to be accurately maintained (e.g., when the base station 600 changes or when the client apparatus moves). In one embodiment, the training signal is interleaved in the actual data signal sent to each client device. Typically, the training signals are much lower throughput than the data signals, so it will have little impact on the overall throughput of the system. Thus, in this embodiment, the channel characteristics matrix 616 can be continuously updated as the base station actively communicates with each client, thereby allowing the client devices to move from one location to the next, To maintain the correct channel characteristics in case the environment changes.

One embodiment of the invention shown in FIG. 7 uses MIMO techniques to improve the upstream communication channel (e.g., the channel from the client devices 706-708 to the base station 700). In this embodiment, the channels from each of the client devices are continuously analyzed and characterized by the up channel characteristic logic 741 in the base station. In more detail, each of the client devices 706-708 analyzes the training signal to generate the N × M channel characteristic matrix 741 (eg, as in a typical MIMO system) Where N is the number of the cluster devices and M is the number of antennas used by the base station. 7 uses three antennas 705 in three client devices 706-708 resulting in a 3x3 channel characteristic matrix 741 stored in the base station and the base station 700 do. The MIMO uplink transmission shown in FIG. 7 may be used to transmit data back to the base station 700 and to transmit channel characteristic vectors back to the base station 700 as shown in FIG. 5, Lt; / RTI > However, unlike the embodiment shown in FIG. 5 where the channel characteristic vectors of each client are transmitted in separate times, the method shown in FIG. 7 allows simultaneous transmission from multiple client devices back to the scrambling base station 700 , Thereby dramatically reducing the impact of the channel characteristic vectors on the feedback channel throughput.

As mentioned above, the characteristics of each signal may include many elements, including, for example, phase and amplitude proportional to the internal reference for the receiver, absolute reference, relative reference, characteristic noise or other factors. For example, in a quadrature amplitude modulation ("QAM") -modulated signal, the characteristic may be a vector of phase and amplitude offsets of several of the multipath images of the signal. In an orthogonal frequency division multiplexing ("OFDM") -modulated signal, as in the other example, it may be a vector of phase and amplitude offsets of some or all of the individual sub-signals in the OFDM spectrum . The training signal is converted to analog by a D / A converter (not shown) and then transmitted to the encoding and modulation subsystem 711 of each client device, which is converted from baseband to RF by the transmitter 709 of each client device. Lt; / RTI > In one embodiment, to ensure that the training signals are synchronized, the client devices transmit training signals only on request by the base station (e.g., in a round robin fashion). In addition, training signals may be transmitted with or interleaved with the actual data signal sent from each client device. Thus, even when the client devices 706-708 are mobile, the training signals can be continuously transmitted and analyzed by the uplink channel characteristic logic 741, thereby allowing the channel characteristics matrix 741 to be updated .

The total channel capacity supported by the above embodiments of the present invention may be defined as a minimum (N, M) where M is the number of client devices and N is the number of base station antennas. That is, the capacity is limited by the number of antennas, either the base station side or the client side. As such, an embodiment of the present invention is only being transmitted / received at a given time with minimal (N, M) antennas.

In a typical scenario, the number of antennas 705 on the base station 700 will be less than the number of client devices 706-708. A preferred scenario is shown in Figure 8, which illustrates five client devices 804-808 that communicate with a base station with three antennas 802. [ In this embodiment, after determining the total number of client devices 804-805 and collecting the required channel characteristics information (e.g., as described above), the base station 800 may communicate with three clients And selects the first group of three clients in this example because the minimum (N, M) = 3). After communicating with the first group of clients 810 for a specified time period, the base station selects another group of three clients 811 for communication. In order to evenly distribute the communication channel, the base station 800 selects two client devices 807 and 808 that are not included in the first group. In addition, since a redundant antenna is available, the base station 800 selects additional client devices 806 included in the first group. In one embodiment, the base station 800 cycles in a manner that allows each client to effectively allocate the same amount of excess rate of overtime between client groups. For example, to evenly allocate throughput, the base station may communicate with three clients (not including client device 806) (e.g., since client device 806 belongs to communication with the base station for the first two cycles) And may select any combination of devices.

In one embodiment, in addition to standard data communications, the base station transmits training signals to each of the client devices and uses the techniques described above to receive training signals and signal characteristic data from each of the client devices. .

In one embodiment, groups of predetermined client devices or client devices may be assigned different levels of throughput. For example, client devices may be preferentially processed such that relatively higher priority client devices may be guaranteed to have more communication cycles (e.g., higher throughput) than lower priority client devices . The "priority" of the client device may be determined, for example, by determining the level of user subscription to the wireless service (e.g., where users are willing to pay more for additional throughput) and / (E.g., real-time communications such as audio and video on a telephone may be handed over in a non-real time priority such as e-mail).

In one embodiment of the base station, the throughput based on the current load required by each client device is assigned dramatically. For example, if the client device 804 is streaming live video and the other devices 805-806 are performing non-real-time functions such as e-mail, the base station 800 may send You can allocate a relatively higher throughput. It should be noted, however, that the underlying principles of the invention are not limited to any particular throughput allocation technique.

As shown in FIG. 9, the two client devices 907 and 908 may be very close, so that the channel characteristics for the clients may be effectively the same. As a result, the base station will effectively receive and store the corresponding channel characteristic vectors for the three client devices 907, 908, and thus will not be able to generate unique spatially distributed signals for each client device. Thus, in one embodiment, the base station will ensure that any two or more client devices that are very close to each other are assigned to different groups. In Figure 9, for example, the base station 900 may communicate with a first group 910 of client devices 904, 905, and 908 by first allowing the client devices 907 and 908 to be in different groups, and; And then communicates with a second group of client devices (905, 906, 907).

Alternatively, in one embodiment, the base station 900 communicates with both the client devices 907 and 908 one after another, multiplexing the communication channels using known channel multiplexing techniques. For example, the base station may employ time division multiplexing ("TDM"), frequency division multiplexing ("TDM") to divide a single, spatially correlated signal between client devices 907 and 908, FDM ") or code division multiple access (" CDMA ") techniques.

Although each client device described above has a single antenna, the underlying principles of the present invention can be used with client devices having multiple antennas to increase throughput. For example, when used in the wireless systems described above, a two-fold increase in throughput is realized for a client with two antennas (e.g., assuming a spatial and angular spacing between the antennas) , And a client with three antennas would be a three-fold increase in throughput. The base station may apply the same general rules when cycling through client devices with multiple antennas. For example, it can treat each antenna as an individual client and assign a throughput to that "client " as if it were some other client (e.g., each client would be provided with an appropriate or corresponding communication cycle).

As mentioned above, one embodiment of the present invention provides a MIDO and / or MIMO signal transmission technique as described above to increase the signal-to-noise ratio and throughput within a Near Vertical Incidence Skywave ("NVIS ≪ / RTI > Referring to FIG. 10, in an embodiment of the present invention, a first NVIS base station 1001 with a matrix of N antennas 1002 is configured to communicate with M client devices 1004. The NVIS antennas 1002 and the antennas of the various client devices 1004 transmit signals up to about 15 degrees vertically to minimize terrestrial interference effects. In one embodiment, the antennas 1002 and the client devices 1004 may use multiple MIDO and MIMO techniques described above at a specified frequency within the NVIS spectrum to provide multiple independent data streams 1006 (e.g., Carrier frequency at or below 23 MHz, or typically below 10 MHz), thereby significantly increasing the throughput at the designated frequency (e.g., with an element proportional to the number of statistically independent data streams).

The NVIS antennas that provide a given base station may be physically spaced apart from one another. Given long wavelengths of less than 10 MHz and long distances (such as 30 miles round trip) where the signals are shifted, the physical separation of the antennas, which are separated by 100 yards and even miles, can provide advantages in diversity. In these situations, the individual antenna signals may be brought back to a centralized location to be processed using conventional wired or wireless communication systems. Alternatively, each antenna may have a local facility using conventional wired or wireless communication systems to process the signals and then communicate the data back to a centralized location. In one embodiment of the present invention, the NVIS base station 1001 has a broadband link 1015 to the Internet 1010 (or other broadband network), whereby the client devices 1003 with remote, high speed, .

In one embodiment, the base station and / or users may utilize the polarization / pattern diversity techniques described above to reduce array size and / or distance of users while providing diversity and increased throughput. For example, in a MIDO system with HF transmission, users may be in the same position and their signals are not correlated due to polarization / pattern diversity. In particular, by using pattern diversity, one user communicates with the base station via terrestrial waves while the other user communicates via NVIS.

Additional embodiments of the present invention

I. DIDO-OFDM pre-coding by I / Q imbalance

One embodiment of the present invention employs a system and method for compensating phase and modulation (I / Q) imbalance in distributed-input distributed-output (DIDO) systems by orthogonal frequency division multiplexing (OFDM). Briefly, according to this embodiment, the user devices estimate channel and feedback information for the base station; The base station calculates a pre-coding matrix for canceling carrier-to-user interference generated by I / Q imbalance; And the parallel data streams are transmitted to the multiple user devices via DIDO encoding; User devices demodulate data through zero-forcing (ZF), minimum mean-square error (MMSE), or maximum likelihood (ML) receivers to suppress residual interference.

As described in detail below, the present invention is not limited to some of the important features of this embodiment, including:

Precoding to cancel inter-carrier interference (ICI) from mirror tones (due to I / Q mismatch) in OFDM systems;

Pre-coding to cancel inter-user interference (due to I / Q mismatch) and ICI in DIDO-OFDM systems;

Techniques for clearing ICI (due to I / Q mismatch) through the ZF receiver in DIDO-OFDM systems;

Techniques for canceling inter-user interference and ICI (due to I / Q mismatch) through pre-coding (at the transmitter) and ZF or MMSE filters (at the receiver) in DIDO-OFDM systems;

Techniques for canceling inter-user interference and ICI (due to I / Q mismatch) via non-linear detectors such as pre-coding (at the transmitter) and maximum likelihood (ML) detector at the DIDO- ;

The use of pre-coding based on channel state information to cancel inter-carrier interference (ICI) from a mirror tone (due to I / Q mismatch) in OFDM systems;

The use of pre-coding based on channel state information to cancel inter-carrier interference (ICI) from mirror tones (due to I / Q mismatch) in DIDO-OFDM systems;

The use of an I / Q-aware DIDO receiver at the base station and a DIDO precoder at the base station;

Using I / Q mismatch recognition DIDO precoder at base station, I / Q recognition DIDO receiver at user terminal, and I / Q aware channel estimator;

A DIDO precoder for recognizing I / Q mismatch at the base station, an I / Q recognition DIDO receiver at the user terminal, an I / Q recognition channel estimator, and an I / Q recognition DIDO feedback generator for transmitting channel state information from the user terminal to the base station use;

I / Q aware DIDO using I / Q channel information to perform functions including DIDO precoder and user selection, adaptive coding and modulation, space-time frequency mapping, or precoder selection at base station The use of configurators;

The use of I / Q aware DIDO receivers to clear ICI (due to I / Q mismatch) through ZF receivers in DIDO-OFDM systems using block diagonalization (BD) precoders;

The use of an I / Q aware DIDO receiver that cancels ICI (due to I / Q mismatch) through a non-linear detector such as a pre-coding (at the transmitter) and a maximum likelihood detector (at the receiver) in DIDO-OFDM systems; And

The use of I / Q-aware DIDO receivers to minimize ICI (due to I / Q mismatch) through ZF or MMSE filters in DIDO-OFDM systems.

a Background Technology

The transmitted and received signals of typical wireless communication systems are composed of in-phase and quadrature (I / Q) components. In real systems, the inphase and quadrature components may be distorted due to defects in mixing and baseband operation. These distortions appear as I / Q phase, gain and delay mismatch. Phase imbalance is caused by sine and cosine in a modulator / demodulator that is not perfectly orthogonal. The gain imbalance is caused by different amplifications between inphase and quadrature components. There may be additional distortion in the analog circuit, called delay imbalance, due to the delay difference between the I- and Q-rails.

In an orthogonal frequency division multiplexing (OFDM) system, I / Q imbalance causes inter-carrier interference (ICI) from the mirror tones. These effects have been studied in the literature and methods for compensating I / Q mismatch in single-input single-output SISO-OFDM systems are described in MD Benedetto and P. Mandarini, "Analysis personal communications, pp. 175-186, 2000, S. Schuchert and R. Hasholzner, "A novel I / Q imbalance compensation scheme for the reception OFDM signals ", IEEE Transaction on Consumer Electronic, August 2001; M. Valkama, M. Renfors, and V. Q. Imbalance compensation in communication receivers, Kohunen ' s " Advanced methods for I / Q imbalance compensation ", Proc., October 2001; R. Rao and B. Daneshrad, "Analysis of I / Q mismatch and a cancellation scheme for OFDM ITS Mobile Communication Summit, June 2004; A. Tarighat, R. Bagheri, and AH Sayed, "Compensation schemes and performance analysis of IQ imbalances in OFDM receivers", Signal Processing, IEEE Transactions on [Acoustics, Speech , And Signal Processing, IEEE Transactions on, vol. 53, pp. 3257-3268, It was presented in August 2005.

An extension of this work for multi-input multiple-output MIMO-OFDM systems is described in R. Rao and B. Daneshrad, "I / Q mismatch canceling for MIMO OFDM systems ", in Persoanl, Indoor and Mobile Radio Communications, 2004; PIMRC 2004. 15th IEEE International Symposium on, vol. 4, 2004, pp. 2710-2714, R. M. Rao, W. Zhu, S. Lang, C. Oberli, D. Browne, J. Bhatia, J.F. Frigon, J. Wang, P; Gupta, H. Lee, D.N. Liu, S. G. Wong, M. Fitz, B. Daneshrad and O. Takeshita, "Multinatenna testbeds for research and edu- cation in wireless communications ", IEEE Communications Magazine, vol. 42, no. 12, 99. 72-81, December 2004; S. Lang, M. R. Rao, and B. Daneshrad, " IEEE Communications Magazine, vol. 42, no. 6, pp. 6-12, June 2004, MIMO OFDM receivers for systems with IQ imbalances by A. Tarighat and A. H. Sayed, IEEE Trans. Sig. Proc., Vol. 53, pp. 3583-3596, September 2005, for orthogonal space-time block codes (OSTBC).

Unfortunately, there is no literature on how to correct I / Q gain and phase imbalance errors in current distributed-input distributed-output (DIDO) communication systems. The embodiments of the invention described below provide a solution to these problems.

