JP2010528503A - Adaptive soft output M algorithm receiver structure - Google Patents

Abstract

According to the present invention, a device used in a wireless communication system includes a transmitter and a receiver that receives an information-carrying signal transmitted wirelessly from the transmitter using OFDM and bit interleaved coded modulation. An inner decoder having a SOMA-based MIMO joint demapper that uses a soft output M-algorithm (SOMA) based multiple-in multiple-out (MIMO) detection process to perform joint inner demapping for each subtone Provide structure. The SOMA-based MIMO joint demapper identifies the best candidate among multiple candidates by searching the detection tree under the control of a parameter representing the total number of paths extended from each level, and from all levels of the tree Only the best alternatives are deployed and the SOMA-based MEvIO detection process adapts one or more of the parameters based on tone quality.
[Selection] Figure 1

Description

priority

  [0001] This application is related to US Provisional Patent Application No. 60/930805 filed May 18, 2007, entitled "Adaptive Soft Output M-algorithm Receiver Structures for MIMO / OFDM / QAM Systems with IDM. , And incorporates by reference.

  [0002] The present invention relates to the field of wireless communications, and more specifically, the invention relates to an adaptive soft output M-algorithm receiver.

  [0003] Future wireless systems will require more efficient use of the radio frequency spectrum in order to increase the data rate achievable within a given transmission bandwidth. This can be achieved by using multiple transmit and receive antennas combined with signal processing. Several recently developed techniques and emerging standards have made it possible to use multiple antennas at the base station to improve the reliability of data communication over the wireless medium without compromising the effective data rate of the wireless system. Based. A so-called space-time block code (STBC) is used for this purpose.

  [0004] Specifically, recent advances in wireless communications have made it possible for the base station to encode symbols together across time and transmit antennas, thereby improving reliability (diversity) benefits and each cellular user from the base station. It has been demonstrated that an increase in effective data rate per unit of bandwidth can be obtained. These multiplexing (throughput) gains and diversity benefits depend on the space-time coding technique used at the base station. Multiplexing gain and diversity benefits are essentially limited by the multiplexing-diversity trade-off curve defined by the number of transmit antennas and receive antennas in the system, in the sense that transmission in deployed systems It also essentially depends on the number of antennas and receiving antennas.

  [0005] For high data rates and wideband transmission, the use of OFDM makes the equalizer unnecessary. With a multilevel modem, a coded modulation system can be separated by an interleaver in an outer binary code such as a convolutional code and a so-called bit-interleaved coded modulation (BICM) system. It can be designed easily.

  [0006] In many emerging and future wireless networks, any particular cell user's data can be made available from multiple base stations. Joint signaling from multiple base stations can easily extend the range / coverage of the transmission. Furthermore, since each base station having data for a particular user is considered as an element of the virtual antenna array (or group of elements if multiple transmit antennas are present at each base station), diversity can be provided to the desired user. It is proposed to use a cooperative signal coding scheme across these base stations to provide benefits. However, since the encoded signals are transmitted by spatially scattered base stations, they arrive at the receiver with separate relative delays relative to each other, ie asynchronously. These relative delays can in principle be estimated at the receiver, but are unknown at the transmitting base station (and therefore adjust for it) without relative delay information feedback from the receiver to the transmitting base station. Can't).

  [0007] A large population of STBCs has recently been proposed as a means of providing diversity and / or multiplexing benefits by utilizing multiple transmit antennas on the forward link of a cellular system. What is important is the actual symbol rate R of the STBC scheme, which is equal to k / t (ie, the ratio of k to t). A full rate STBC is an STBC whose rate R is equal to channel usage per symbol. Another important attribute of STBC is its decoding complexity. The decoding complexity of an optimal decoder for any STBC is exponential in the number of symbols k that are encoded together, but there are designs with much lower complexity. One such attractive class of designs, called orthogonal space-time code (OSTBC), can provide full diversity, but its optimal decoding is per symbol (linear processing and subsequent) It is separated into decryption. Full rate OSTBC exists only for the two transmit antenna system. For more than two antennas, the rate cannot exceed 3/4 symbols / per channel usage. As a result, the enforced orthogonality constraint results in a simple decoding structure, but imposes constraints on the multiplexing gain (and thus spectral efficiency and throughput) that can be provided by such a scheme.

  [0008] Many MIMO / OFDM systems take advantage of the large QAM constellation and BICM / ID and have an optimal inner MIMO detector block with high complexity.

  [0009] Multiple systems deployed to broadcast common audio / video information from multiple base stations utilize coded OFDM transmission under a single frequency network concept. These systems use a common coded OFDM based transmission from each of the broadcasting base stations. OFMD-based transmission allows asynchronous reception of multiple signals, resulting in increased coverage. However, since all base stations transmit the same encoded version of the information bearing signal, SFN (single frequency network) systems generally do not provide full transmit base station diversity with full coding gain. (This form of diversity is limited because it has not been adjusted, but can be used in the form of multipath diversity). The scheme with inner modified orthogonal STBC provides OFDM-based benefits for single frequency networks while simultaneously using separate coordinated transmissions from separate base stations with bit interleaved coded modulation. Can be viewed as a method that enables full transmit base station diversity and frequency diversity harvested from

[0010] A class of schemes that can achieve high spectral efficiency and reliable transmission includes space-time bit interleaved coded modulation systems using OFDM. These systems can provide spatial (transmit and receive antenna) diversity, frequency diversity, and can handle asynchronous transmission. Furthermore, a flexible UEP system can be achieved by changing the binary convolutional code to a block with a rate compatible punctured convolutional code. One disadvantage associated with such systems is that suboptimal receivers can be very complex (computation intensive). The required joint demapper unit (inner MAP decoder or MaxLogMAP decoder) increases in complexity with the product of the number of transmit antennas and the number of bits per modem constellation point. As an example with 16 QAM (4 bits / symbol) and 4 transmit antennas, the computational complexity at the inner decoder is proportional to 2 ^{4 × 4} = 2 ¹⁶ .

  [0011] It is well known that a gray mapper for a QAM constellation is a good choice for a non-iterative decoder, but not a good choice for an iterative decoder.

  [0012] A method and apparatus for an adaptive soft output M-algorithm receiver structure is disclosed. In one embodiment, a device used in a wireless communication system includes a transmitter and includes a receiver that receives an information-carrying signal transmitted wirelessly from the transmitter using OFDM and bit interleaved coded modulation. , The receiver has an SOMA-based MIMO joint demapper that uses a soft output M-algorithm (SOMA) based multiple-in multiple-out (MIMO) detection process to perform joint inner demapping for each subtone It has a structure. The SOMA based MIMO joint demapper is operable to identify the best candidate among multiple candidates by searching the detection tree under control of a parameter representing the total number of paths extended from each level. Only the best alternatives are developed from all levels of the tree, and the SOMA based MIMO detection process adapts one or more of the parameters based on tone quality.

  [0013] The present invention will be more fully understood from the detailed description given below and the accompanying drawings of various embodiments of the invention, which will be described in conjunction with the specific embodiments. It should not be construed as limiting, but only for explanation and understanding.