DIDO systems are capable of transmitting parallel data streams (via pre-coding) to multiple users to improve downlink throughput while using the same radio resources (such as the same slot period and frequency band) as in conventional SISO systems And one base station with distributed antennas to transmit. A detailed description of DIDO systems is given in S.G. No. 10 / 902,978, entitled " Conventional Application ", issued to Perlman and T. Cotter, " System and Method for Distributed Input-Distributed Output Wireless Communications ", assigned to the assignee of the present application, .

There are many ways to implement DIDO precoders. As one approach, Q. H. Spencer, A. L. Swindlehurst, and M. Haardt, "Zero forcing methods for downlink spatial multiplexing in multiuser MIMO channels", IEEE Trans. Sig. Proc., Vol. 52, pp. Pp. 461-174, February 2004, K. K. Wong, R. D. Murch, and K. B. Letaief, "A joint channel diagonalization for multiuser MIMO antenna systems", IEEE Trans. Wireless Comm., Vol. 2, pp. 773-786, July 2003; L. U. Choi and R. D. Murch, " A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach ", IEEE Trans. Wireless Comm., Vol. 3, pp. 20-24, January 2004; Z. Shen, J.G. Andrews, R. W. Heath, and IEEE Trans. Sig. B. L. Evans, "Low complexity user selection algorithms for multi-user MIMO systems with block diagonalization", published for Proc. October 2005 IEEE Trans. Z. Shen, R. Chen, J. G. Andrews, R. W. Heath, and B. L. Evans submitted to Wireless Comm., "Sum capacity of multiuser MIMO bradcast channels with block diagonalization"; IEEE Trans. there is a block diagonalization (BD) described in R. Chen, R. W. Heath, and J. G. Andrews in On Signal Processing, "Transmit selection diversity for unitary precoded multiuser spatial multiplexing systems with linear receivers".

In DIDO-OFDM systems, I / Q mismatch causes two results, ICI and inter-user interference. The former is due to interference from the mirror tones as in SISO-OFDM systems. The latter is due to the fact that I / Q mismatch destroys the orthogonality of the DIDO precoder causing interference across users. Both types of interference can be eliminated at the transmitter and at the receiver via the methods described herein. Three methods for I / Q compensation in DIDO-OFDM systems are described and their performance is compared against systems with I / Q mismatch. Results based on simulation and actual measurements performed by the DIDO-OFDM prototype are provided.

These embodiments are an extension of the conventional application. In particular, these embodiments relate to the following features of the prior application.

Systems such as those described in the prior application where I / Q lies are affected by gain and phase imbalance.

The training signals used for channel estimation are used to compute a DIDO precoder with I / Q compensation at the transmitter; And

The signal characteristic data is used in the transmitter to calculate the distortion due to I / Q imbalance and calculate the DIDO precoder according to the method proposed in this document.

b. Embodiments of the Invention

First, the mathematical model and framework of the present invention will be described.

Before presenting a solution, it is useful to explain the concept of core mathematical. We describe it as assuming I / Q gain and phase imbalance (phase delay is not included in the description, but is handled automatically in the DIDO-OFDM version of the algorithm). To illustrate the basic concept, we propose that we multiply two complex numbers s = s _I + js _Q and h = h _I + jh _Q , and let x = h * s. We use subscripts to represent the in-phase and quadrature components. x _I = s _I h _I - s _Q h _Q and x _Q = s _I h _Q - s _Q h _I.

This can be rewritten in matrix form as:

Note the unitary transformation by the channel matrix (H). Now s is the transmission symbol and h is the channel. The presence of I / Q gain and phase imbalance can be modeled by generating a non-unitary transformation as follows.

The secret is to recognize that it is possible to write:

Now, if you write (A) again,

및

And

로 정의하자.

.

Both of these matrices have a unit structure, and thus can be equivalently represented by a composite scalar as follows.

h _e = h ₁₁ + h ₂₂ + j (h ₂₁ -h ₁₂ ) and

h _c = h ₁₁ - h ₂₂ + j (h ₂₁ + h ₁₂ )

Using all of these observations, we can put back into a real equation of scalar form with two channels, the equivalent channel (h _e ) and the conjugate channel (H _c ). Then, the actual deformation equation in (5) becomes x = h _e s + h _c s ^* .

We refer to the first channel as the equivalent channel and the second channel as the conjugate channel. The equivalent channel is the channel to be observed if there is no I / Q gain and phase imbalance.

The input-output relationship of a distributed-time MIMO N × M system using a similar argument and having I / Q gain and phase imbalance (using a scalar equivalent equation to establish its matrix correspondence) can be expressed as .

Here, t is a time index and ^{_{dispersion, h e, h c ∈C M}} × N, s = [s 1, ..., s N], x = [x 1, ..., x M], L Is the number of channel taps.

In DIDO-OFDM systems, the received signal in the frequency domain is represented. FFT _K {s [t]} = S [k] and then FFT _K {s ^* [t]} = S ^* [(-k)] = S ^* [Kk], where k = 0,1. ..., K-1, call back from the signals and systems. The equivalent input-output relationship for the MIMO-OFDM system for subcarrier k by OFDM is:

Here, k = 0, 1, ..., K-1 is an OFDM subcarrier index, and H _e and H _c are equivalent and conjugated channel matrixes, respectively.

및

And

The second contribution in equation (1) is interference from the mirror tone. This can be addressed by constructing the following stacked matrix system (note conjugates).

및

은 각각 주파수 영역에서 전송 및 수신 기호의 벡터이다.here,

And

Are vectors of transmit and receive symbols in the frequency domain, respectively.

Using this method, the actual matrix is established for use in the DIDO operation. For example, by a DIDO 2x2 input-output relationship (assuming each user has a single receive antenna), the first user equipment (in the absence of noise) looks like this

On the other hand, the second user observes as follows

,

는 매트릭스 H_e 및 H_c의 m번째 열을 각각 나타내고, W ∈ C^4×4는 DIDO 사전부호화 매트릭스 이다. 식 (2) 및 (3)으로부터, 사용자 m의 수신 기호

은 I/Q 불균형에 의해 발생된 두 개 소스의 간섭, 미러 톤(이를 테면,

)으로부터의 반송파 간 간섭 및 사용자 간 간섭(이를 테면,

및 p≠m인

)에 의해 영향을 받는다. 식 (3)의 DIDO 사전부호화 매트릭스 W는 이러한 두 개의 간섭 용어들을 소거하도록 설계된다.here,

,

Denotes the mth column of the matrices H _e and H _c , respectively, and W ∈ C ^{4 × 4} is the DIDO pre-coding matrix. From equations (2) and (3)

Interference from two sources caused by I / Q imbalance, mirror tones (eg,

) And inter-user interference (e.g.,

And p? M

). ≪ / RTI > The DIDO pre-coding matrix W in equation (3) is designed to cancel these two interference terms.

보다는) 복합 채널 [

,

]로부터 계산된, Q. H. Spencer, A. L. Swindlehurst, 및 M. Haardt의 "Zeroforcing methods for downlink spatial multiplexing in multiuser MIMO channels", IEEE Trans. Sig. Proc., vol. 52, pp. 461-471, 2004년 2월. K. K. Wong, R. D. Murch, 및 K. B. Letaief의 "A joint channel diagonalization for multiuser MIMO antenna systems"의 IEEE Trans. Wireless Comm., vol. 2, pp. 773-786, 2003년 7월. L. U. Choi 및 R. D. Murch의 "A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach", IEEE Trans. Wireless Comm., vol. 3, pp. 20-24, 2004년 1월. 2005년 9월, IEEE TR문 Sig. Proc.에 공개가 허용된 Z. Shen, J. G. Andrews, R. W. heath, 및 B. L. Evans의 "Low complexity user selection algorithms for multiuser MIMO systems with block diagonalization", 2005년 10월 IEEE Trans. Wireless Comm.에 제출된 S. Shen, R. Chen, J. G. Andrews, R. W. Heath, 및 B. L. Evans의 "Sum capacity of multiuser MIMO broadcast channels with block diagonalization"을 참조한다). 그래서, 현 DIDO 시스템은 다음과 같도록 프리코더를 선택한다.There are several other embodiments of a DIDO precoder that can be used here according to the joint detection applied to the receiver. In one embodiment, block diagonalization (BD) is used (e.g., (

Rather than a composite channel [

,

QH Spencer, AL Swindlehurst, and M. Haardt, "Zeroforcing methods for downlink spatial multiplexing in multiuser MIMO channels ", IEEE Trans. Sig. Proc., Vol. 52, pp. 461-471, February 2004. K. Wong, RD Murch, and KB Letaief, "A joint channel diagonalization for multiuser MIMO antenna systems" Wireless Comm., Vol. 2, pp. 773-786, July 2003. LU Choi and RD Murch, " A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach ", IEEE Trans. Wireless Comm., Vol. 3, pp. 20-24, January 2004. In September 2005, IEEE TR Statement Sig. &Quot; Low Complexity User Selection Algorithms for Multi-User MIMO Systems with Block Diagonalization ", by Z. Shen, JG Andrews, RW Heath, and BL Evans, Proc. See S. Shen, R. Chen, JG Andrews, RW Heath, and BL Evans submitted to Wireless Comm., "Sum capacity of multiuser MIMO broadcast channels with block diagonalization"). Thus, the current DIDO system selects a precoder as follows.

이다. 이 방법은 상기 프리코더를 사용하기 때문에 장점이 있으며, I/Q 이득 및 위상 불균형의 효과들이 전송기에서 완전히 소거되므로 전과 동일한 DIDO 프리코더의 다른 양태들을 유지하는 것이 가능하다.Here,? _{I, j} is a constant,

to be. This method is advantageous because it uses the precoder and it is possible to keep other aspects of the same DIDO precoder as before, since the effects of I / Q gain and phase imbalance are completely canceled in the transmitter.

It is also possible to design DIDO precoders that pre-cancel inter-user interference without pre-eliminating ICI due to IQ imbalance. With this method, the receiver (instead of the transmitter) compensates for the IQ imbalance by using one of the receive filters described below. Then, the pre-coding design criterion of equation (4) can be changed as follows.

And

및

는 사용자 m에 대한 수신 기호 벡터이다.Here, for the mth transmission symbol

And

Is the received symbol vector for user m.

을 추정하기 위해, 사용자 m은 ZF 필터를 사용하며, 상기 추정된 기호 벡터는 다음과 같이 주어진다.On the receiving side,

, The user m uses a ZF filter, and the estimated symbol vector is given as follows.

While the ZF filter is the easiest to comprehend, the receiver can apply a number of different filters to those of ordinary skill in the art. A popular choice is the following MMSE filter:

Where rho is the signal-to-noise ratio. Alternatively, the receiver may perform maximum likelihood symbol detection (or a sphere decoder or iterative change). For example, the first user may use the ML receiver and solve the following optimal equation.

Where S is a set of all possible vectors s and depends on the constellation size. The ML receiver eliminates the need for additional complexity at the receiver and provides better performance. Apply a similar set of equations to the second user.

및

은 전부 제로를 갖는 것으로 가정된다. 이러한 가정은 전송 프리코더가 식(4)의 기준에서처럼 사용자 간 간섭을 완전히 소거할 수 있을 경우에만 유지된다. 마찬가지로,

및

은 전송 프리코더가 반송파 간 간섭을 (이를 테면, 미러 톤으로부터) 완전히 소거할 수 있을 경우에만 대각 매트릭스들이다.In equations (6) and (7)

And

Is assumed to have all zeros. This assumption is maintained only if the transport precoder can completely erase the inter-user interference as in the criterion of equation (4). Likewise,

And

Are diagonal matrices only if the transmit precoder can completely cancel inter-carrier interference (e. G., From a mirror tone).

및

을 추정하고, 이러한 추정값들을 AP 내의 프리코더(1302)로 피드백한다. 상기 BS는 사용자 간 간섭뿐만 아니라 I/Q 이득 및 위상 불균형으로 인한 간섭을 사전 소거하기 위해 DIDO 프리코더 가중치(매트릭스 W)를 계산하고, 상기 무선 채널(1304)을 통해 사용자들에게 상기 데이터를 전송한다. 사용자 장치 m은 잔존 간섭을 소거하고 상기 데이터를 복호화하기 위해 유닛(1304)에 의해 제공된 채널 추정값들을 이용하여 ZF, MMSE 또는 ML 수신기(1308)를 사용한다.13 is a block diagram illustrating an I / Q compensation including an IQ-DIDO precoder 1302, a transmission channel 1304, channel estimation logic 1306 in a user equipment, and a ZF, MMSE or ML receiver 1308 in a base station (BS) Lt; RTI ID = 0.0 > DIDO-OFDM < / RTI > The channel estimation logic 1306 may generate training symbols

And

And feeds back these estimates to the precoder 1302 in the AP. The BS calculates a DIDO precoder weight (matrix W) to pre-cancel interferences due to I / Q gain and phase imbalance as well as inter-user interference, and transmits the data to users via the wireless channel 1304 do. The user equipment m uses the ZF, MMSE or ML receiver 1308 using the channel estimates provided by the unit 1304 to cancel the residual interference and decode the data.

The following three embodiments may be used to implement the I / Q compensation algorithm.

및

은 대각 매트릭스들이 될 것으로 가정된다. 따라서, 식(8)은 다음과 같이 단순화된다.Method 1 - TX Compensation: In this embodiment, the transmitter computes a pre-coding matrix according to equation (4). At the receiver, user devices use a "simplified" ZF receiver, where

And

Are assumed to be diagonal matrices. Therefore, equation (8) is simplified as follows.

Method 2 - RX Compensation: In this embodiment, the transmitter does not cancel intercarrier and user-to-user interference according to the criteria of Equation (4) a pre-coding matrix based on the conventional BD method described in R. Chen, R. W. Heath, and J. G. Andrews, accepted for on signal processing, described in "Transmit selection diversity for unitary precoded multiuser spatial multiplexing systems with linear receivers" is calculated. By this method, the pre-coding matrix in equations (2) and (3) is simplified as follows.

및

둘 다의 피드백을 필요로하는 방법 1과 반대로, 상기 DIDO 프리코더를 계산하기 위해 상기 전송기에 대한 벡터

만을 피드백하는 것을 필요로 한다. 따라서, 방법 2는 저속 피드백 채널들을 갖는 DIDO 시스템들에 특히 적합하다. 한편, 방법 2는 식(11)보다는 식(8)에서 상기 ZF 수신기를 계산하기 위해 상기 사용자 장치에서 약간 더 높은 계산적 복잡도를 필요로 한다.At the receiver, the user devices use a ZF filter as in equation (8). Note that this method does not pre-clear the interference at the transmitter as in Method 1 above. Therefore, it cancels inter-carrier interference in the receiver but can not cancel inter-user interference. In addition, in method 2,

And

In contrast to method 1, which requires feedback of both, to calculate the DIDO precoder, the vector for the transmitter

Lt; / RTI > Thus, Method 2 is particularly suitable for DIDO systems with slow feedback channels. On the other hand, method 2 requires slightly higher computational complexity in the user equipment to calculate the ZF receiver in equation (8) than in equation (11).