4 is a flow diagram illustrating one embodiment of a decryption process. FIG. 2 is a block diagram illustrating one embodiment of a transmitter for space-time coding using bit interleaved coded modulation (BICM) with OFDM modulation for a wideband frequency selective channel. FIG. 2 is a block diagram illustrating one embodiment of a receiver having an iterative decoder for space-time codes for an OFDM system. FIG. 3 is a block diagram illustrating one embodiment of a MIMO demapper 305 having MIMO joint demapper units for different OFDM tones / subchannels. FIG. 3 is a diagram illustrating an embodiment of a set partition type mapper. It is a figure which shows application of H matrix and a weight with respect to two signals. FIG. 4 shows another representation of the receiver of FIG. 3 where each MIMO demapper for each subtone is shown. FIG. 5 shows a decision tree that allows recursive calculation of metrics on the tree when there are three transmit antennas. It is a figure which shows the example of a decision tree. 6 is a flow diagram illustrating one embodiment of a process for setting up a SOMA inner decoding operation for a tone. 3 is a flow diagram illustrating a SOMA detection process at a specific depth. It is a figure which shows QR decomposition | disassembly.

  [0026] Embodiments of the present invention generally relate to an adaptive receiver structure for receiving digital information via a wireless system having multiple transmit antennas and multiple receive antennas. Embodiments of the present invention address a flexible and efficient MIMO joint demapper based on an improved version of the soft output M algorithm.

  [0027] Such an adaptive receiver structure can be used in a wireless communication environment where a mobile receives signals transmitted via multiple transmit antennas (by using one or more antennas) Antennas can be distributed across multiple base stations (ie, not co-located). In one such system, the transmit antennas are placed together in the same base station.

  [0028] Embodiments of the present invention provide transmit base station diversity, frequency diversity available within transmission bandwidth, receive antenna diversity when multiple receive antennas are used, and extended coverage. For example, a reduced system complexity receiver that utilizes intelligent wideband transmission of information-carrying signals over multiple independently fading paths from each transmitting base station to the receiver. Embodiments of the present invention are applicable to systems that can use information-carrying signals at multiple base stations and settings that include a single active base station with multiple transmit antennas. In one embodiment, a single base station with multiple transmit antennas is used for transmission as well as OFDM-based BICM systems.

  [0029] Embodiments of the present invention apply to MIMO / OFDM based systems that use bit interleaved coded modulation (BICM) with iterative decoding (ID). These systems can provide full spatial diversity when another (outer) code with a sufficiently low coding rate is used. If high rate codes are used, there is a reduction in the degree of spatial diversity. In one embodiment, OFDM based wideband transmission and bit interleaved coded modulation with outer binary codes are used. Orthogonal frequency division multiplexing OFDM is used to achieve a flexible wideband system. Bit interleaved coded modulation BICM (at the transmitter) with iterative decoding ID (at the receiver) is used for efficiency. An inner joint demapper is used adaptively based on the quality of the OFDM tone. This system can be used with or without an inner orthogonal space-time block code.

  [0030] The present invention is applicable to both space-time coding schemes for systems having base stations at the same location and systems having base stations at non-co-locations.

  [0031] In the following description, numerous details are set forth to provide a more thorough explanation 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, rather than in detail, in order to avoid obscuring the present invention.

  [0032] Some portions of the following detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by engineers in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is considered herein and generally a self-contained sequence of steps that leads to a desired result. A step is a step that requires physical manipulation of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

  [0033] However, it should be noted that these and other items must be associated with appropriate physical dosages and are merely convenient labels applied to these quantities. Unless otherwise stated explicitly from the following discussion, terms such as “processing”, “computing”, “calculation”, “decision”, “display”, or the like are used throughout this description. The discussion to manipulate data represented as physical (electronic) quantities in computer system registers and memory, computer system memory or registers, or other such information storage devices, information transmission devices, or information It should be understood that it refers to actions and processes of a computer system or similar electronic computing device that translates into other data that is also represented as a physical quantity in the display device.

  [0034] The present invention also relates to an apparatus for performing the operations herein. The apparatus may be specially configured for the required purposes, or may include a general purpose computer selectively activated or reconfigured by a computer program stored on the computer. Such computer programs can be any type of disk, including floppy disks, optical disks, CD-ROMs, and magneto-optical disks, read only memory (ROM), random access memory (RAM), EPROM, EEPROM, magnetic card or optical Each may be stored in a computer readable storage medium coupled to a computer system bus, such as but not limited to a card, or any type of medium suitable for storing electronic instructions.

  [0035] The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the necessary method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It should be understood that various programming languages may be used to implement the teachings of the invention described herein.

  [0036] A machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (eg, a computer). For example, a machine-readable medium may be read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage medium, optical storage medium, flash memory device, electrical, optical, acoustic, or other form of propagation. Signal to be transmitted (eg, carrier wave, infrared signal, digital signal, etc.).

Overview
[0037] Wireless communication system A first device having a transmitter (eg, a base station) and reception for receiving information bearing signals from a wirelessly transmitted transmitter using OFDM and bit interleaved coded modulation A second device (eg, a mobile terminal) having a device. In one embodiment, the communication system described herein includes a transmitter that applies space-time coding using bit interleaved coded modulation combined with multi-carrier OFDM modulation, and OFDM that uses iterative demapping and decoding. A coded modulation system including a receiver that applies demodulation. The system described herein has N _t transmit antennas and N _r receive antennas. Each N _r receive antennas receives a which is the sum signal of the channel distortion version of the transmitted signals from the N _t transmit antennas. Such a coded modulation system according to the present invention can be advantageously used in wireless local area / wide area network (LAN / WAN) applications.

  [0038] Although exemplary embodiments are described with respect to space-time coding with bit-interleaved coded modulation, other types of coded modulation with respect to space-time coding can be used. Further, exemplary embodiments describe mapping of bit interleaved encoded data to symbols using QAM, but other modulation schemes such as but not limited to phase shift keying (PSK), etc. Can be used.

[0039] In general, the receiver includes circuitry that estimates the values of the elements of the channel response matrix H [k], such estimates being transmitted by the transmitter to the receiver in a periodic test (pilot). It can be generated using a signal. Such a priori information of the channel impulse response can also be generated via simulation. A matrix H [k] represents a channel response in the kth OFDM tone, and is a matrix of dimension N _r × N _t .

  [0040] When combined with signal processing, multiple transmit and receive antennas provide a communication link with increased bandwidth efficiency (data rate), increased power efficiency (range), or both Can do. Embodiments of the present invention primarily deal with the forward link or transmission direction from the base station to the mobile. A method and apparatus for an adaptive soft output M algorithm based receiver structure is disclosed.