Method 3 - TX-RX Compensation: In one embodiment, the two methods described above are combined. The transmitter computes a pre-encoding matrix as in equation (4), and the receivers estimate transmission symbols according to equation (8).

Whether the phase imbalance, the gain imbalance or the delay imbalance, the I / Q imbalance produces a detrimental degradation in signal quality in wireless communication systems. For this reason, past circuit hardware is designed to have a very low imbalance. However, as described above, it is possible to correct this problem by using digital signal processing in the form of pre-transmission acquisition and / or specific receivers. One embodiment of the present invention includes a system having several new functional units, each of which is important for the implementation of I / Q correction in an OFDM communication system or a DIDO-OFDM communication system.

One embodiment of the present invention uses pre-coding based on channel state information to cancel inter-carrier interference (ICI) from mirror tones (due to I / Q mismatch) in an OFDM system. 11, the DIDO transmitter according to this embodiment includes a user selector unit 1102, a plurality of coding module units 1104, a corresponding plurality of mapping units 1106, a DIDO IQ-aware pre-coding unit 1108, a plurality of RF transmitter units 1114, a user feedback unit 1112, and a DIDO configuration unit 1110.

The user selector unit 1102 selects data associated with a plurality of users U ₁ -U _M based on the feedback information obtained by the feedback unit 1112, and each of the plurality of encoding module units 1104 ) To provide this information. Each coding module unit 1104 encodes and modulates information bits of each user and sends them to the mapping unit 1106. The mapping unit 1106 maps the input bits to complex symbols and sends the results to the DIDO IQ-aware pre-coding unit 1108. The DIDO IQ-aware pre-coding unit 1108 is configured to calculate the DIDO IQ-aware pre-coding weights from the users that pre-encode the input symbols obtained by the feedback unit 1112 from the users And uses the channel state information obtained by the feedback unit 1112. [ Each coded data stream is sent by the DIDO IQ-aware pre-coding unit 1108 to the OFDM unit 1115 which calculates the IFFT and adds a cyclic prefix. This information is sent to the D / A unit 1116, which operates as a digital to analog conversion and sends it to the RF unit 1114. The RF unit 1114 up-converts the baseband signal to a mid / radio frequency and sends it to the transmit antenna.

The precoder works together for normal and mirror tones to compensate for I / Q imbalance. Many precoder design criteria can be used including ZF, MMSE, or weighted MMSE design. In a preferred embodiment, the precoder completely removes the ICI due to I / Q mismatch, thereby preventing the receiver from performing any additional compensation.

In one embodiment, the precoder uses a block diagonalization criterion that completely eliminates inter-user interference while not completely eliminating I / Q effects for each user requiring additional receiver processing. In another embodiment, the precoder uses a zero-forcing criterion to completely erase both inter-user interference and ICI due to I / Q imbalance. This embodiment may use a conventional DIDO-OFDM processor in the receiver.

One embodiment of the present invention uses pre-coding based on channel state information to cancel inter-carrier interference (ICI) from mirror tones (due to I / Q mismatch) in a DIDO-OFDM system, DIDO receiver is used. 12, in an embodiment of the present invention, the system including the receiver 1202 includes a plurality of RF units 1208, a corresponding plurality of A / D units 1210, an IQ- An estimator unit 1204 and a DIDO feedback generator unit 1206.

The RF units 1208 receive signals transmitted from the DIDO transmitter units 1114, downconvert the signals to baseband, and transmit the down-converted signals to the A / D units 1210 to provide. The A / D units 1210 then converts the signal from analog to digital and sends it to the OFDM units 1213. The OFDM units 1213 operate the FFT to enhance the cyclic prefix and report the signal in the frequency domain. During the training period, the OFDM units 1213 send an output to an IQ-aware channel estimation unit 1204 that computes channel estimates in the frequency domain. Alternatively, the channel estimates may be computed in the time domain. During the data period, the OFDM units 1213 send the output to an IQ-aware receiver unit 1202. The IQ-aware receiver unit 1202 computes an IQ receiver and demodulates / decodes the signal to obtain the data 1214. The IQ-aware channel estimation unit 1204 sends the channel estimate values to the DIDO feedback generator unit 1206, which can quantize the channel estimate values and send it back to the transmitter via a feedback control channel 1112.

The receiver 1202 shown in FIG. 12 may operate under a number of criteria known to those of ordinary skill in the art, including ZF, MMSE, maximum likelihood, or MAP receivers. In a preferred embodiment, the receiver uses an MMSE filter to cancel the ICI generated by the IQ imbalance for the mirror tones. In another preferred embodiment, the receiver uses a non-linear detector such as a maximum likelihood detector to jointly detect symbols for the mirror tones. This method has improved performance by eliminating more complexity.

In one embodiment, IQ-aware channel estimator 1204 is used to determine receiver coefficients for removing ICI. Thus, we use pre-coding based on channel state information to cancel inter-carrier interference (ICI) from the mirror tones (due to I / Q mismatch), the IQ- DI DIODO receiver, and the IQ- DIDO-OFDM system. The channel estimator can use conventional training signals or use specially configured training signals sent on inphase and quadrature signals. Many estimation algorithms can be implemented including least squares, MMSE, or maximum likelihood. The IQ-aware channel estimator provides an input to the IQ-aware receiver.

The channel state information may be provided to the base station via channel reciprocity or via a feedback channel. One embodiment of the present invention includes a DIDO-OFDM system with an I / Q-aware precoder and an I / Q-aware feedback channel for conveying channel state information from the user to the base station. The feedback channel may be a physical or logical control channel. The feedback information also claims that the user terminal can be generated using a DIDO feedback generator. The DIDO feedback generator takes the output of the I / Q aware channel estimator as an input. Which can quantize the channel coefficients or use a number of limited feedback algorithms known in the art.

The allocation, modulation and coding rate of users, and mapping to space-time frequency code slots may vary according to the results of the DIDO feedback generator. Thus, one embodiment configures the DIDO IQ-aware precoder and selects one or more users to select a mapping for modulation rate, coding rate, a subset of allowed users for transmission, and their space-time frequency code slots Lt; RTI ID = 0.0 > IQ-aware < / RTI >

To evaluate the performance of the proposed compensation methods, three DIDO 2 × 2 systems will be compared as follows:

1. In case of I / Q mismatch: Transmits over all tones (except DC and edge tones) without compensating I / Q mismatch.

2. If there is I / Q compensation: Transfers over all tones and compensates for I / Q mismatch by using "Method 1" described above.

3. Ideal Case: Transmits data across odd tones to prevent interference between users and carriers (eg from mirror tones) caused by I / Q mismatch.

Hereinafter, the results obtained from measurements by the DIDO-OFDM prototype in actual propagation scenarios are presented. Figure 14 shows 64-QAM constellations obtained from the three systems described above. These constellations are obtained by the positions of the same users and a fixed average signal to noise ratio (~ 45 dB). The first constellation 1401 is very noisy due to interference from the mirror tones generated by the I / Q imbalance. The second constellation 1402 indicates a slight improvement due to I / Q compensation. Note that the second constellation 1402 is not as clean as the ideal case, as shown in the constellation 1403, due to the phase noise possibility that causes inter-carrier interference (ICI).

15 shows the average SER (Symbol Error Rate) 1501 and the per-user effective throughput 1502 performance of DIDO 2 × 2 systems with 64-QAM and a coding rate of 3/4 and with or without I / Q mismatch. The OFDM bandwidth is 250 KHz with 64 tones and the cyclic prefix length L _cp = 4. In an ideal case we transmit data over only a subset of tones, so the SER and effective throughput performance are evaluated as a function of the average per-tone transmit power (rather than total power transmission) to ensure a fair comparison to other cases. In addition, in the following results, we use the transmit power (expressed in decibels) of the normalized values because our goal is to compare the relative performance (rather than absolute) of the various systems. FIG. 15 shows that in the presence of an I / Q imbalance, the SER does not reach the target SER (~ 10 ^-2 ) and consistently obtains the "MIMO OFDM receivers for systems with IQ imbalances" by A. Tarighat and AH Sayed, IEEE Trans. Sig. Proc., Vol. 53, pp. 3583-3596, and September 2005, respectively. This saturation effect is due to the fact that both the signal (from the mirror tones) and the interference power increase as the TX power increases. However, through the I / Q compensation method described above, it is possible to cancel the interference and obtain a better SER performance. Note that the slight increase in SER at high SNR is due to the amplitude saturation effects in the DAC due to the larger transmit power needed for 64-QAM modulation.

Moreover, the SER performance in the presence of I / Q compensation is very close to the ideal case. The 2 dB gap in the TX power between these two cases is due to the possibility of phase noise causing additional interference between adjacent OFDM tones. As a result, since the effective throughput curve 1502 uses all the data tones rather than only odd tones (ideally in the ideal case), it is possible to use as much data as when the I / Q method is applied compared to the ideal case Lt; RTI ID = 0.0 > times < / RTI >

16 is a graph showing SER performance of various QAM constellations depending on whether there is I / Q compensation. We observe in this embodiment that the proposed method is particularly useful for 64-QAM constellations. For 4-QAM and 16-QAM, the method for I / Q compensation may require greater power to enable both the data transmission and the interference cancellation from the mirror tones, so the I / Q mismatch Resulting in worse performance. In addition, 4-QAM and 16-QAM are not affected by I / Q mismatch by 64-QAM due to the larger minimum distance between the constellation points. A. Tarighat, R. Bagheri, and AH Sayed, "Compensation schemes and performance analysis of IQ imbalances in OFDM receivers", Signal Processing, IEEE Transactions on [see Acoustic, Speech, and Signal Processing, IEEE Transactions on Vol. . 53, pp. 3257-3268, August 2005. It can also be observed in Figure 16 by comparing the I / Q mismatch for the ideal case for 4-QMA and 16-QAM. Thus, the additional power required by the DIDO precoder with interference cancellation (from the mirror tones) is not reasonable with the small advantage of I / Q compensation for 4-QAM and 16-QAM cases. Note that this topic can be corrected by using methods 2 and 3 for I / Q compensation described above.

After all, the three methods described above are measured at various propagation conditions. Also, for reference, the SER property in the presence of I / Q mismatch is described. Figure 17 shows SER measured for a DIDO 2x2 system by 64-QAM at a carrier frequency of 450.5 MHz and a bandwidth of 250 KHz, at two different locations of users. At position 1, the users are ~ 6 lambda from BS in various laboratories and NLOS (Non-Line of Sight) conditions. At location 2, the users are ~ lambda from BS at LOS (Line of Sight).

Figure 17 shows that all three compensation methods always outperform those without compensation. In addition, it should be noted that Method 3 outperforms the other two compensation methods in a given channel scenario. The relative performance of methods 1 and 2 depends on propagation conditions. This is observed through realistic campaigns that surpass Method 2 because Method 1 is typically a pre-cancellation (at the transmitter) of the inter-user interference caused by the I / Q imbalance. When this inter-user interference is minimal, Method 2 can outperform Method 1 as shown in graph 1702 of FIG. 17 since it does not suffer from power loss due to the I / Q compensated precoder.

Up to now, various methods have been compared taking into account only a limited set of propagation scenarios as in Fig. In the following, the relative performance of these methods is measured in an ideal independent and identically-distributed (i.i.d.) channel. DIDO-OFDM systems are simulated with I / Q phase and gain imbalances on the transmit and receive sides. FIG. 18 shows the performance of the proposed methods when there is only gain imbalance on the transmit side (e.g., having a gain of 0.8 on the I rail of the first transmission chain and a gain of 1 on the other rail). It is observed that Method 3 surpasses all other methods. In addition, Method 1, as opposed to the results obtained at position 2 of graph 1702 of FIG. 0.0 > 2 < / RTI >

Thus, in the three new methods given to compensate for the I / Q imbalance in the DIDO-systems described above, Method 3 outperforms other proposed compensation methods. In systems with slow feedback channels, method 2 may be used to reduce the amount of feedback required for the DIDO precoder by eliminating the worse SER performance.

II. Adaptive DIDO transmission system

Another embodiment of a system and method for improving the performance of distributed-input distributed-output (DIDO) systems will now be described. This method dynamically allocates radio resources to different user devices by tracking changes in channel conditions to increase throughput while satisfying a predetermined target error rate. The user equipment estimates channel quality and feeds it back to the base station (BS); The base station processes the channel quality obtained from the user devices to select an array configuration for the next set of transmissions, the DIDO configuration, the modulation / coding scheme (MCS) of the user devices; The base station transmits the parallel data to the multi-user devices through pre-coding and the signals are demodulated in the receiver.

A system for effectively allocating resources for a DIDO wireless link is also described. The system comprising: a DIDO base station having a DIDO configurer for processing feedback received from the users to select a best set of users, a DIDO setup, a modulation / coding setup (MCS) and an array configuration for the next transmission; A receiver in a DIDO system for measuring channels and other corresponding parameters for generating a DIDO feedback signal; And a DIDO feedback control channel for transmitting feedback information from the users to the base station.

As described in detail below, certain important features of the above embodiments of the invention include, but are not limited to, the following:

To minimize the SER and maximize user or downlink per spectral efficiency, adapt the array configurations based on the number of users, DIDO transmission systems (such as antenna selection or multiplexing), the MCS and channel quality information Techniques for making the choice;

Techniques for defining a set of EK DIDO transmission modes in a combination of DIDO schemes and MCSs;

Techniques for allocating different DIDO modes for different time slots, OFDM tones and DIDO sub-streams according to channel conditions;

Techniques for dynamically allocating different DIDO modes for different users based on channel quality;

A criterion enabling adaptive DIDO switching based on link-quality metrics calculated on time;

A criterion enabling adaptive DIDO switching based on lookup tables.

(E.g., antenna selection or multiplexing), modulation / coding schemes (MCS), and channel quality information to minimize SER or maximize user or downlink spectral efficiency. 19, a DIDO system having a DIDO configurator at a base station;

As shown in FIG. 20, the base station has a DIDO configurator and a DIDO feedback generator in each user equipment. The DIDO configurer is used to generate a feedback message to be input to the DIDO configurator, DIDO system using parameters.