[0041] In one embodiment, the reduced complexity soft output MIMO detector in the receiver adaptively utilizes a modified soft output M algorithm (SOMA). In one embodiment, the soft output MIMO detector is applied for every tone or subchannel in the OFDM system, as well as at every iteration in the decoding process. To illustrate the benefits of the SOMA demapper, consider an optimal MIMO detector called a maximum posterior probability (MAP) detector. The MAP performs a joint demapping function for all transmit antennas and for all used QAM constellation symbols and bits. Often an exhaustive MaxLogMAP detection algorithm that is asymptotically optimal but much simpler is used. However, even a simpler MaxLogMAP detector requires an exhaustive demapping operation, which is accompanied by the product of the number of transmit antennas (N _t ) and the number of bits per QAM constellation point (B). With an exponentially increasing search space. For example, for a 6 × 6 MIMO system (6 transmit antennas and 6 receive antennas) using 64 QAM modulation (6 bits per constellation point), this product is 36. In that case, the decoding complexity has an order of 2 ³⁶ and MaxLogMAP cannot be implemented using current technology. In contrast, in one embodiment, the SOMA detector uses only a fraction of the total number of candidates in its MIMO detection process, and thus there is a significant complexity reduction. Of course, there is a trade-off between performance and the degree of complexity reduction. In one embodiment, the amount of computation allocated to each SOMA module (one per OFDM tone) determines the channel conditions (for a given OFDM tone) to optimize the overall complexity-performance tradeoff of the receiver. SOMA is used adaptively in that it is adapted to

  [0042] During all inner / outer decoder iterations, the SOMA detector performs a SOMA detection process for each OFDM tone. In one embodiment, the number of candidates explored in the SOMA detection process is controlled by a parameter (M) that indicates the number of paths extended from each node or level in the detection tree. Specifically, at every given level in the detection tree, only the subset M of candidates that are visited will be saved as survivors and extended at the next level. The remaining candidates to be tested at this level are referred to as early-terminated paths. The early terminated path is used by SOMA to perform soft output calculations. In one embodiment, the number of early terminated paths explored in the SOMA detection process is also an adaptive parameter because these paths also play a role in soft output calculations. In this specification, this value is denoted as T and is used by the algorithm to calculate the soft output value. In the overall detection process, the number of inner / outer decoder iterations I also affects the total decoding complexity and associated performance.

  [0043] In one embodiment, the parameters M and T and / or I are adaptively selected for the best overall performance for a given total complexity level, and the amount guiding flexibility is different OFDM tones. Of quality. For example, a high signal level for a tone or alternatively a high signal-to-noise ratio (SNR) means a good quality level for that tone. In that case, the SOMA detection process performs decoding using a lower value of M, a lower value of T, and a potentially lower value of I. On the other hand, for bad quality tones, i.e., tones with low signal levels or low SNR, the SOMA detection process performs decoding with larger values of M, T, and I for the best use of total complexity. Execute. The adaptability can also be extended over time, i.e. across consecutive OFDM symbols.

  [0044] In one embodiment, the space-time coding system described herein includes OFDM for wideband transmission, MIMO for high spectral efficiency and large QAM constellation, bit interleaved coded modulation scheme (BICM). Bit interleaver for), as well as outer binary code. The overall detection is usually performed iteratively. This requires that both the inner MIMO demapper and the outer decoder perform soft-in-soft-out (SISO) detection / decoding. One system component that contributes to complexity is typically a joint demapper as described above. The outer code is less critical with respect to complexity. In one embodiment, the MIMO detector in principle processes all binary outer codes. This code may be a turbo code, an LDPC code, a normal convolutional code, or an RCPC code. The decoder for the outer code is preferably a soft-in-soft-out (SISO) type decoder, for example a MAP decoder. The outer decoder provides soft information to the inner MIMO detector for iterative decoding.

  [0045] FIG. 1 is a flow diagram of one embodiment of a decryption process. This process may be performed by processing logic that may include hardware (eg, dedicated logic, circuitry, etc.), software (such as that running on a general purpose computer system or a dedicated machine), or a combination of both. . In one embodiment, the decoding process is performed by a receiver in the wireless communication system.

  [0046] Referring to FIG. 1, the process begins by processing logic evaluating the quality of individual OFDM tones received by a receiver in a wireless communication system (processing block 101). The quality of individual OFDM tones / subchannels is evaluated / estimated at the receiver. In one embodiment, the quality of the OFDM tone is based on the signal level. In another embodiment, the quality of the OFDM tone is based on the signal to noise ratio (SNR).

  [0047] After evaluating the quality of the OFDM tones, processing logic performs a maximum likelihood determination of the transmitted bits, including performing a SOMA-based MIMO detection process for each subtone for disjoint inner demapping. Performing a first decoding operation to produce a first set of output data representing the values and confidence values of these decisions, wherein the best candidates are a predetermined number of best alternatives from all levels of the tree Are identified among a plurality of candidates by searching the detection tree under the control of a parameter representing the total number of paths extended from each level (processing block 102). In one embodiment, decoding is performed at all levels by calculating a metric difference between the estimated best path at that level and each of all or a subset of that level's early-terminated paths. Calculating the output value, whereby each such metric difference is used to update the bit position where the two paths (based on which the metric difference is calculated) do not match in the value. In one embodiment, a soft-in soft-out (SISO) outer decoder uses the soft output values from the inner SOMA-based MIMO joint demapper to produce output data and inner decodes the soft values for iterative decoding. Supply back to the container structure. In another embodiment, the soft input hard output Viterbi decoder uses the soft output values from the inner SOMA based MIMO joint demapper to produce hard output data for non-iterative decoding. Note that in that case, a simpler outer decoder is used to produce a hard output.

  [0048] In one embodiment, the SOMA based MIMO detection process is adapted based on the number of early terminated paths (T) in the tree, where T is used in the soft output value calculation. In another embodiment, the SOMA-based MIMO detection process is adapted based on the number of repetitions of each tone based on tone quality. In another embodiment, the SOMA-based MIMO detection process is adapted during each iteration and for all tones based on tone quality. In another embodiment, the SOMA-based MIMO detection process adapts one or more parameters, the number of early terminated paths in the tree, and the total number of iterations based on tone quality.

  [0049] After performing the first decoding operation, processing logic performs a second decoding operation using the binary outer coder (processing block 103). In one embodiment, the outer decoder includes a MAP decoder for the associated convolutional code that is used as the outer encoder in the transmission system. The outer decoder is a conventional optimal or sub-optimal decoding for such binary codes when rate compatible punctured convolution (RCPC) codes, turbo codes, and LDPC codes are used as outer encoders in the transmission system. Can be included.

Transmitter and receiver embodiments
[0050] FIGS. 2 and 3 show block diagrams of transmitters and receivers of a MIMO / OFDM system using BICM and ID. More specifically, FIG. 2 is a block diagram of one embodiment of a transmitter for space-time coding using bit interleaved coded modulation (BICM) with OFDM modulation for wideband frequency selective channels. Referring to FIG. 2, the transmitter 200 includes a convolutional encoder 201, a bit interleaver 202, a serial-to-parallel converter 203, mapper modems 207 _{1 to} 207 _Nt , inverse fast Fourier transform (IFFT) modules 208 _{1 to} 208 _Nt , and The transmission antennas 209 _{1 to} 209 _Nt are included. Note that IFFT modules 208 ₁ -208 _Nt also include cyclic prefix operations that are performed in a manner well known in the art.

  [0051] To perform BICM encoding on data, convolutional coder 201 applies a binary convolutional code to input bits (input data) 210. Next, the bit interleaver 202 interleaves the encoded bits from the convolutional coder 201 to generate bit interleaved encoded bits. This bit interleaving removes the fading channel correlation, maximizes diversity, removes the correlation in the sequence of convolutionally coded bits from the convolutional coder 201, and conditions the data to enhance the performance of iterative decoding. Convolutional coder 201 and bit interleaver 202 can typically operate on separate blocks of input data, such as data packets.

[0052] After performing bit interleaving, bit mapping, modulation, and OFDM are applied to the bit interleaved coded bits. The serial to parallel converter 203 receives a serial bit interleaved encoded bit stream from the bit interleaver 202. It is noted that the serial to parallel converter 203 can include a framing module (not shown) that inserts framing information into the bitstream that enables the receiver to synchronize its decoding for separate blocks of information. I want. Serial to parallel converter 203 generates a word of length _{N t} length, each element of the word is supplied to a corresponding one of mapper modems ₂₀₇ 1 to 207 _Nt. The elements of this word can be single bit values or B bit values, where B is the number of bits represented by each modem constellation symbol.

[0053] Each mapper modems ₂₀₇ 1 to 207 _Nt, converts the B bits to corresponding symbols (the Q-ary symbol space is Q = ^{2 B).} The output of each modem mapper 207 is a symbol. Each of the IFFT modules 208 ₁ -208 _Nt collects up to F symbols and then applies an IFFT operation of length F to the block of F symbols. F is an integer whose value can be in the range of as little as 64 to 4096, or is larger and needs to be addressed by the available transmission bandwidth, carrier frequency, and system Depends on the amount of Doppler shift. Accordingly, each of IFFT modules 208 _{1 to} 208 _Nt generates F parallel subchannels that can be transmitted via the corresponding transmit antennas 209 _{1 to} 209 _Nt . Each subchannel is a modulated subcarrier transmitted on the channel.

すなわちＢビットのうちのｌ番目を介して送信されるＱＡＭシンボルは、ｓ_ｋ［ｎ］を作るためにマッパモデム２０７_１〜２０７_Ｎｔの１つで入力として使用される。

であるものとすると、
ｓ_ｋ［ｎ］＝ｍａｐ（ｂ_ｋ［ｎ］） （１）
であり、ここで、ｍａｐは、マッパ動作を表す。 [0054] In an embodiment, the transmitter and receiver have an equal number of transmit and receive antennas, ie, N _t = N _T = N. This binary information bearing signal, denoted u _k by, first, at the transmitter, by an outer binary codes are encoded using a convolutional coder 201, coding sequence c _k is generated. This sequence is interleaved by a pseudo random bit interleaver 202. Each of the mapper modems 207 ₁ -207 _Nt then maps a group of B interleaved bits to 2 ^B QAM symbols at a time. The resulting QAM symbols are multiplexed via N transmit antennas 209 _{1 to} 209 _Nt in round robin form, and OFDM transmission is applied on each antenna using IFFT modules 208 _{1 to} 208 _Nt. . For convenience, herein, s _k [n], ie on tone n by antenna k,

That is, the QAM symbol transmitted via the lth of the B bits is used as an input by one of the mapper modems 207 _{1 to} 207 _Nt to create s _k [n].

Assuming that
s _k [n] = map (b _k [n]) (1)
Where map represents the mapper operation.

[0055] FIG. 3 is a block diagram of one embodiment of a receiver having an iterative decoder for a space-time code for an OFDM system. Referring to FIG. 3, the receiver 300 includes receiving antennas 301 _{1 to} 301 _Nr , fast Fourier transform (FFT) modules 302 _{1 to} 302 _Nr , a demodulator / detector 303, a parallel / serial converter 307, and a bit deinterleaver 308. , A maximum a posteriori (MAP) decoder 309, a bit interleaver 310, and a serial to parallel converter 311. Although not shown, each of the FFT modules 302 ₁ -302 _Nr is preceded by a front end that performs filtering, band rate sampling, and cyclic prefix removal operations.

[0056] For wideband systems, the receiver 300 performs OFDM demodulation for each of the receive antennas 301 _1-Nr , and demodulation and demapping are performed across F parallel subchannels. The i-th receive antenna 301 (i) receives various contributions of signals transmitted from N _t transmit antennas (ie, multiple F parallels transmitted via corresponding antennas 209 ₁ to 209 _Nt in FIG. 2). , Narrowband, flat fading subchannel contribution). Each of the FFT modules 302 _{1 to} 302 _Nr applies F-point FFT to the corresponding signals of the receiving antennas 301 _{1 to} 301 _Nr to generate N _r parallel sets of F subchannels.

[0057] In one embodiment, the demodulator / detector 303 is not only within one subchannel, as in prior art narrowband flat fading systems, but with F subchannels (slowly changing with flat fading). )) To estimate the bit. Demodulator 304 demodulates F subchannel carriers to baseband for each of N _r parallel sets of F subchannels. A multi-input multi-output (MIMO) demapper 305 is based on N _r parallel sets of F sub-channels from FFT modules 302 ₁ -302 _Nr , and F F from N _t antennas in the transmitter. Make a MAP estimate of each demapped bit (ie, mapped from the constellation symbol) of each of the subchannels. The MIMO demapper 305 uses the reliability information generated by the soft output decoding (followed by reinterleaving) by the MAP decoder 309 to produce demapped bit estimates and reliability information about these bits. .

  [0058] In one embodiment, the MIMO demapper 305 calculates the soft value of the bits transmitted on the F subchannels that overlap, along with an estimate (approximation) of the posterior probability that the soft value is correct. . This is performed in a manner well known in the art.

[0060] FIG. 4 is a block diagram of one embodiment of a MIMO demapper 305 having MIMO joint demapper units for different OFDM tones / subchannels. Referring to Figure 4, each signal _{N r} receive antennas ₃₀₁ 1 to 301 _Nr is divided into F subchannels by applying FFT (by a demodulator 304, not shown in FIG. 4) , To corresponding subchannel MIMO demappers 401 _{1 to} 401 _F. Signal outputs of the k sub-channels of all N _r receive antennas is supplied to the k subchannel MIMO demapper 401 (k), reliability information is generated from the output of the MAP decoder 309 in the previous iteration Use external information. This external information is exchanged between the MIMO demapper 305 and the MAP decoder 309 to improve the bit error rate performance of each iteration. Methods for calculating extrinsic information with such inner / outer decoder settings are well known in the art. In the first iteration, no external information is input to the soft demapper. In subsequent iterations, in one embodiment, the external information is calculated as follows: First, the soft output is calculated by the MAP outer decoder, from which the input reliability information (input to the same outer decoder) is subtracted to calculate the external information produced by the MAP decoder 309. . This external information is deinterleaved and passed as input to the MIMO demapper 305 in the next iteration.