A DIDO configurer at the base station, a DIDO feedback generator, and a DIDO feedback control channel for communicating DIDO-specific configuration information from the users to the base station.

a. Background technology

In multi-input multiple-output (MIMO) systems, diversity systems such as orthogonal space-time block codes (OSTBC) (V. Tarokh, H. Jafarkhani, and AR Calderbank, (RW Heath Jr., S. Sandhu, and AJ Paulraj, "Codes for orthogonal designs ", IEEE Trans. Info. Th., vol. 45, pp. 1456-467, July 1999) (See IEEE Trans. Comm., Vol. 5, pp. 142-144, April 2001, "Antenna selection for spatial multiplexing systems with linear receivers") provides increased link robustness that translates to better coverage. To prevent channel fading. On the other hand, spatial multiplexing (SM) enables the transmission of multiple parallel data streams as a means to improve system throughput. G. J. Foschini, G. D. Golden, R. A. Valenzuela, and P. W. Wolniansky, "Simplified processing for high spectral efficiency wireless communication employing multielement arrays ", IEEE Jour. Select. Areas in Comm., Vol. 17, no. 11, pp. 1841-1852, November 1999. These advantages are discussed in L. Zheng and D.N.C. Tse "Diversity and multiplexing: a fundamental tradeoff in multiple antenna channels ", IEEE Trans. Info. Th., Vol. 49, no. 5, pp. Can be accomplished simultaneously in MIMO systems, according to the theoretical diversity / multiplexing tradeoffs presented in < RTI ID = 0.0 > IEEE, < / RTI > One embodiment is to adaptively switch between diversity and multiplexed transmission systems by tracking changes in channel conditions.

Many adaptive MIMO transmission techniques have been proposed so far. R. W. Heath and A. J. Paulraj, "Switching between diversity and multiplexing in MIMO systems ", IEEE Trans. Comm., Vol. 53, no. 6, pp. 962-968, June 2005, the diversity / multiplexing switching scheme has been designed to improve the bit error rate (BER) for fixed rate transmission based on ad hoc channel quality information. Alternatively, statistical channel information can be found in S. Catreux, V. Erceg, D. Gesbert, and R. W. Heath. Jr., " Adaptive modulation and MIMO coding for broadband wireless data networks ", IEEE Comm. Mag., Vol. 2, pp. 108-115, June 2002 ("Catreux"), which results in a reduction of the feedback overhead and the number of control messages. Adaptive transmission algorithms in Catreux are designed to improve spectral efficiency for predefined target error rates in orthogonal frequency division multiplexing (OFDM) systems based on channel time / frequency selectivity indicators. Similar low feedback adaptive methods have been proposed for narrowband systems using channel space selectivity for switching between diversity schemes and spatial multiplexing. For example, IEEE Trans. on Veh. Tech., A. Forenza, M. R. McKay, A. Pandharipande, R. W. Heath, Jr., and I. B. Collings, "Adaptive MIMO transmission for exploiting the capacity of spatially correlated channels "; IEEE Trans. on Veh. Tech., M. R. McKay, I. B. Collings, A. Forenza, and R. W. Heath. Jr., "Multiplexing / beamforming switching for coded MIMO in spatially correlated Rayleigh channels "; A. Forenza, M. R. McKay, R. W. Heath. Jr., and I. B. Collings, "Switching between OSTBC and spatial multiplexing with linear receivers in spatially correlated MIMO channels ", Proc. IEEE Veh. Technol. Conf., Vol. 3, pp. 1387-1391, May 2006; Proc. See, for example, M. R. McKay, I. B. Collings, A. Forenza, and R. W. Heath Jr., "A throughput-based adaptive MIMO BICM approach for spatially correlated channels", IEEE ICC, June 2006.

In this document, we extend the scope of the work presented in various prior publications on DIDO-OFDM systems. For example, R. W. Heath and A. J. Paulraj, "Switching between diversity and multiplexing in MIMO systems ", IEEE Trans. Comm., Vol. 53, no. 6, pp. Pp. 962-968, June 2005, S. Catreux, V. Erceg, D. Gesbert, and R. W. Heath Jr., "Adaptive modulation and MIMO coding for broadband wireless data networks", IEEE Comm. Mag., Vol. 2, pp. 108-115, June 2002; A. Forenza, M. R. McKay, A. Pandharipande, R. W. Heath Jr., and I. B. Collings, "Adaptive MIMO transmission for exploiting the capacity of spatially correlated channels", IEEE Trans. on Veh. Tech., Vol. 56, n.2, pp. 619-630, March 2007; IEEE Trans. on Veh. Tech., M. R. McKay, I. B. Collings, A. Forenza, and R. W. Heath Jr., "Multiplexing / beamforming switching for coded MIMO in spatially correlated Rayleigh channels", December 2007; A. Forenza, M. R. McKay, R. W. Heath Jr., and I. B. Collings, "Switching between OSTBC and spatial multiplexing with linear receivers in spatially correlated MIMO channels", Proc. IEEE Veh. Technol. Conf., Vol. 3, pp. 1387-1391, May 2006; Proc. See, for example, M. R. McKay, I. B. Collings, A. Forenza, and R. W. Heath Jr., "A throughput-based adaptive MIMO BICM approach for spatially correlated channels", IEEE ICC, June 2006.

A new adaptive DIDO transmission strategy for switching between a number of different users, a plurality of transmit antennas based on channel quality information and transmission schemes as a means for improving system performance is described herein. Systems that adaptively select the users in multiuser MIMO systems have already been described by M. Sharif and B. Hassibi in " On the Capacity of MIMO broadcast channel with partial side information ", IEEE Trans. Info. Th., Vol. 51, p. 506522, February 2005; And IEEE Trans. It is noted in W. Choi, A. Forenza, J. G. Andrews, and R. W. Heath Jr. in "On Cmmunications", "Opportunistic space division multiple access with beam selection". However, in these publications, the opportunistic space division multiple access (OSDMA) scheme is designed to maximize the capacity sum by using multi-user diversity, since they are not fully pre-erased at the transmitter, only a fraction of the theoretical capacity of dirty paper codes is achieved. In the DIDO transmission algorithm described herein, block diagonalization is used to pre-cancel inter-user interference. However, the proposed adaptive transmission scheme can be applied to a given DIDO system independently of the pre-coding technique type.

This patent application describes, without limitation, the following additional features of the present invention, including but not limited to the above,

1. training symbols of a conventional application for channel estimation may be used by wireless client devices to evaluate link-quality metrics in the adaptive DIDO scheme;

2. The base station receives signal characteristic data from the client devices as described in the prior application. In this embodiment, the signal characteristic data is defined as a link-quality metric used to enable adaptation;

3. The conventional application describes a mechanism for selecting multiple transmit antennas and users as well as defining throughput allocation. In addition, different levels of throughput can be dynamically assigned to different clients as in the conventional application. This embodiment of the invention defines new criteria for such selection and throughput allocation.

b. Embodiments of the Invention

The goal of the proposed adaptive DIDO technique is to dynamically allocate radio resources in time, frequency and space to different users in the system, thereby improving spectral efficiency per user or downlink. A common adaptation criterion is to increase the throughput while satisfying the target error rate. Also, in accordance with propagation conditions, such an adaptive algorithm may be used to improve the link quality (or coverage) of the users via diversity schemes. The flowchart shown in FIG. 21 describes the steps of the adaptive DIDO scheme.

In step 2102, the base station (BS) collects channel state information (CSI) from all users. In step 2104, from the received CSI, the base station calculates link quality metrics in a time / frequency / spatial domain. At step 2106, these link quality metrics are used to select the users to be provided for the next transmission as well as the transmission mode for each of the users. Note that the transmission modes consist of various combinations of modulation / coding and DIDO schemes. As a result, the BS transmits data to the users through DIDO pre-coding as in step 2108.

In step 2102, the base station collects channel state information (CSI) from all user equipments. In step 2104, the CSI is used by the base station to determine temporal or statistical channel quality for all user equipments. In DIDO-OFDM systems, the channel quality (or link quality metric) may be estimated in the time, frequency and spatial domain. Then, in step 2106, the base station uses the link quality metric to determine the best subset of users and the transmission mode in the current wave conditions. The set of DIDO transmission modes is defined as a combination of DIDO systems (such as antenna selection or multiplexing), modulation / coding systems (MCSs), and array configuration. In step 2108, data is transmitted to the user devices using the number of users selected and the transmission modes.

The mode selection is enabled by pre-computed lookup tables (LUTs) based on the error rate performance of DIDO systems in various propagation environments. These LUTs map channel quality information to error rate performance. To construct the LUTs, the error rate performance of DIDO systems is evaluated in various propagation scenarios as a function of SNR. From the error rate curves, it is possible to calculate the minimum SNR required to achieve a predetermined predefined target error rate. We define these SNR conditions with SNR thresholds. The SNR thresholds are then evaluated and stored in the LUTs in various propagation scenarios and in various DIDO transmission modes. For example, the SER in Figures 24 and 26 can be used to construct the LUTs. Then, from the LUTs, the base station selects transmission modes for active users that increase the throughput while meeting a predefined target error rate. As a result, the base station transmits data to selected users through DIDO pre-coding. The various DIDO modes may be designated with various time slots, OFDE tones, and DIDO substreams so that adaptation may occur in time, frequency and spatial regions.

One embodiment of a system using DIDO adaptation is shown in Figures 19-20. Several new functional units are introduced to enable the implementation of the proposed DIDO adaptation algorithms. In one embodiment, the DIDO constructor 1910 is configured to determine the number of users, DIDO transmission systems (e.g., antenna selection or multiplexing), modulation / coding schemes (MCS) Information 1012, and the like.

Selects data associated with the plurality of users U ₁ -U _M obtained by the DIDO constructor 1910 and provides the information to each of the plurality of encoding modulation units 1905. Each coding modulation unit 1904 encodes and modulates the information bits of each user and sends them to a mapping unit 1906. The mapping unit 1906 maps the input bits to composite symbols and sends it to the pre-encoding unit 1908. [ Both the encoding modulation units 1904 and the mapping unit 1906 use information obtained from the DIDO configurator unit 1910 to select the type of modulation / coding scheme to use for each user. The information is calculated by the DIDO configurator unit 1910 by using the channel quality information of each of the users as provided by the feedback unit 1912. [ The DIDO pre-coding unit 1908 uses the information obtained by the DIDO composer unit 1910 to calculate DIDO pre-coding weights and to pre-encode the input symbols obtained from the mapping units 1906. Each of the pre-encoded data streams is sent to an OFDM unit 1915 which calculates the IFFT by the DIDO pre-coding unit 1908 and adds the cyclic prefix. The information is sent to a D / A unit 1916 which operates on digital to analog conversion and sends the resulting analog signal to an RF unit 1914. The RF unit 1914 upconverts the baseband signal to an intermediate / radio frequency and sends it to the transmit antenna.

The RF units 2008 of each client device receive signals transmitted from the DIDO transmitter unit 1914, downconvert the signals to baseband, transmit the down-converted signals to the A / D units 2010, . The A / D units 2010 then convert the signal from analog to digital and send it to the OFDM units 2013. The OFDM units 2013 remove the cyclic prefix and perform an FFT to report the signal in the frequency domain. During the training period, the OFDM units 2013 send an output to a channel estimation unit 2004, which computes channel estimation values in the frequency domain. Alternatively, the channel estimate may be computed in the time domain. During the data period, the OFDM units 2013 send an output to a receiver unit 2002 that demodulates / decodes the signal to obtain data 2014. The channel estimation unit 2004 sends the channel estimate values to a DIDO feedback generator unit 2006 that quantizes the channel estimate values and sends it back to the transmitter via a feedback control channel 1912. [

The DIDO constructor 1910 uses the information from the base station or, in the preferred embodiment, further uses the output of the DIDO feedback generator 2006 (see FIG. 20) operating in each user equipment. The DIDO feedback generator 2006 may include information at the receiver, quantize information, and / or use a partially limited feedback scheme known in the art to which the present invention pertains.

The DIDO constructor 1910 may use the recovered information from the DIDO feedback control channel 1912. The DIDO feedback control channel 1912 is a logical or physical control channel used to send the output of the DIDO feedback generator 2006 from the user to the base station. The control channel 1912 may be implemented in many ways known in the art of the present invention and may be a logical or physical control channel. As a physical channel, it may include the illustrated time / frequency slot assigned to the user. It may also be a random access channel shared by all users. The control channel may be pre-specified, which may be generated by stolen bits from an existing control channel in a predefined manner.

In the following discussion, the results obtained through measurements according to the DIDO-OFDM prototype are described in actual propagation environments. These results show the potential benefits achievable in adaptive DIDO systems. The performance of different orderer DIDO systems is presented for the first time to show that it is possible to increase the number of antennas / users to achieve more downlink throughput. The DIDO performance is then described as a function of the location of the user equipment, showing the need for tracking changes in channel conditions. Finally, the performance of DIDO systems using diversity techniques is described.

i. Performance of Multiday DIDO Systems

The performance of both DIDO systems is estimated by increasing the number of transmit antennas N = M, where M is the number of users. The performance of the following systems is compared to: SISO, DIDO 2x2, DIDO 4x4, DIDO 6x6, and DIDO 8x8. DIDO N × M refers to the DIDO with N transmit antennas and M users in the BS.

22 shows a transmit / receive antenna arrangement. The transmit antennas 2201 are arranged in a rectangular array configuration, and the users are located around the transmit array. In Fig. 22, T denotes "transmit" antennas and U denotes "user devices 2202 ".

The various antenna subsets are activated with an 8-element transmit array according to the value of N selected with various measurements. For each DIDO order, (N) subsets of antennas covering the largest real estate for the fixed size constraint of the 8-element array are selected. This criterion is expected to improve spatial diversity for a given given N value.

FIG. 23 shows array configurations for various DIDO orders for an available real world (e.g., dotted line). The square dotted box is 24 "× 24", corresponding to ~ λ × λ at a carrier frequency of 450 MHz in size.

Based on the explanations relating to FIG. 23, and referring to FIG. 22, the performance of each of the following systems will be defined and compared as follows:

SISO with T1 and U1 (2301)

DIDO 2 x 2 with T1,2 and U1,2 (2302)

DIDO 4 x 4 with T1, 2, 3, 4 and U1,2,3,4 (2303)

DIDO 6 x 6 with T1, 2, 3, 4, 5, 6 and U1,2,3,4,5,6 (2304)

DIDO 8x8 with T1,2,3,4,5,6,7,8 and U1,2,3,4,5,6,7,8 (2305)

24 shows SER, BER, and SE (Spectral Efficiency) as a function of the transmit (TX) power for the DIDO systems described above by 4-QAM and 1/2 FEC (Forward Error Correction) Spectral efficiency) and effective throughput performance. It is observed that the SER and BER performances decrease as the value of N increases. This result is due to two phenomena: for fixed TX power, the input power to the DIDO array is split between an increased number of users (or data streams); The spatial diversity is reduced by an increase in the number of users in actual (spatially correlated) DIDO channels.

To compare the relative performance of the multi-order DIDO systems, the target BER is fixed at 10 ^-4 (this value may vary depending on the system), which corresponds to approximately SER = 10 ^-2 , as shown in FIG. We refer to the TX power values corresponding to the target as TX power thresholds (TPT). For a given N, if the TX power is less than the TPT, we assume that it is not possible to transmit with a DIDO order N, and we need to switch to a lower order DIDO. Also in FIG. 24, the SE and effective throughput performance are saturated when the TX power exceeds the TPTs for a given N value. From these results, an adaptive transmission scheme can be designed to switch between SE or a multi-way DIDO for a fixed predefined target error rate.

ii. Performance due to variable user location

The purpose of this experiment is to evaluate the DIDO performance for various users' positions through simulations on spatially correlated channels. DIDO 2 × 2 systems are considered by 4QAM and FEC ratios of 1/2. User 1 is in the broadside direction from the transmit array while User 2 changes positions in the endfire direction as shown in FIG. The transmit antennas are spaced apart by ~ lambda / 2 and are ~ 2.5λ away from the users.

26 shows the SER and user-specific SE results for various locations of the user equipment 2. As shown in FIG. The angles of arrival (AOAs) of the user equipment are in the range between 0 ° and 90 ° and are measured from the transverse direction of the transmission array. It is observed that as the angular separation of the user equipment increases, the DIDO performance is improved due to the greater diversity available in the DIDO channel. Further, at a target SER = 10 ^-2 , there is a 10 dB gap between AOA ² = 0 ° and AOA 2 = 90 °. This result is consistent with the simulation results obtained in Fig. 35 for an angle spread of 10 [deg.]. Also, in the case of AOA1 = AOA2-0, there can be coupling effects between the two users (due to the proximity of the antennas) whose performance can be changed from the simulated results of FIG.

iii. Preferred scenarios for DIDO 8 × 8

Figure 24 shows that DIDO 8x8 results in a larger SE than lower DIDO while eliminating the higher TX power conditions. The goal of this analysis is to show that DIDO 8 × 8 surpasses DIDO 2 × 2 in terms of maximum power efficiency (SE) as well as TX power condition (or TPT) to achieve low maximum SE.

Note that for i.i.d. (ideal) channels, there is a ~ 6dB gap in the TX power between DIDO 8x8 and SE of DIDO 2x2. This gap is due to the fact that DIDO 8 x 8 separates the TX power over 8 data streams while DIDO 2 x 2 separates only between the two streams. This result is shown through the simulation of Fig.

However, in spatially correlated channels, the TPT is a function of the propagation environment (e.g., array direction, user location, angular spread) characteristics. For example, Figure 35 shows a ~ 15dB gap for low angular spread between two different user equipment locations. Similar results are presented in Figure 26 of the present application.

Similar to MIMO systems, the performance of DIDO systems degrades when the users are positioned in the longitudinal directions from the TX array (due to lack of diversity). These effects have been observed through measurements by current DIDO prototypes. Thus, one way to show that DIDO 8x8 outperforms DIDO 2x2 is to place the users in the longitudinal directions for the DIDO 2x2 array. In this scenario, DIDO 8x8 surpasses DIDO 2x2 due to the higher diversity provided by the 8-antenna array.

In this analysis, the following systems are considered as follows:

System 1: DIDO 8x8 by 4-QAM (transmitting 8 parallel data streams per time slot);

System 2: DIDO 2 x 2 by 64-QAM (four are sent to users X and Y per time slot). In such a system, we consider a combination of the following four TX and RX antenna positions: a) T1, T2 U1, U2 (longitudinal direction); b) T3, T4 U3,4 (longitudinal); c) T5, T6 U5,6 (~ 30 [deg.] from the longitudinal direction); d) T7, T8 U7,8 (NLOS (Non-Line of Sight));

System 3: DIDO 8x8 by 64-QAM; And

System 4: MISO 8 × 1 by 64-QAM (sent to user X every 8 time slots)

In all these cases, a FEC ratio of 3/4 was used.

The locations of the users are shown in Fig.

In FIG. 28, the SER exhibits a ~ 15dB gap between systems 2a and 2c due to various array directions and user locations (similar to simulation results in Figure 35). The first subplot in the second column represents the value of the TX power at which the SE curves are saturated (e. G., Corresponding to BER 1e-4). We observe that System 1 causes a larger per-user SE for lower TX power conditions (less than ~ 5 dB) than System 2. In addition, the advantage of DIDO 8 × 8 for DIDO 2 × 2 is more evident for DL (downlink) SE and DL effective throughput due to the multiplexing gain of DIDO 2DIDO 2 × 2 or more DIDO 8 × 8. System 4 has lower TX power conditions (less than 8 dB) than System 1 due to the array gain of beamforming (e.g., MRC by MISO 8 × 1). System 2 has worse performance than System 1 (e.g., resulting in a lower SE for larger TX power conditions). As a result, System 3 results in a much larger SE (due to higher order modulations) than System 1 for larger TX power conditions (~ 15dB).

From these results, the following conclusions are drawn as follows:

One channel scenario was identified to increase DIDO 8 × 8 to increase DIDO 2 × 2 (eg, to result in a larger SE for lower TX power conditions).

In this channel scenario, DIDO 8x8 results in a per-user SE that is greater than DIDO 2x2 and MISO 8x1;

It is possible to further increase the performance of DIDO 8x8 using higher order modulations (such as 64-QAM rather than 4-QAM), which have eliminated the larger TX power conditions (~ 15dB or more).

iv. DIDO by antenna selection

In the following, we describe the IEEE Trans. On Signal Procesing, R. Chen, R. W. Heath, and J. G. Andrews, 2005, "Advantages of Antenna Selection Algorithms" described in "Transmit selection diversity for unitary precoded multiuser spatial multiplexing systems with linear receivers" We present results for one particular DIDO system with two users, 4-QAM and 1/2 FEC ratios. The following systems are compared in Figure 27:

DIDO 2 x 2 with T1,2 and U1,2; And

DIDO 3 × 2 using antenna selection with T1,2,3 and U1,2

The positions of the transmission antennas and the user device positions are the same as in the case of FIG.

Figure 29 shows that DIDO 3x3 by antenna selection can provide ~ 5dB gain over DIDO 2x2 systems (no selection). Note that the channels are mostly fixed (such as no Doppler), so that the selection algorithms are adapted to path-loss and channel space correction rather than fast-fading. Also, in this particular embodiment, the antenna selection algorithm is observed to select antennas 2 and 3 for transmission.

v . SNR thresholds for LUTs

In method 2, we mentioned that mode selection is enabled by LUTs. The LUTs may be precalculated by evaluating SNR thresholds to achieve a specific pre-defined target error rate performance for the DIDO transmission modes in various propagation environments. Hereinafter, we provide the performance of DIDO systems with and without antenna selection and a variable number of users that can be used as a guideline for constructing the LUTs. Figures 24, 26, 28 and 29 come from measured values by the DIDO prototype, while the following figures are obtained through simulations. The following BER results assume no FEC.

Figure 30 shows the average BER performance of various DIDO pre-coding schemes on the iid channels. "No selection" is displayed, so the curve refers to when BD is used. In the same figure, the performance of the antenna selection ASel is shown by the number of additional antennas (with respect to the number of users). As the number of additional antennas increases. It can be seen that ASel provides better diversity gain (characterized by the slope of the BER curve in the high SNR system) resulting in better coverage. For example, if we fix the target BER to 10 ^-2 (the actual value for unencoded systems), the SNR gain provided by Asel is increased by the number of antennas.

Figure 31 shows the SNR gain of ASel as a function of the number of additional transmit antennas in the iid channels, for various target BERs. By adding just one or two antennas, it can be seen that ASel results in a significant SNR gain compared to BD. In the following sections, we will evaluate the performance of ASel by fixing the target BER to 10 ^-2 (for unencoded systems) in the case of only one or two additional antennas.

[0252] FIG. SNR thresholds as a function of the number of users (M) for BD and ASel with 1 and 2 additional antennas in the channels. Note that we assume a fixed total transmit power (depending on the variable number of transmit antennas) for many users. In addition, Fig. 32 shows that the gain due to antenna selection is i. Channels are constant for many users.

는 사용자들의 평균 AOA들 간의 각 분리이다.In the following, we show the performance of DIDO systems in spatially correlated channels. We describe the "Channel models for link and system level simulations" by X. Zhuang, FW Vook, KL Baum, TA Thoma, and M. Cudak, IEEE 802.16 Broadband Wireless Access Working Group, 259 Simulate the channel of each user through the spatial channel model. We create a single cluster for each user. As a case study, we assume NLOS channels of a uniform linear array (ULA) in the transmitter with a 0.5 lambda element spacing. In the case of a two-user system, we simulate clusters with respective average arrival angles (AOA1 and AOA2) for the first and second users. When there are two or more users in the system, the AOAs create clusters of users with uniformly spaced average AOAs in the range [-φ _m , φ _m ], where we have K, the number of users, And

Is the separation between the average AOAs of the users.

Note that the angular range [-φ _m , φ _m ] is centered at 0 ° corresponding to the transverse direction of the ULA. In the following, we study the BER performance of DIDO systems with BD and ASel transmission systems and various users as a function of channel angular spreading (AS) and each separation between users.

Figure 33 shows BER versus user average SNR for two users located in the same respective direction (e.g., AOA1 = AOA2 = 0 for the horizontal direction of the ULA), by the values of the various ASs. Wherein the AS is improved as the BER performance is increased and the i.i.d. You can see that it is close to the case. In fact, the higher the AS statistically, the less overlap between the eigenmodes of the two users and the better the performance of the BD precoder.

34 shows results similar to those of FIG. 33, but has a higher angular separation between users. We consider AOA1 = 0 DEG and AOA2-90 DEG (such as 90 DEG angle separation). The best performance is achieved in low AS cases. In fact, in the case of a high angular separation, there is less overlap between the eigenmodes of the users when the respective angles are low. Interestingly, we found that for the same reasons mentioned in the BER performance at low AS, i.i.d. Channels are better observed.

Next, we compute SNR thresholds for a target BER of 10 ^-2 in various correlation scenarios. Figure 35 constructs the SNR thresholds as a function of the AS for the average AOAs of users of various values. For each separation of low users, reliable transmission with reasonable SNR conditions (e.g., 18dB) is only possible for channels characterized by high AS. On the other hand, if the users are spatially separated, less SNR is required to meet the same target BER.

로 된 식(13)의 정의에 따라 생성된다. 우리는

= 0°이고 AS < 15°일 때, BD는 사용자들 사이에 작은 각 분리로 인해 저조하게 수행하며, 상기 목표 BER은 만족하지 않는 것으로 관측된다. AS를 증가시키기 위해 고정된 목표 BER을 충족시키기 위한 조건은 저하된다. 한편,

= 30°일 때, 최소 SNR 조건은 도 35에 결과들에 대해 계속 낮은 AS에서 획득된다. AS가 증가함에 따라, 상기 SNR 임계값들은 i.i.d. 채널들 중 하나로 포화된다. 5개의 사용자들에 의한

= 30°은 [-60°, 60°]의 AOA 범위에 해당하며, 이는 120°의 섹터화된 셀들을 갖는 셀룰러 시스템들의 기지국에선 전형적이다. Figure 36 shows the SNR threshold for a case of five users. The average AOAs of the users may be separated

(13). &Lt; / RTI &gt; We are

= 0 [deg.] And AS &lt; 15 [deg.], BD is performed poorly due to small angular separation between users, and the target BER is observed to be unsatisfactory. The condition for meeting the fixed target BER to increase AS is degraded. Meanwhile,

= 30 [deg.], The minimum SNR condition is obtained at the lower AS for the results in Fig. As the AS increases, the SNR thresholds saturate to one of the iid channels. By 5 users

= 30 ° corresponds to the AOA range of [-60 °, 60 °], which is typical for base stations of cellular systems with 120 ° sectored cells.

Next, we study the performance of the ASel transmission scheme in spatially correlated channels. 37 compares SNR thresholds of BD and ASel with one and two additional antennas for two user cases. We consider two different angular separation cases between users: {AOA1 = 0 °, AOA2 = 0 °} and {AOA1 = 0 °, AOA2 = 90 °}. The curves for the BE scheme (e.g., antenna not selected) are the same as in FIG. We have observed that ASel causes 8dB and 10dB SNR gain, respectively, by one and two additional antennas for high AS. As the AS decreases, the gain due to ASel through the BD becomes smaller due to the reduced number of degrees of freedom in the MIMO broadcast channel. Interestingly, in the case of AS = 0 DEG (such as near the LOS channel) and {AOS1 = 0 DEG, AOA2 = 90 DEG}, ASel does not provide any gain due to diversity in the spatial domain. Figure 38 shows results similar to Figure 37, but with five user cases.

=120°/(M-1)이다.We compute the BD and ASel transmission systems, both for the (assuming a target BER of usually 10 ^-2) on the system (M) as a function of the number of users SNR threshold. The SNR thresholds correspond to the average SNR, so that the total transmit power is constant at a predetermined M. We assume maximum separation between the average AOAs of each user's cluster within the azimuth range [-φ _m , φ _m ] = [-60 °, 60 °]. Thereafter, each separation between users

= 120 DEG / (M-1).

가 매우 작고 BD가 비현실적이므로, 상기 SNR 조건은 40dB 이상이다. 게다가, AS > 10°일 때 상기 SNR 임계값은 거의 소정 M으로 일정하게 존재하고, 공간적으로 상관 있는 채널들에서의 DIDO 시스템은 i.i.d. 채널들의 성능에 근접한다.Figure 39 shows SNR thresholds for a BD system with AS of various values. We observe that the lowest SNR condition is obtained at AS = 0.1 ° (eg, low angular spread) by a relatively small number of users (eg, K ≤ 20) due to large angular separation between users. However, when M> 50,

Is very small and BD is unrealistic, the SNR condition is more than 40dB. In addition, when AS > 10, the SNR threshold is almost constant at a predetermined M, and the DIDO system in spatially correlated channels approximates the performance of the iid channels.

In order to reduce the value of the SNR thresholds and to improve the performance of the DIDO system, we apply the ASel transmission scheme. Figure 40 shows SNR thresholds on spatially correlated channels by AS = 0.1 for BD and AS d with one and two additional antennas. Also, for reference, we refer to the i.i.d. Report the curve for the case. For low numbers of users (such as M &lt; = 10), antenna selection can not further reduce the SNR condition due to lack of diversity in the DIDO broadcast channel. As the number of users increases, the ASel advantage from multi-user diversity results in an SNR gain (e.g., 4 dB for M = 20). In addition, for M? 20, the performance of an ASel with one or two additional antennas in highly spatially correlated channels is the same.