  [0061] Returning to FIG. 3, the estimated values of the bits in the F parallel streams from MIMO demapper 305 together with the reliability values of these bits, along with the external reliability information for each of these bits. Is supplied to the parallel-serial converter 307. Reliability information is calculated as the difference between the output reliability value of these bits (made by demapper 305) and the input reliability value of these bits (input to demapper 305). The converter 307 reconstructs the estimated value of the BICM encoded bitstream generated by the transmitter, which was estimated by the receiver 300. The estimated BICM encoded bitstream (and external reliability information) is then deinterleaved by the bit deinterleaver 308 and applied to the MAP decoder 309 to reverse the convolutional encoding applied by the transmitter. The The inverse operation in this case generates an estimate of the bit value of the information bitstream that is the input to the convolutional coder 201, and is passed back to the MIMO demapper 303 as new reliability information (after reinterleaving). It also supports creating external information.

  [0062] The MAP decoding process generates a soft output value of the transmitted information bits in a manner well known in the art.

[0063] External information from the MAP decoder 309 is first applied to the bit interleaver 310. Bit interleaving aligns the elements of external information into the interleaved estimated BICM encoded bitstream from MIMO demapper 305. Further, the interleaved external information is applied to the serial-to-parallel converter 311. The serial-to-parallel converter 311 forms N _t parallel streams of external information corresponding to the parallel bit stream formed at the transmitter.

[0064] External information is exchanged between the MIMO demapper 305 and the MAP decoder 309 to improve the bit error rate performance of each iteration. In one embodiment, a MaxLogMAP type approximation is used to calculate the bit LLR value for each bit position. In another embodiment, an improved Max-Log approximation for LLR computation is used in both the MIMO demapper 305 and the MAP decoder 309 associated with the convolutional code used as the outer encoder in the transmission scheme. be able to. The improved Max-Log approximation for the calculation of the a posteriori LLR value is obtained when calculating the forward recursive metric sequence, the reverse recursive metric sequence, and the branch metric sequence updated to calculate the LLR: * The term relation can be used.
^{max * (x, y) =} log (e x + e y) = max (x, y) + log (1 + e - | x-y |)
Therefore, each constituent MIMO demapper 305 or MAP decoder 309 calculates the max ^* term by separate calculation of the max item (max (x, y)) and the logarithmic correction term (log (1 + e− ^| xy−)). calculate.

  [0065] FIG. 5 illustrates one embodiment of a so-called set partition type mapper for 16QAM used in iterative decoding. This type of mapper is suitable for BICM with iterative decoding (ID) as opposed to a gray mapper that is suitable for non-iterative decoding processes.

と表すことができ、ここで、ｈ_ｍｋ［ｎ］は、第ｎトーン上の第ｋ送信アンテナと第ｍ受信アンテナとの間の有効チャネル利得を表し、ｗｍ［ｎ］は、第ｍアンテナ及び第ｎトーン上の関連する熱雑音項を表す。代替案では、（２）を、次のようにコンパクトに表しなおすことができる。
ｙ［ｎ］＝Ｈ［ｎ］ｓ［ｎ］＋ｗ［ｎ］ （３）
ここで、ｈ［ｎ］＝［ｈ_１［ｎ］ ｈ_２［ｎ］…ｈ_Ｎ［ｎ］］^Ｔであり、ｈ_ｍ［ｎ］＝［ｈ_１ｍ［ｎ］ ｈ_２ｍ［ｎ］…ｈ_Ｎｍ［ｎ］］^Ｔであり、ｓ［ｎ］＝［ｓ_１［ｎ］ ｓ_２［ｎ］…ｓ_Ｎ［ｎ］］^Ｔであり、ｙ［ｎ］及びｗ［ｎ］は、同様に定義され、Ｎ_ｔ＝Ｎ_ｒ＝Ｎであると仮定される。 Example of inner decoder structure
[0066] After OFDM front-end pre-processing, the samples from each receive antenna and on each tone are the inner / outer soft-in soft-out decoder structures for decoding shown in FIGS. 3 and 4 described above. Passed through. As also described above, in one embodiment, the outer decoder is an optimal (soft in soft out) BCJR decoder. The near-optimal receiver complexity associated with these types of coded OFDM / BICM / OFDM systems resides in the inner decoder of the receiver structure of FIG. Received signal samples on the mth receive antenna and the nth tone,

Where h _mk [n] represents the effective channel gain between the k th transmit antenna and the m th receive antenna on the n th tone, and w m [n] is the m th antenna and Represents the associated thermal noise term on the nth tone. In an alternative, (2) can be re-expressed compactly as follows:
y [n] = H [n] s [n] + w [n] (3)
_Here, a _{h [n] = [h 1} [n] h 2 [n] ... h N [n]] T, h m [n] = [h 1m [n] h 2m [n] ... h Nm [N]] ^T , s [n] = [s ₁ [n] s ₂ [n]... S _N [n]] ^T , and y [n] and w [n] are defined similarly. , N _t = N _r = N.

  [0067] Channel state information (CSI) is not available at the transmitter, but CSI is assumed to be fully available at the receiver, ie, the set of H [n] is at the receiver It is assumed that it is known but unknown at the transmitter.

[0068] On each OFDM tone, N QAM symbols are transmitted simultaneously, and each of the N receive antennas receives a linear combination of these N symbols. Defined by the dynamic channel coefficient). In one embodiment, the complexity of an optimal (hard or) soft output inner decoder is defined by the number of possible N tuples of 2 ^B QAM symbol candidates, which is C = (2 ^B ). ^N = 2 ^BN , where B is the number of bits per QAM signal point and N is the number of transmit antennas. The complexity measure C indicates the number of terms required to form a log-likelihood ratio (LLR) in the joint demapper (inner decoder). This is also the number of terms that are checked by the MAP decoder or the MaxLogMAP decoder. As an example, for a 4 × 4 16 QAM (1 Gb / s) system [2], C = 2 ¹⁶ , but for 6 × 6 64 QAM (2.5 Gb / s) and 12 × 12 64 QAM (5 Gb / s) systems, The value of C is clearly in the “impractical range” with C = 2 ³⁶ and C = 2 ⁷² , respectively.

  [0069] As noted above, in one embodiment, the receiver uses a modified version of the soft output M algorithm (SOMA). SOMA is well known in the art, and is incorporated by reference, for example, Wong, “The Soft Output M-algorithm and its applications”, Canada, Kingston, Queen University PhD thesis, See August 2006. In one embodiment, a modified soft output M algorithm (SOMA) is used adaptively. The M algorithm is well known in the art and is described, for example, in Lin and Costello, “Error Control Coding, 2nd Edition”, Prentice Hall, New York, USA, 2003.