Then we compute SNR thresholds for two or more channel scenarios: AS = 5 [deg.] In Figure 41 and AS = 10 [deg.] In Figure 42. [ Also, Figure 41 shows that, due to the large angular variance, contrary to Figure 40, ASel causes SNR gains for a relatively small number of users (e.g., M &lt; = 10). In the case of AS = 10, the SNR thresholds are further reduced as shown in FIG. 42 because the ASel is higher.

After all, we summarize the results presented in the correlated channels so far. Figures 43 and 44 show the SNR thresholds as a function of the number of users (M) and the variance (AS) for BD and ASel schemes. When AS = 30 °, i.i.d. Channels, and we note that we only use this AS value as a configuration for the graph representation. We observe that while BD is affected by channel spatial correlation, ASel results in approximately the same performance for a given AS. In addition, if AS = 0.1, ASel performs as well as BD for low M, whereas for large M (such as M &gt; = 20) it exceeds BD due to multi-user diversity.

Figure 49 compares the performance of various DIDO schemes for SNR thresholds. The DIDO schemes are considered: BD, ASel, BD with eigenmode selection (BD-ESel), and maximum ratio combining (MRC).

Note that the MRC does not pre-clear the interference at the transmitter (unlike other methods), but provides a larger benefit when users are spatially separated. In Figure 49 we construct the SNR threshold for the target BER = 10 ^-2 for DIDO N x 2 systems when two users are located at -30 ° and 30 ° respectively from the transverse direction of the transmission array . We note that for a low AS, the MRC scheme provides a 3 dB gain over other systems because the users' spatial channels are well separated and the effect of inter-user interference is low. Note that the gain of the MRC over DIDO N × 2 is due to the array gain. For ASs greater than 20 °, the QR-Asel setup surpasses the others and results in a gain of about 10 dB compared to BD 2 × 2 with no optics. QR-ASel and BD-ESel provide about the same performance for a given value of AS.

A novel adaptive transmission technique for DIDO systems is described above. This method dynamically switches between the DIDO transmission modes for various users to improve the throughput for the fixed target error rate. It is observed that the performance of multi-order DIDO systems can be measured at various propagation conditions and significant gains in throughput can be achieved by dynamically selecting the DIDO modes and number of users as a function of the propagation conditions.

III. Precompensation of frequency and phase offset

a. Background technology

As previously described, wireless communication systems use carriers to convey information. These carriers are typically sinusoids that are modulated amplitude and / or phase in response to information to be transmitted. The nominal frequency of the sinusoidal wave is known as the carrier frequency. To generate such a waveform, the transmitter uses up-conversion to synthesize one or more sinusoids and generate a modulated signal with sinusoids at a specified carrier frequency. This can be done via direct conversion if the signal is directly modulated for the carrier or via multiple up-conversion steps. To process such a waveform, the receiver must demodulate the received RF signal and effectively remove the modulated carrier. This requires that the receiver synthesize one or more sinusoidal signals, contrary to the modulation process, at the transmitter, known as downconversion. Unfortunately, the sinusoidal signals generated at the transmitter and receiver are derived from various reference oscillators. Without a reference oscillator, you can not create a perfect frequency reference; There is always a certain deviation from the actual true frequency.

In wireless communication systems, the differences in the outputs of the reference oscillators at the transmitter and receivers create a phenomenon known as a carrier frequency offset at the receiver, or simply a frequency offset. This results in a higher bit error rate and lower throughput, resulting in distortion in the received signal.

There are a variety of techniques for dealing with carrier frequency offsets. Most approaches estimate the carrier frequency offset in the receiver and then apply a carrier frequency offset correction algorithm. The carrier frequency offset estimation algorithm is based on offset QAM (T. Fusco and M. Tanda, "Blind Frequency-offset Estimation for OFDM / OQAM Systems", Signal Processing, IEEE Transactions on [Acoustics, Speech, , Vol. 55, pp. 1828-1838, 2007); Periodic properties (E. Serpedin, A. Chevreuil, GB Giannakis and P. Loubaton, "Blind channel and carrier frequency offset estimation using periodic modulation precoders", Signal Processing, IEEE Transactions on [Acoustics, Speech, Transactions on, vol. 48, no. 8, pp. 2389-2405, August 2000); (JJ van de Beek, M. Sandell, and PO Borjesson, "ML estimation of time and frequency offset in OFDM systems" Signal Processing, IEEE Transactions on [Acoustics U. Tureli, H. Liu, and MD Zoltowski, "OFDM blind carrier offset " in IEEE Transactions on Vol. 45, No. 7, pp. 1800-1805, July 1997; M. Lowise, M. Marselli, and J. Reggiannini, "Low-complexity blind carrier," IEEE Trans. Commun., Vol. 48, No. 9, pp. 1459-1461, 50, No. 7, pp. 1182-1188, July 2002), for example, in a frequency-selective OFDM signal over frequency-selective radio channels ", IEEE Trans. Commun., vol.

Alternatively, special training signals may be generated using the repetitive data symbols (PH Moose, " A technique for orthogonal frequency division multiplexing frequency offset correction ", IEEE Trans. Commun., Vol. 42, no. 10, pp. 2908-2914, 1994 October); Two other symbols (T. M. Schmidl and D. C. Cox, "Robust frequency and timing synchronization for OFDM ", IEEE Trans. Commun., 45, no. 12, pp. 1613-1621, December 1997); Or periodically inserted known symbol sequences (M. Luise and R. Reggiannini, "Carrier frequency acquisition and tracking for OFDM systems ", IEEE Trans. Commun., Vol. 44, No. 11, pp. 1590-1598, November 1996). &Lt; / RTI &gt; The correction may occur in analog or digital. The receiver may also use a carrier frequency offset estimate to pre-correct the transmitted signal to remove the offset. Carrier frequency offset correction has been extensively studied for multicarrier and OFDM systems due to its sensitivity to frequency offsets (JJ van de Beek, M. Sandell, and PO Borjesson, "ML estimation of time and frequency offset in OFDM systems U. Tureli, H. Liu, "Signal Processing, IEEE Transactions on [Acoustics, Speech, and Signal Processing, IEEE Transactions on Vol. 45, No. 7, pp. 1800-1805, July 1997] And MD Zoltowski, "OFDM Blind Carrier Offset Estimation: ESPRIT ", IEEE Trans. Commun., Vol. 48, no 12, pp. 1613-1621, December 1997; M. Luise, M. Marselli, and R. Reggiannini, "Low-complexity blind carrier frequency recovery for OFDM signals over frequency-selective radio channels &quot;, IEEE Trans. Commun., Vol. 50, No. 7, pp. 1182-1188, July 2002).

Frequency offset estimation and correction is an important topic in multi-antenna communication systems or, more generally, multiple input multiple out (MIMO) systems. In MIMO systems, when transmit antennas are locked to one frequency reference and receivers are locked to another frequency reference, there is a single offset between the transmitter and the receiver. Several algorithms have been proposed to address this problem using training signals (K. Lee and J. Chun, "Frequency-offset estimation for MIMO and OFDM systems using orthogonal training sequences, IEEE Trans. Veh. Signal Processing, IEEE Transactions on [Acoustics, Speech, Vol. 1, pp. 146-156, January 2007; M. Ghogho and A. Swami, "Training design for multipath channel and frequency offset estimation in MIMO systems" , 54, no. 10, pp. 3957-3965, October 2006, and adaptive tracking C. Oberli and B. Daneshrad, "Maximum likelihood tracking algorithms for MIMOOFDM", IEEE Transactions on Signal Processing, a more serious problem is that although the transmit antennas are not locked to the same frequency reference, the receive antennas And is encountered in MIMO systems when locked together. Actually occurs in the uplink of a space division multiple access (SDMA) system, which can be viewed as a MIMO system where the various users correspond to the various transmit antennas. In this case, the compensation of the frequency offset is much more complex In detail, the frequency offset generates interference between the various transmitted MIMO streams, which can be used for complex combination estimation and equalization algorithms (A. Kannan, TP Krauss, and MD Zoltowski, "Separation of cochannel signals under imperfect timing and carrier synchronization &quot;, IEEE Trans. Veh. Technol., Vol. 50, no. 1, pp. 79-96, January 2001) and equalization according to frequency offset estimation (T. Tang and R. W. Heath, "Joint frequency offset estimation and interference cancellation for MIMO-OFDM systems [mobile radio] ... VTC2004-Fall 2004 IEEE 60 th Vehicular Technology Conference, vol 3, pp 1553-1557, September 2004, 26-29; X. Dai's "Carrier frequency offset estimation for OFDM / SDMA systems using consecutive pilots", IEEE Proceedings-Communications, vol. 152, pp. 624-632, October 7, 2005). Some work addresses the problem of residual phase off-set and tracking rate when residual phase offsets are estimated and compensated after frequency offset estimation, but this task only considers the uplink of the SDMA OFDMA system (L. Haring, S. et al. Bieder, and A. Czylwik, "Residual Carrier and Sampling Frequency Synchronization in OFDM Systems &quot;, 2006. VTC, April 2006. IEEE 63rd Vehicular Technoloty Conference, vol. 4, pp. 1937-1941, 2006). The most serious case in MIMO systems occurs when all transmit and receive antennas have different frequency references. Only available work on this topic deals with asymptotic analysis of estimation errors in flat fading channels (O. Besson and P. Stoica, "On parameter estimation of MIMO flat-fading channels with frequency offsets" , Signal Processing, IEEE Transactions on [Acoustics, Speech, and Signal Processing, IEEE Transactions on, vol. 51, no. 3, pp. 602-613, March 2003).

Unless a great deal of research has been done, various transmission antennas of the MIMO system do not have the same frequency reference, and they occur when the receiving antennas process the signals independently. This is what is known in the literature as a distributed input distributed-output (DIDO) communication system, also referred to as the MIMO broadcast channel. DIDO systems use the same radio resources (such as the same slot period and frequency band) as conventional SISO systems, while transmitting parallel data streams (via pre-coding) to multiple users to improve downlink throughput And one access point with distributed antennas. A detailed description of DIDO systems is presented in S. G. Perlman and T. Cotter, " System and method for distributed input-distributed output wireless communications ", US Patent Application 20060023803, July 2004. There are many ways to implement DIDO precoders. As a solution, for example, Q. h. Spencer, A. L. Swindlehurst, and M. Haardt, "Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels", IEEE Trans. Sig. Proc., Vol. 52, pp. 461-471, February 2004; K. K. Wong, R. D. Murch, and K. B. Letaief, "A joint-channel diagonalization for multiuser MIMO antenna systems ", IEEE Trans. Wireless Comm., Vol. 2, pp. 773-786, July 2003; L. U. Choi and R. D. Murch, "A Transmit Preprocessing Technique for Multiuser MIMO Systems Using a Decomposition Approach", IEEE Trans. Wireless Comm., Vol. 3, pp. 20-24, January 2004; IEEE Trans. Sig. Proc., Z. Shen, J. G. Andrews, R. W. Heath, and B. L. Evans, published in September 2005, entitled "Low complexity user selection algorithms for multi-user MIMO systems with block diagonalization "; IEEE Trans. Z. Shen, R. Chen, J. G. Andrews, R. W. Heath, and B. L. Evans, Submitted to Wireless Comm., 2005, 10 dnjfp, "Sum capacity of multiuser MIMO broadcast channels with block diagonalization"; IEEE Trans. there is a block diagonalization (BD) described in R. Chen, R. W. Heath, and J. G. Andrews, "On Signal Processing, 2005," Transmit selection diversity for unitary precoded multiuser spatial multiplexing systems with linear receivers.

In DIDO systems, transport pre-coding is used to separate planned data streams into other users. The carrier frequency offset causes some problems associated with system implementation when the transmit antenna radio frequency chains do not share the same frequency reference. When this happens, each antenna effectively transmits at a slightly different carrier frequency. This results in each user experiencing additional interference and destroys the integrity of the DIDO precoder. Some solutions to this problem are presented below. In one embodiment of the present invention, the DIDO transmit antennas share a frequency reference over a wired, optical, or wireless network. In another embodiment of the solution, one or more users estimate the frequency offset differences (relative differences in offsets between antenna pairs) and send this information back to the transmitter. The transmitter then pre-corrects for the frequency offset prior to training and precoder estimated phases for DIDO. There is a problem with this embodiment when it is delayed in the feedback channel. The reason is that there may be residual phase errors produced by the correction process not described in the continuous channel estimation. To solve this problem, one additional embodiment uses a new frequency offset and phase estimator that can correct this problem by estimating the delay. Results based on both simulation and measured values performed by the DIDO-OFDM prototype are presented.

The frequency and phase offset compensation methods disclosed in this document can be sensitive to estimation errors due to noise in the receiver. Thus, one additional embodiment suggests methods for robust time and frequency offset estimation even under low SNR conditions.

There are various approaches for performing time and frequency offset estimation. Because of its sensitivity to synchronization errors, many such methods have been presented in detail for the OFDM waveforms.

Typically, the algorithms do not utilize the structure of the OFDM waveform, and thus they both contain both single carrier and multi-carrier waveforms. The algorithms described below are among the class of techniques that use known reference symbols, e.g., training data, to aid in synchronization. Most of these methods are extensions of Moose's frequency offset estimator (see, for example, PH Moose, "A Technique for Orthogonal Frequency Division Multiplexing Frequency Offset Correction," IEEE Trans. Commun., Vol. 42, No. 10, pp. 2908-2914, October 1994). Moose suggested using two repeated training signals and derived a frequency offset using the phase difference between both received signals. Moose's method can only correct for the partial frequency offset. The extension of the Moose method is proposed by Schmile and Cox (TM Schmidl and DC Cox, "Robust frequency and timing synchronization for OFDM ", IEEE Trans. Commun., Vol. 45, no. 12, pp. December 1997). Their major innovation was to use one periodic OFDM symbol according to the additional differential coded training symbols. The differential coding in the second symbol enables integer offset correction. Coulson, T. M. Schmidl and D.C. Cox "Robust frequency and timing synchronization for OFDM ", IEEE Trans. Commun., Vol. 45, no. 12, pp. 1613-1621, December 1997, and A. J. Coulson, "Maximum likelihood synchronization for OFDM using a pilot symbol: analysis ", IEEE j. Select. Areas Commun., Vol. 19, no. 12, pp. 2495-2503, December 2001; A. J. Coulson, "Maximum likelihood synchronization for OFDM using a pilot symbol: algorithms ", IEEE J. Select. Areas Commun., Vol. 19, no. 12, pp. 2486-2494, December 2001, algorithms and analysis provided detailed discussion. One key difference is that Coulson uses repeated maximum length sequences to provide good correlation properties. Also, because of the constant envelope characteristics in the time and frequency domain, he suggested using chirp signals. Coulson considered some real details but does not include integer estimates. Multiple repeat training signals are described in H. Minn, V. K. Bhargava and K. B. Letaief, " A robust timing and frequency synchronization for OFDM systems ", IEEE Trans. Wireless Commun., Vol. 2, no. 4, pp. 822-839, July 2003, but the structure of the training was not optimized. Shi and Serpedin show that the training structure has a certain optimal shape in terms of frame synchronization (K. Shi and E. Serpedin, "Coarse frame and carrier synchronization of OFDM systems: a new metric and comparison &quot;, IEEE Trans. Wireless Commun., Vol. 3, no. 4, pp. 1271-1284, July 2004). One embodiment of the present invention uses the Shi and Serpedin methods to perform frame synchronization and partial frequency offset estimation.

Many methods in the literature focus on frame synchronization and partial frequency offset correction. The integer offset correction is described in T. M. Schmidl and D. C. Cox, "Robust frequency and timing synchronization for OFDM ", IEEE Trans. Commun., Vol. 45, no. 12, pp. &Lt; / RTI &gt; 1613-1621, December 1997. For example, Morrelli et al., M. Morelli, A. N. D'Andrea, and U. Mengali, "Frequency ambiguity resolution in OFDM systems", IEEE Commun. Lett., Vol. 4, no. 4, pp. 134-136, "Robust frequency and timing synchronization for OFDM" by T. M. Schmidl and D. Cox in IEEE Trans. Commun., Vol. 45, no. 12, pp. 1613-1621, December 1997. Alternative methods using various preamble structures have been proposed by Morelli and Mengali (M. Morelli and U. Mengali, "An improved frequency offset estimator for OFDM applications", IEEE Commun. Lett., Vol. 3, pp. 75-77 , March 1999). This method uses the correlation between M iterative recognition training symbols as an element of M to increase the range of the partial frequency offset estimator. This is the best linear unbiased estimator, allowing large offsets (by proper design) but not providing good time synchronization.

System Description

One embodiment of the present invention uses pre-coding based on channel state information to cancel frequency and phase offsets in DIDO systems. See FIG. 11 and the associated description above for the description of this embodiment.

In an embodiment of the invention, each user uses a receiver with a frequency offset estimator / compensator. 45, in an embodiment of the present invention, the system including the receiver includes a plurality of RF units 4508, a corresponding plurality of A / D units 4510, a frequency offset estimator / Receiver 4512, and DIDO feedback generator units 4506. The DIDO feedback generator units 4506,

The RF units 4508 receive signals transmitted from the DIDO transmitter units, down-convert the signals to baseband, and provide the down-converted signals to the A / D units 4510. The A / D units 4510 then convert the signal from analog to digital and send it to the frequency offset estimator / compensator units 4512. The frequency offset estimator / compensator units 4512 estimate and compensate for the frequency offset as described herein, and then send a scrambled signal back to the OFDM units 4513. The OFDM units 4513 operate Fast Fourier Transform (FFT) to remove the cyclic prefix and report the signal in the frequency domain. During the training period, the OFDM units 4513 send an output to a channel estimation unit 4504 which calculates channel estimates in the frequency domain. Alternatively, the channel estimates may be computed in the time domain. During the data period, the OFDM units 4513 send the output to the DIDO receiver unit 4502 which demodulates / decodes the signal to obtain data. The fraud channel estimation unit 4504 sends the channel estimates to the DIDO feedback generator unit 4506 which can quantize the channel estimates and send them back to the transmitter via a feedback control channel as shown.

Description of an embodiment of an algorithm for a DIDO 2x2 scenario

Embodiments of the algorithm for frequency / phase offset compensation in DIDO systems are described below. The DIDO system model is initially described with or without frequency / phase offsets. For simplicity, a specific implementation of a DIDO 2 x 2 system is provided. However, the underlying principles of the present invention may be implemented on higher order DIDO systems.

DIDO System Model w / o Frequency and Phase Offset

The received signals of DIDO 2 占 2

로 쓸 수 있고, 제2 사용자에 대해서는

, And for the second user

로 쓸 수 있다.

Can be written as.

Where h _mn and w _mn are the channel and DIDO pre-coding weights between the m-th user and the n-th transmit antenna, respectively, and x _m is the transmission signal for user m. Note that h _mn and w _mn are not functions of t since we have assumed that the channel is constant over the period between training and data transmission.

In the presence of frequency and phase offset, the received signals are expressed as:

And

, m-번째 사용자에 대한

이며, f_Tn 및 f_Um은 각각 n-번째 전송 안테나와 m-번째 사용자에 대한 (오프셋에 의해 영향을 받는) 실제 반송 주파수들이다. 값 t_mm은 상기 채널 h_mm에 걸친 위상 오프셋을 발생시키는 임의 지연들(random delays)을 나타낸다. 도 46은 상기 DIDO 2×2 시스템 모델을 도시한다.Where T _s is the symbol period, and for the n-

, for the m-th user

, And f _Tn and f _im are the actual carrier frequencies (affected by the offset) for the n-th transmit antenna and the m-th user, respectively. T represents the value _mm any delayed (random delays) to generate the phase offset over the channel h _mm. 46 shows the DIDO 2 x 2 system model.

For the time being, we use the following definitions to denote the frequency offset between the m-th user and the n-th transmit antenna:

Description of an embodiment of the present invention

The method according to an embodiment of the present invention is shown in Fig. The method includes the following general steps (including sub-steps, as shown): training period 4701 for frequency offset estimation; Training period 4702 for channel estimation; Data transmission via DIDO pre-coding by compensation (4703). These steps are described in detail below.

(a) a training cycle for frequency offset estimation (4701)

During the first training period, the base station sends one or more training sequences to each of the users from each transmit antenna (4701a). As described herein, "users" are wireless client devices. The signal received by the m-th user is given by DIDO 2 x 2, as follows.

Here, p ₁ and p ₂ are the training sequences transmitted from the first and second antennas, respectively.

및

을 추정한다. 그 후, 이러한 값들로부터 상기 사용자는 다음과 같은 두 개의 전송 안테나들 간 주파수 오프셋을 계산한다.The m-th user may use a certain type of frequency offset estimator (e.g., convolution by the training sequence)

And

. From these values, the user then calculates the frequency offset between the two transmit antennas as follows.

Finally, the value of equation (7) is fed back to the base station (4701b).

및

을 추정할 수 있다. 대안적으로, 일 실시 예에서, 상기 동일한 훈련열이 두 개의 연속적인 타임 슬롯들을 거쳐 사용되고, 상기 사용자는 r로부터 상기 오프셋을 추정한다. 게다가, 식(7)의 오프셋의 추정을 향상시키기 위해, 상기에 기술된 동일한 계산들이 상기 DIDO 시스템들의 (상기 m-번째 사용자만이 아닌) 모든 사용자들에 대해 수행될 수 있고, 최종 추정은 모든 사용자들로부터 획득된 (가중된) 평균값일 수 있다. 하지만, 이러한 해결 방안은 더 많은 계산 시간과 피드백 양을 필요로 한다. 결국, 상기 주파수 오프셋 추정의 업데이트는 상기 주파수 오프셋이 오버 타임을 변경하는지만을 필요로 한다. 따라서, 상기 전송기에서 시계의 안정성에 따라, 상기 알고리즘의 이 단계(4701)는 피드백 오버헤드의 감소를 초래하여 장기적으로(이를 테면, 매 데이터 전송마다 수행하지는 않는)수행될 수 있다. P ₁ and p ₂ in equation (6) are designed to be orthogonal,

And

Can be estimated. Alternatively, in one embodiment, the same training sequence is used over two consecutive timeslots, and the user estimates the offset from r. Further, in order to improve the estimation of the offset of Eq. (7), the same calculations described above can be performed on all users of the DIDO systems (not only the m-th user) (Weighted) average value obtained from users. However, this solution requires more computation time and feedback amount. Eventually, the update of the frequency offset estimate requires only that the frequency offset change the overtime. Thus, depending on the stability of the clock in the transmitter, this step 4701 of the algorithm may be performed in the long term (e.g., not per every data transmission) resulting in a reduction of the feedback overhead.

(b) training period for channel estimation (4702)

During the second training period, the base station first obtains the frequency offset feedback from the m-th user or from a number of users with a value of Equation (7). The value of equation (7) is used to pre-compensate for the frequency offset on the transmitting side. Thereafter, the base station sends training data to all users for the channel estimation example (4702a).

In a DIDO 2 x 2 system, the signal received at the first user

로 주어지고, 상기 제2 사용자에서 수신된 신호가

And the signal received at the second user is given as

로 주어진다.

.

이고,

는 상기 기지국의 제1 및 제2 전송들 간 임의 또는 공지된 지연이다. 게다가, p₁ 및 p₂는 각각 주파수 오프셋 및 추정을 이해, 상기 제1 및 제2 안테나들로부터 전송된 훈련열이다. here,

ego,

Is any or a known delay between the first and second transmissions of the base station. In addition, p ₁ and p ₂ are the training sequences transmitted from the first and second antennas, respectively, to understand the frequency offset and estimation.

Precompensation is applied only to the second antennas in this embodiment.

If we expand equation (8), we get

Similarly, for the second user

이다. 여기서,

이다.

to be. here,

to be.

On the receiving side, the users compensate for the residual frequency offset by using the training sequence p ₁ and p ₂ . The users then estimate the vector channels through a training sequence (4702b).

In equation (12), this channel or channel state information (CSI) is supplied to the base station (4702b) which computes the DIDO precoder as described in the subsection below.

(c) DIDO pre-coding by pre-compensation (4703)

이 되도록 한다.The base station receives the channel state information (CSI) of Equation (12) from the users, calculates pre-coding weights (4703a) through block diagonalization (BD)

.

Here, the vector h ₁ is defined in equation (12) and w _m = [w _m1 , w _m2 ]. Note that the invention presented in this specification may be applied to any other DIDO pre-coding method other than BD. In addition, the base station pre-compensation for the phase offset estimated by the expression using the frequency offset and the second train transmission and the current transmission delay (△ t ₀₎ between the estimated by the (7) (4703a). As a result, the base station sends data to the users through the DIDO precoder.

After this transmission processing, the signal received by the user 1 is given by the following equation.

이다. 속성(13)을 사용하며, 우리는 다음 식을 얻는다.here,

to be. Using property (13), we obtain the following equation.

Similarly, for user 2 we also get:

And, expanding equation (16)

이다. here,

to be.

Eventually, the users calculate the residual frequency offset and the channel estimate to demodulate the data streams x ₁ [t] and x ₂ [t] (4703c).

Generalization to DIDO N × M

In this section, the previously described techniques are generalized to DIDO systems by N transmit antennas and M users.

i. Training cycle for frequency offset estimation

During the first training period, the signal received by the m-th user resulting from the training sequence transmitted from the N antennas is given by:

Where p _n is the training sequence transmitted from the n-th antenna.

을 추정한 후, 상기 m-번째 사용자는 상기 제1 및 n-번째 전송 안테나 간에 다음과 같은 주파수 오프셋을 계산한다. The offsets

The m-th user calculates the following frequency offset between the first and n-th transmit antennas.

Finally, the values of equation (19) are fed back to the base station.

ii. Training period for channel estimation

During the second training period, the base station first obtains frequency offset feedback from the m-th user or a number of users with a value of equation (19). The value of equation (19) is used to pre-compensate for the frequency offset feedback on the transmission side. The base station then sends training data to all users for channel estimation.

For DIDO N x N systems, the signal received at the m-th users is given by:

이고, △t는 상기 기지국의 제1 및 제2 전송들 간에 임의 또는 공지된 지연이다. 게다가, p_n은 주파수 오프셋 및 채널 추정을 위해 상기 n-번째 안테나로부터 전송된 훈련열이다.here,

, And [Delta] t is any or a known delay between the first and second transmissions of the base station. In addition, p _n is the training sequence transmitted from the n-th antenna for frequency offset and channel estimation.

On the receiving side, the users compensate for the residual frequency offset by using the training sequence p _n . Each user m then estimates the vector channel of the following equation through the training sequence,

To the base station that computes the DIDO precoder as described in the subsections below.

iii. DIDO pre-coding by precompensation

The base station receives the channel state information (CSI) of Equation (12) from the users and calculates pre-coding weights through a block diagonalization (BD) as follows.

Here, h _m is a vector defined in equation (21), wm = [w m1, w m2, ..., w mN]. In addition, the base station pre-compensate for the phase offset estimated by the frequency offset and the second train and transfer delay (△ t ₀₎ between the current transmission by the use of the estimate of equation (19). As a result, the base station sends data to the users through the DIDO precoder.

After this transfer process, the signal received at user i is given by the following equation.

이다. 속성(22)을 사용하면, 우리는 다음 식을 얻는다.here,

to be. Using property (22), we get the following equation.

Eventually, the users calculate the residual frequency offset and channel estimate to demodulate the streams x _i [t].

Results

Figure 48 shows SER results of DIDO 2x2 with or without frequency offset. The proposed method can be seen to completely erase the frequency / phase offsets that yield the same SER as the system without the offsets.

Next, we evaluate the sensitivity of the proposed compensation method to variations in frequency offset estimation errors and / or offsets at any point in time. Therefore, we rewrite Eq. (14) as

Where [epsilon] denotes a change in the estimation error and / or the frequency offset between the training sequence and the data transmission. Note that the effect of ∈ is to destroy the orthogonal property of Eq. (13) so that the interference conditions of Eqs. (14) and (16) are not completely erased in the transmitter. As a result, the SER performance is degraded to increase the value of?.