  [0070] In contrast to the basic M algorithm that does not provide soft output values, in one embodiment, the joint demapper increases the candidate exponentially by performing a reduced search in the detection tree. Use the modified SOMA to find the best alternative in the population to be. This is done by expanding only the M best alternatives from all levels of the tree, not all alternatives. In one embodiment, the M best alternatives are determined using a metric. In one embodiment, this metric is well known in the art, as described in Lin and Costello, “Error Control Coding, 2nd Edition”, Prentice Hall, New York, USA, 2003, etc. This is a so-called MaxLogMAP type metric.

  [0071] Based on the search through the detection tree, the joint demapper calculates the soft output value by comparing the estimated best path with the best alternative path that branches from the best path. These paths through the levels of the tree may be terminated at the end of the tree (there are M such paths) or non-terminated at all levels (there are T early terminated paths) it can. That is, the SOMA detection process performs these soft output computations iteratively during the tree search in the algorithm, thereby calculating confidence values for all possible bit positions at each depth in the tree. To do that, we use the early terminated path at that depth and the best candidate at the same depth.

  [0072] The soft output from the inner SOMA-based MIMO joint demapper is then used by a soft-in-soft-out (SISO) decoder for the outer binary code. The decoder supplies soft values back to the inner decoder with iterative turbo type iterative decoding. In another embodiment, a soft input hard output Viterbi decoder (ie, a simpler outer decoder) uses a soft output value from an inner SOMA-based MIMO joint demapper to use a hard input for non-iterative decoding. Create output data.

  [0073] In one embodiment, the inner decoder is channel adaptive. Such a channel-adaptive version of the SOMA inner decoder saves in complexity without significant performance degradation (with respect to the base SOMA design), while at the same time being optimal for a given channel implementation towards the desired target BER performance Is possible.

  [0074] In one embodiment, the SOMA algorithm first computes the (estimated) symbol by performing an approximate maximization computation by making the above computation a computation on the tree and then restricting the search through the tree. Compute reliability information for decision values and associated bit estimates.

[0075] Next, the focus is on SOMA operation for a fixed but arbitrary OFDM tone n. For convenience, the dependence of all variable vectors and matrices on the OFDM index n is omitted. In one embodiment, the mapping of the MaxLogMAP demapper computation to the tree structure is described based on exploiting the QR type decomposition of the channel matrix. π: {1, ..., N }: {1, ..., N} is, represents a permutation ^{function, s (π) = [s} π (1), s π (2) ... s π (N)] is ^T , S represents the associated N symbol permutation, and p ^(π) represents the associated permutation matrix, ie the matrix that yields s ^(π) −P ^π s.

をもたらし、このベクトルは、次のように表すことができ

これによって、

であり、ｉ＞ｊのときには

である。例については図１２を参照されたく、この図では、式（４）の右辺の最初の項の構造が、Ｎ_ｔ＝Ｎ_ｒ＝Ｎ＝３について示されている。 [0076] in association with any fixed order [pi, decomposition, the channel matrix H from equation ^{(3), H (π)} = Q (π) in the form of ^{L (π) H (π)} = H [P ^(π) ] ^T , Q ^(π) is a unitary matrix, and L ^(π) is a lower triangular matrix. As a result, the information lossless projection operation of y onto [Q ^(π) ] ^H is a vector that constitutes a set of measured values equivalent to those at y from equation (3)

This vector can be expressed as

by this,

And when i> j

It is. See FIG. 12 for an example, in which the structure of the first term on the right hand side of Equation (4) is shown for N _t = N _r = N = 3.

の式を与えられれば、フル検索ＭａｘＬｏｇＭＡＰを、ツリーでの検索を介する測定値の上の集合に基づいて簡単に実施することができる。ツリー内の深さｋでは、最初のｋ個の式だけが、候補をランキングするために式（４）から考慮される。これらの式は、ｓ^（π）の最初のｋ個のシンボルだけに依存するので、候補の集合は、グループ内でランキングされ、これによって、各グループは、πによって記述されるオーダー内の最初のｋ個のシンボル内の同一のシンボル値を有するすべてのＮシンボル候補に対応する。具体的に言うと、

が、２^Ｂ個のＱＡＭシンボル値の任意のＮ×１ベクトルを表し、

であり、

が、

にマッピングされる第ｋビットの関連する値を表すものとすると、ＭａｘＬｏｇＭＡＰ計算は、

に縮小され、ここで、

であり、

である。 [0077] gave above

Given the equation, a full search MaxLogMAP can be easily implemented based on the top set of measurements via a search in the tree. At depth k in the tree, only the first k equations are considered from equation (4) to rank the candidates. Since these equations depend only on the first k symbols of s ^(π) , the candidate set is ranked within the group so that each group is the first in the order described by π. Corresponds to all N symbol candidates having the same symbol value in k symbols. Specifically,

Represents an arbitrary N × 1 vector of 2 ^B QAM symbol values,

And

But,

Assuming that the associated value of the kth bit that is mapped to

Where

And

It is.

は、深さＮ及びノードあたり２^Ｂ個の分岐のツリーに対するフルツリー検索を介してたやすく再帰的に実施することができる。 [0078] amount

Can be easily and recursively implemented through a full tree search on a tree of depth N and 2 ^B branches per node.

を直接にデマッピングし、入手するのに使用される。信頼性メトリックは、すべての長さＮのツリー候補２≦ｒ≦Ｍ＝Ｎ_ｔｅｒｍに基づいて、２つの行列内で更新される。次に、第ｍ ＱＡＭシンボル内で表される第ｋビットの相対信頼性情報は、

によって与えられる。Ｍ（深さあたりの生き残る候補）及びＮ−ｔｅｒｍ（アーリーターミネーテッドパスに基づいてソフト情報を収集するのに使用される候補の個数）の値を変更して、計算の複雑さとビットエラーレート性能とをトレードオフすることができる。反復復号セッティングでは、各反復サイクルに、各復号器が、他の復号器に入力として渡される（ＭＩＭＯデマッパの場合には適当にインターリーブされ、他のＭＡＰ復号器の場合には再インターリーブされる）外部情報を計算する。この外部情報は、復号器によって作られる軟出力情報（たとえば、ＭＩＭＯデマッパの場合には、式（７）を参照されたい）と復号器への入力固有情報との間の差として計算される。通常、任意の所与の特定のビット位置について復号器の間で渡される外部情報は、差分値の形すなわち、「ビット＝１」値と「ビット＝０」信頼性値との間の差である。反復復号が使用される場合に、式（６）に示されたＳＯＭＡ復号に使用されるメトリックは、外部項を含むように変更される。具体的に言うと、もう１つの項が、式（６）の右辺に追加され、この項は、シンボル

の２進表現内のビット位置ごとに１項の項の合計である。差分信頼性値が使用されるときには、任意の所与だが固定されたビット位置に対応する、追加される項は、

内のそのビット位置のビット値が０である場合には０と等しく、そうでない場合には差分入力信頼性値と等しい。 [0079] The SOMA algorithm essentially performs a limited MaxLogMAP metric based search on the tree. As with all M algorithms, from all surviving candidates at all given levels, all possible candidates are expanded to the next level (in this case 2 ^B M), but part of these candidates Only the set M is saved for searching deeper in the tree. An important element of SOMA is to recursively generate and update a quality metric estimate for each value of each of the NB bits represented on the tree. Specifically, SOMA leverages two N × B matrix delta a ⁽⁰⁾ and delta ^(1), whereby the relative reliability metric associated with the value 0 and 1 of the k bits in the _{s m} Are given by δ ⁽⁰⁾ _{m, k} = [Δ ⁽⁰⁾ ] _{m, k} and δ ⁽¹⁾ _{m, k} = [Δ ⁽¹⁾ ] _{m, k} , respectively. This method includes recursively extending the respective survive path level m in the 2 ^B pieces of the path extension of the next level, and calculating the cumulative metrics of the new path, the path in the order of decreasing metric Rely on sorting. If p _{[I, i], r} represents the rth ranked path of depth I, the top M paths, ie, the set {p _{[I, i], r} ; 1 ≦ r ≦ M } Are saved, and paths in {p _{[I, i], r} ; r> M} are aborted. However, the subset of the best N _term truncated paths {p _{[I, i], r} ; M + 1 ≦ r ≦ M + N _term } is related to [Δ ⁽⁰⁾ ] and [Δ ⁽¹⁾ ] By updating the position, it is still used before being discarded to create a relative reliability update of the bit and the bit value it represents. After completion at depth N, SOMA first selects the surviving length N path with the best cumulative metric as a hard estimate. This N × 1 vector of QAM symbols is an NB bit hard estimate

Used to directly demap and obtain The reliability metric is updated in two matrices based on all length N tree candidates 2 ≦ r ≦ M = N _term . Next, the k-th bit relative reliability information represented in the m-th QAM symbol is

Given by. Change the values of M (candidates per depth) and N-term (number of candidates used to collect soft information based on early-terminated paths) to improve computational complexity and bit error rate performance Can be traded off. In the iterative decoding setting, each decoder passes each decoder as an input to each other decoder (appropriately interleaved for the MIMO demapper and reinterleaved for the other MAP decoders). Calculate external information. This external information is calculated as the difference between the soft output information produced by the decoder (see, for example, Equation (7) for the MIMO demapper) and the input specific information to the decoder. Typically, the external information passed between decoders for any given specific bit position is in the form of a difference value, ie, the difference between a “bit = 1” value and a “bit = 0” reliability value. is there. When iterative decoding is used, the metric used for SOMA decoding shown in equation (6) is modified to include the outer term. Specifically, another term is added to the right side of equation (6), which is the symbol

Is the sum of one term for each bit position in the binary representation. When a differential confidence value is used, the added term corresponding to any given but fixed bit position is

Is equal to 0 if the bit value at that bit position is 0, otherwise it is equal to the differential input reliability value.

この場合に、ｓは、サイズＮ_ｔ＝Ｎ_ｒ＝Ｎ＝２のベクトルであり、ｓの各項目は、コンステレーションシンボルに対応する。 [0080] FIGS. 6A and 6B illustrate the operation of a simple 2 × 2 example one subtone MIMO demapper. Referring to FIG. 6A, the y ₁ and y ₂ signals are generated from symbols s ₁ and s ₂ from the first and second antennas. This is done in a known manner according to the following.

In this case, s is a vector of size N _t = N _r = N = 2, and each item of s corresponds to a constellation symbol.

[0081] The MIMO demapper receives the y ₁ and y ₂ signals for one subtone as shown in FIG. 6B and returns an estimate of the bits represented by the symbols s ₁ and s ₂ . In addition, soft output (reliability information) is provided for the estimated set of bits. FIG. 7 shows another representation of the receiver of FIG. 3, where each MIMO demapper for each subtone is shown.

の第１測定値は、ｓ_１だけに依存するが、第２測定値は、ｓ_１とｓ_２との両方に依存する。次に、式（６）のメトリックが考慮され、これは、このケースでは２つの項の合計である。第１の項（ｍ＝１）は、

の第１測定値に起因する項であり、ｓ_１だけに依存する。第２の項（ｍ＝Ｎ＝２）は、第２測定値に起因する項目である（１つのｌ_１２項及び１つのｌ_２２項からなる）。この構造は、（６）のメトリックのそれぞれの計算をツリーに対して実行することを可能にする。ツリーの第１レベルでは、（６）の合計の第１項（ｍ＝１）だけが計算される。これらは、ｓ_１だけに依存するので、計算される項の個数（したがって、ツリー内のレベル１ノードの個数）は、ｓ_１がとることのできる可能な値の個数と等しい。第２ステップでは、レベル１の各ノード（それぞれがｓ_１の別個の値に対応する）から、ｓ_２の各可能な値の葉が、延長され、式（６）の合計の第２項（分岐メトリック）が、計算され、ｓ_１の特定の値に対応する第１項に加算される。最後に（このケースではレベル２で）、シンボルの候補ベクトルと同数のエンドノードがあり、各ノードは、特定のベクトルシンボル候補の（６）の計算を表す。したがって、これらを、ＱＡＭシンボルベクトルによって表されるすべてのビットのビット推定値及び信頼性情報を提供するために、（５）と同様に比較することができる。 [0082] After the QR decomposition, two scalar measurements are obtained in the form described in equation (4). For illustration, consider the permutation order π corresponding to orders s ₁ and s ₂ . Due to the structure of the L ^π matrix in (4),

The first measured value of depends on s ₁ only, whereas the second measured value depends on both s ₁ and s ₂ . Next, the metric of equation (6) is considered, which in this case is the sum of the two terms. The first term (m = 1) is

This term depends on the first measurement value of, and depends only on s ₁ . The second term (m = N = 2) is, (consisting of one _{l 12} Section and one _{l 22} Section) The second is an item due to the measured value. This structure allows each calculation of the metric of (6) to be performed on the tree. At the first level of the tree, only the first term (m = 1) of the sum of (6) is calculated. Since these depend only on s ₁ , the number of terms computed (and thus the number of level 1 nodes in the tree) is equal to the number of possible values that s ₁ can take. In the second step, from each node at level 1 (each corresponding to a distinct value of s ₁ ), the leaves of each possible value of s ₂ are extended and the second term (6) in the sum of equation (6) ( A branch metric) is calculated and added to the first term corresponding to a particular value of s ₁ . Finally (at level 2 in this case) there are as many end nodes as there are symbol candidate vectors, each node representing the (6) calculation of a particular vector symbol candidate. Thus, they can be compared as in (5) to provide bit estimates and reliability information for all bits represented by the QAM symbol vector.

[0083] FIG. 8 shows a decision tree that allows this recursive calculation of metrics on the tree when there are three transmit antennas and thus N _t = N _r = N = 3. The SOMA algorithm searches a limited set of paths, not the entire tree. The form of limiting the path is to start the path expansion from the root of the tree and at each level, save only a subset of the paths as surviving paths (ie, as further expanded paths). Referring to FIG. 8, at each new level of the tree, a decision is made to expand the tree with respect to only the M best branches. This determination can be based on calculating a partial distance metric for each candidate. Specifically, at level “n”, the distance metric used for comparison is the expression for the first “n” terms in equation (6) (ie, m = 1, 2,..., “N”). (Sum of all terms in (6)). Then, based on this metric, the best M metrics are selected as survivors. Thus, the tree is pruned at each depth by preserving only the best M paths. This is illustrated in FIG.