FIG. 48 shows SER performance indicating the frequency offset compensation method for various values of e. These results assume T _s = 0.3 ms (ie, a signal with a bandwidth of 3 KHz). We note that if ∈ = 0.001 Hz (or less), the SER performance is observed to be similar to the case without offset.

f. Description of an embodiment of an algorithm for time and frequency offset estimation

In the following, we describe additional embodiments for performing time and frequency offset estimation (step 4701b in FIG. 47). The transmission signal structure under consideration is described in H. Minn, VK Bhargava, and KB Letaief, " A robust timing and frequency synchronization for OFDM systems &quot;, IEEE Trans. Wireless Commun., Vol. 2, no. 4, pp. 822-839, July 2003, and K. Shi et al. &Quot; Coarse frame and carrier synchronization of OFDM systems: a new metric and comparison &quot;, IEEE Trans. Wireless Commun., Vol. 3, no. 4, pp. 1271-1284, July 2004. Generally, sequences with good correlation properties are used in training. For example, in our system, D. Chu, "Polyphase codes with good periodic correlation properties (corresp.), IEEE Trans. Inform. Theory, vol. 18, no. 4, pp. 531-532, 1972 7 These sequences have interesting properties in that they have perfect cyclic correlations: L _cp denotes the length of the cyclic prefix, and N _t denotes the length of the component training sequences Let N _t = M _t , where M _t is the length of the training sequence. Under this assumption, the transmission symbol sequence for the preamble can be written as:

s [n] = t [nN _t ] where n = -1, ..., -L _cp

s [n] = t [n] where n = 0, ..., N _t -1

s [n] = t [nN _t ] where n = N _t , ..., 2N _t -1

s [n] = -t [n-2N _t ] where n = 2N _t , ..., 3N _t -1

s [n] = t [n-3N _t ] where n = 3N _t , ..., 4N _t -1

Note that the structure of this training signal can be extended to other lengths, but the block structure is repeated. For example, to use 16 training signals, we consider the following structure:

B, B, B, B, B, B, B, B,

By using this structure and setting N _t = 4M _t , all of the algorithms to be described can be used without modification. Effectively, we are repeating the above training sequence. This is particularly useful when appropriate training signals may not be available.

After matching filtering and downsampling in sign rate, consider the following received signal:

Where [epsilon] is the unknown variance-time frequency offset, [Delta] is the unknown frame offset, h [l] is the unknown variance-time channel coefficient, and v [n] is the additive noise. In order to illustrate key ideas in the following sections, the presence of additional noise is ignored.

i. Course frame synchronization

The purpose of Coarse Frame Synchronization is to resolve the unknown frame offset?. We will make the following definitions.

The proposed course frame synchronization algorithm is based on K. Shi and E. Serpedin, "Coarse frame and carrier synchronization of OFDM systems: a new metric and comparison", IEEE Trans. Wireless Commun., Vol. 3, no. 4, pp. 1271-1284, July 2004.

Method 1 - Improved course frame synchronization: The course frame synchronization estimator solves the following optimization.

here,

Let the corrected signal be defined by the following equation.

An additional correction condition may be used to compensate for the small initial tap in the channel and may be adjusted based on the application. From this time on, this additional delay can be included in the channel.

ii. Partial frequency offset correction

The fractional frequency offset correction is followed by the course frame synchronization block.

Method 2 - Improved partial frequency offset correction : The partial frequency offset is a solution to the following equation.

This is known as the partial frequency offset because the algorithm can only correct for the offsets.

These issues will be addressed in the next section. The fine frequency offset correction signal is defined by the following equation.

과

둘 다의 사용이다.

의 사용은 그것이 기호간 간섭으로 인해 오염될 샘플들을 무시하기 때문에 종래 추정기를 향상시킨다. Methods 1 and 2 are described in K. Shi and E. Serpedin, "Coarse frame and carrier synchronization of OFDM systems: a new metric and comparison", IEEE Trans. Wireless Commun., Vol. 3, no. 4, pp. 1271-1284, July 2004, which are incorporated herein by reference. Here, certain innovations are described as described above.

and

It is the use of both.

&Lt; / RTI &gt; improves the conventional estimator because it ignores samples to be contaminated due to inter-symbol interference.

iii. Constant frequency offset correction

In order to correct the integer frequency offset, it is necessary to write an equivalent system model for the received signal after fine frequency offset correction. Upon absorption of residual time errors into the channel, in the absence of noise, the received signal has the following structure:

At this time, n = 0, 1, ..., 4N _t -1. The integer frequency offset is k, while the unknown equivalent channel is g [l].

Method 3 - Improved Integer Frequency Offset Correction: The integer frequency offset is a solution to the following equation.

here,

The estimation of the total frequency offset is given by the following equation.

는 사전 계산된다. 불행하게도, 이는 오히려 큰 매트릭스 곱으로 여전히 남는다. 상기 제시된 훈련열에 의해,

인 것으로 구성하는 것이 대안이다. 이는 하기의 복잡도 감소 방법으로 유도한다.In fact, Method 3 has rather high complexity. In order to reduce the complexity, the following observations can be made. First, the product S

Lt; / RTI &gt; Unfortunately, this still remains as a large matrix multiplication. By the training sequence presented above,

Is an alternative. This leads to the following complexity reduction method.

Method 4 - Low Complexity Improved Integer Frequency Offset Correction: The low complexity integer frequency offset estimator is solved by the following equation.

iv. Results

In this section, we compare the performance of various proposed estimators.

First, in FIG. 50, we compare the amount of overhead required for each method. Note that both the new methods reduce the overhead required by 10 to 20 times. To compare the performance of various estimators, Monte Carlo experiments were performed. The set-up considered corresponds to a passband bandwidth of 3 kHz and is a conventional NVIS transmit waveform consisting of raised cosine pulse shaping, 3K symbols per second, from linear modulation at a symbol rate. For each Monte Carlo realization, the frequency offset is generated from a uniform distribution on [-f _max , f _max ].

f _max = 2Hz and a simulation having a small frequency offset with no constant offset correction is shown in Figure 51. It can be seen from this performance comparison that performance by N _t / M _t = 1 is slightly reduced from the original estimator, but still substantially reduces the overhead. The performance by N _t / M _t = 4 is almost 10 dB, much better. All curves undergo knee at low SNR points due to errors in the target offset estimation. Small errors at integer offsets result in large frequency errors, and therefore large mean square errors. Integer offset correction can be turned off at small offsets to improve performance.

In the presence of multipath channels, the performance of frequency offset estimators generally degrades. However, the turning off of the integer offset estimator reveals a fairly good performance in FIG. Thus, it is even more important to perform robust course correction in addition to an improved fine correction algorithm in multi-path channels. Note that the offset performance by N _t / M _t = 4 is much better in the case of multipath.

Embodiments of the present invention may include various steps as described above. The steps may be implemented with machine-executable instructions that cause a general purpose or special purpose processor to perform certain steps. For example, the various components within the base station / APs and the client devices described above may be implemented in software performed on a general purpose or special purpose processor. Various known personal computer components, such as computer memory, hard drives, input devices, and the like, may be omitted from the drawings to avoid obscuring the appropriate aspects of the present invention.

Alternatively, in one embodiment, the various functional modules and associated steps illustrated herein may be implemented as computer-aided and computerized components, such as an application-specific integrated circuit ("ASIC"), And hard-wired logic for performing steps with a certain combination.

In one embodiment, certain modules, such as the encoding, modulation and signaling logic 903 described above, may be implemented as a DSP-like program using a TMS320x architecture from Texas Instruments Inc. (e.g., TMS320C6000, TMS320C5000, May be implemented on a digital signal processor ("DSP") (or a group of DSPs). In this embodiment, the DSP may be embedded in an add-on card for a personal computer, such as, for example, a PCI card. Of course, various other DSP architectures may be used as long as they still follow the underlying principles of the present invention.

Embodiments of the invention may also be provided in a machine-readable medium for storing the machine-executable instructions. The machine-readable medium may include, but is not limited to, flash memory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, Other suitable types of machine-readable media. For example, the present invention may be implemented in a computer system (e.g., a computer system) that is communicated from a remote computer (e.g., server) to a requesting computer (e.g., a client) by data signals embodied in a carrier wave or other propagation medium via a communication link Lt; / RTI &gt;

Throughout the foregoing description, for purposes of explanation, numerous specific details have been set forth in order to provide a thorough understanding of the present system and invention. It will be apparent, however, to those skilled in the art that the system and method may be practiced without some of these specific details. Accordingly, the scope and spirit of the present invention should be determined by the following claims.

In addition, throughout the foregoing description, numerous publications have been cited to provide a more thorough understanding of the present invention. All of these referenced references are incorporated herein by reference.

100: data input subsystem 101: separator
102: Coding and Modulation Subsystem 103: Transmitter
104: transmission antenna 105: reception antenna
106: RF receiver 107: signal processing subsystem
108: 3 × 3 matrix H 108: Data output unit
200, 300, 400, 500, 600, 700,
201, 301, 401, 501, 601, 701: WAN interface
202, 305, 405, 505, 605, 705, 802,
203-207, 804-808, 904-907: Client devices
302, 402, 502,
303, 403, 503, 603, 703, 311, 411, 511, 611, 711: encoding, modulation and signal processing subsystem
304,404,504,604,704,310,410,510,610,710: Transceiver
306-308, 406-408, 506-508, 606-608, 706-708: Data Interface
309, 409, 509, 609, 709: Client device antenna
1001: NVIS base station 1002: NIVS antenna
1004: Client devices 1010: Internet
1015: Link 1020: Remote Servers
1102,1902: User selector unit 1104,1904: Encoding module unit
1106,1906: Mapping unit 1108: DIDO IQ-aware pre-coding unit
1110: DIDO configuration unit 1112: User feedback unit
1114: RF transmitter unit 1116, 1916: D / A unit
1202: IQ-aware receiver unit 1204: IQ-aware channel estimator unit
1206, 1912: DIDO feedback generator unit 1208, 1914: RF unit
1210: A / D units 1213 and 1915: OFDM unit
1302: IQ-DIDO precoder 1304: Transmission channel
1306: Channel estimation logic 1308: ZF, MMSE or ML receiver
1908: DIDO pre-coding unit 1910: DIDO configuring unit

Claims (45)

1. A method for compensating for in-phase and quadrature (I / Q) imbalances in a multi-antenna system (MAS) transmission ("MU-MAS") with multiple users (MUs)
Transmitting training signals between each antenna of the base station and each of the plurality of wireless client devices and acquiring channel characteristic data at the base station;
Calculating a plurality of MU-MAS precoder weights based on the channel characteristic data, wherein the MU-MAS precoder weights are calculated to pre-cancel interference due to I / Q gain and phase imbalance or inter-user interference , A calculation step;
Precoding using the MU-MAS precoder weights to generate pre-coded data signals for each antenna of the base station; And
And transmitting the pre-encoded data signals to each client device via each antenna of the base station. The method according to claim 1,
Wherein the base station is an access point coupling the wireless client devices to a wide area network. The method according to claim 1,
The compensation method comprises:
Demodulating data streams at each user equipment using a zero-forcing (ZF), minimum mean square error (MMSE) or maximum likelihood (ML) receiver to suppress residual interference;
Lt; / RTI &gt; The method according to claim 1,
Wherein pre-coding is performed using block diagonalization (BD) techniques. The method according to claim 1,
Wherein the antennas of the MU-MAS are distributed antennas. The method of claim 5,
The precoder weights cancel inter-user interference but not inter-carrier interference (ICI)
Wherein the wireless client devices include receivers having filters to clear the ICI. A system for compensating for in-phase and quadrature (I / Q) imbalances for MU-MAS communications,
One or more coding modulation units for coding and modulating information bits for each of a plurality of wireless client devices to generate coded and modulated information bits;
One or more mapping units for mapping the encoded and modulated information bits to composite symbols; And
A MU-MAS pre-coding unit using channel state information obtained at a base station to calculate MU-MAS pre-coding weights,
Wherein the MU-MAS pre-coding unit precodes the composite symbols obtained from the mapping units using the weights to pre-cancel interference due to I / Q gain and phase imbalance or inter-user interference. The method of claim 7,
The compensation system comprises:
One or more orthogonal frequency division multiplexing (OFDM) units for receiving the pre-coded signals from the MU-MAS pre-coding unit and for modulating the pre-coded signals according to an OFDM standard;
&Lt; / RTI &gt; The method of claim 8,
Wherein the OFDM standard comprises calculating an inverse fast Fourier transform (IFFT) and adding a cyclic prefix. The method of claim 9,
The compensation system comprises:
One or more D / A units in which the OFDM units perform digital to analog (D / A) conversion on the output to produce an analog baseband signal; And
One or more radio frequency (RF) units that upconvert the baseband signal to a radio frequency and transmit the signals using one or more corresponding transmit antennas;
&Lt; / RTI &gt; The method of claim 7,
Wherein the MU-MAS pre-coding unit is implemented with a minimum mean square error (MMSE), a weighted MMSE, a zero-forcing (ZF) precoder, or a block diagonalization (BD) precoder. delete The method of claim 7,
The antennas of the MU-MAS include distributed antennas,
The compensation system comprising a pre-coding unit of a distributed antenna using channel state information to calculate pre-coding weights,
Wherein the pre-coding unit of the distributed antenna pre-encodes the composite symbols obtained from the mapping units using the weighting factors to pre-cancel the interference due to I / Q gain and phase imbalance or inter-user interference Compensation system. A wireless client device for use in a system compensating for in-phase and quadrature (I / Q) imbalances for MU-MAS communications,
One or more RF units for receiving signals transmitted from one or more MU-MAS transmitter units and down-converting the signals to a baseband;
One or more analog-to-digital (A / D) conversion units for receiving the down-converted signals and converting the signals from analog signals to digital signals;
One or more OFDM units for removing the cyclic prefix for the digital signals and performing a fast Fourier transform (FFT) to report the signals in the frequency domain;
A channel estimation unit for receiving signals output from the one or more OFDM units during a training period and responsively calculating channel estimation data; And
A signal generator unit for transmitting the channel estimation data or training signal to the base station for use of the pre-coded signals prior to transmission to the wireless client device;
The wireless client device comprising: 15. The method of claim 14,
Wherein the channel estimates are computed in the time domain by using an input to the OFDM units. 15. The method of claim 14,
Wherein the signal generator unit further comprises logic for quantizing the channel estimates prior to transmission to the base station. 15. The method of claim 14,
The wireless client device comprising:
A receiver unit for receiving outputs from the OFDM units and responsively calculating an IQ imbalance and demodulating / decoding the signals to obtain an estimate of the transmitted data;
Lt; RTI ID = 0.0 &gt; 1, &lt; / RTI &gt; 18. The method of claim 17,
Wherein the receiver unit is a minimum mean square error (MMSE) receiver, a zero-forcing (ZF) receiver, a maximum likelihood (ML) or a map (MAP) receiver. 18. The method of claim 17,
Wherein the receiver unit comprises an MMSE or ZF filter that cancels inter-carrier interference (ICI) caused by I / Q imbalance for the mirror tones. 18. The method of claim 17,
Characterized in that the receiver unit comprises a non-linear detector (ML) for jointly detecting symbols for the mirror tones. 18. The method of claim 17,
Wherein the channel estimation unit is operable to calculate available coefficients by the receiver unit to remove inter-carrier interference (ICI). 14. The method of claim 13,
The antennas of the MU-MAS include distributed antennas,
Wherein the one or more RF units receive signals transmitted from the MU-MAS transmitter units of the one or more distributed antennas and downconvert the signals to baseband. delete delete delete delete delete delete delete delete delete delete delete delete delete delete delete delete delete delete delete delete delete delete delete

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