  [0084] Note that the SOMA detection process calculates soft (reliability) information for each bit in addition to selecting the best path in a manner well known in the art.

  In one embodiment, the complexity of the inner SOMA decoder is controlled by the values of parameters M (number of best paths) and T (early terminated paths). The overall complexity is also controlled by the number of times the inner decoding algorithm is used, which is determined by the number of OFDM tones and the number of iterations (I) used for iterative decoding. FIG. 10 is a flow diagram of one embodiment of a process for setting up a SOMA inner decoding operation for tone n. This process may be performed by processing logic that may include hardware (eg, circuitry, dedicated logic, etc.), software (such as that running on a general purpose computer system or a dedicated machine), or a combination of both. . This process is performed for each tone.

  [0085] Referring to FIG. 10, the first channel measurements are for all transmit antenna / receive antenna pairs on any given tone f (which should correspond to an estimate of the channel matrix H [f]). Used to estimate the channel between (based on pilot signal) (1001). Channel estimation and SNR calculation are based on pilot measurements (1031) on OFDM tone f. A channel estimation and look-up table (LUT) 1005 is used to set up SOMA adaptability (eg, change I's M, T).

  [0086] More specifically, these measurements set up a QR decomposition of the channel matrix, set up a SOMA detection tree (1003), and select parameters for the SOMA algorithm (eg, via a lookup table) ( 1004). As can be seen from this flow chart, the selection of these parameters depends on the channel conditions. Next, after the QR decomposition (1002), detection tree (1003), and SOMA parameters are set (1004), the measured data (1032) at all receive antennas on tone f is transformed into ) To generate a set of valid channel measurements and build a tree (1003), after which the SOMA inner detection algorithm is implemented. SOMA is implemented on OFDM tone f by using the SOMA parameter on tone f provided by LUT 1005. The output of LUT 1005 may provide one or more variable values of SOMA parameters on tone f, ie, M, T (1032), and the number of inner outer soft output decoder iterations, ie, I (1033). . That is, the LUT 1005 can specify the value of M (when M is adaptive) while the values of T and I are not changed (not adaptable), or the values of M and I change A value of T (when T is adaptive) can be specified while not being done (not adaptable). The same can occur for multiple of the M, T, and I values. These values can be changed at different depths / levels of the tree so that adaptation occurs across different levels. In that case, adaptation occurs based on tone quality and depth. In an alternative embodiment, the LUT is not used and the value is changed in the SOMA algorithm itself. In that case, in one embodiment, the value is a threshold within the algorithm. For example, if a tone channel estimate is in the first range, a value of M (eg, M = 8) is used, but if a tone channel estimate is in another range , Different values of M (eg M = 12) are used. These changes in value may also be based on the number of transmit antennas, the rate of the outer binary code, etc. Certain values can vary based on whether a group of paths with the best metric has a metric clustered together, a metric within a cluster (greater than or less than M) or a cluster Metrics that have a value within a percentage of the worst metric in (for example, have 95% of the value of the lowest quality metric in the cluster) are allowed to continue to the next level / depth in the tree Please note that. The resulting surviving set may have a concentration greater than or less than M. Alternatively, the process may save all paths whose relative metrics are within a certain percentage (eg, 95%) of the best path metric as surviving paths. The output of the SOMA inner detection algorithm is a bit estimate, bit reliability information, and bit external information.

[0087] A flow diagram of the SOMA detection process at depth n is shown in FIG. Referring to FIG. 11, the primary inputs are the survival set of depth n−1 and the metrics (1101) of these survival paths. First, all possible length 1 extensions of each of these length “n−1” paths are constructed based on the previous depth survival and its metric (1101) and are visited by the algorithm. Generate a set of all length n paths. Next, the length n metric (1104) of the length n path composed of 1103 is calculated using the survival metric (1101) of depth n−1 and the effective measurement (1102) of depth n. . Based on the M and T parameter values for a given OFDM tone, depth n (1120), the paths are sorted based on their metric quality (1105). The M paths with the best metrics are selected as survivors (1112) at depth n in the same manner as the well-known SOMA process. The rest of the path is placed in the list of paths to be aborted (1107). For each one of these paths, a relative metric is calculated (1109) with respect to the best path of length n (obviously it is a survivor path). Next, for each path to be censored, the relative metric is compared to a subset of items in the bit LLR (minimum likelihood ratio) table to determine whether the item should be updated. Specifically, if the k-th bit of the m-th symbol representation is 0, the (k, m) item of Δ ⁽⁰⁾ is updated (1108), otherwise Δ ^{(1 of)} (k, ^m) only to update item (1108). An entry is updated when the relative metric provided by the new path improves the metric of the associated entry in the LLR table. The output is an updated bit LLR table (1111).

  [0088] In one embodiment, different SOMA detectors are used for different tones and are adaptively selected based on tone quality. For tones with good quality (high signal level or high SNR), the M value can be lowered and / or the T value can be lowered and / or the I value can be lowered. [The range of values for M and T varies as a function of the number of transmitted streams (number of transmit antennas) N, the size of the QAM constellation used, and the rate of outer codes in the system. As an example, based on experiments with 4 × 4 16QAM MIMO, M = 16 is sufficient to obtain near-optimal performance. However, this value increases with increasing number of streams and QAM constellations. Typically, a (precomputed) lookup table can be used that lists the values of M (and T) that must be used for a set of SNR (or signal level) ranges. This approach results in a lower relative complexity for that particular SOMA detector. On the other hand, for bad quality (low signal level or low SNR) tones (OFDM subchannel), larger values are selected for all or a subset of M, T, and I. For example, one approach corresponds to setting the target performance at the bit error rate on the mobile, which can be preset by the application. In this case, the higher the SNR on the tone, the smaller the value of M required to achieve the desired performance. The opposite effect occurs when the SNR decreases. However, there is an SNR level (regardless of complexity) that lowering the received SNR beyond that value makes it impossible to achieve the desired performance in mobile. Beyond that level, either the maximum allowable value of M is used, or the event is declared an accident. This leads to higher value complexity for this particular tone. The adaptive use and number of iterations of SOMA saves complexity without performance degradation. For the non-adaptive case, the performance is defined to a large extent by the worst quality tone that corresponds to the highest relative complexity of the SOMA detector.

  [0089] In another embodiment, complexity allocation may be performed across successive OFDM symbols, ie over time. For example, resources can be allocated together across frequencies (OFDM tones) as well as time, so that the complexity does not exceed a predetermined value across blocks of OFDM symbols.

  [0090] In another embodiment, the value of M is changed within one SOMA detector, ie, a variable number of expanded paths and the use of a variable number at each level in the soft output calculation. Search the tree with the early terminated path to be played.

  [0091] In another embodiment, S.I. Lin and D.C. J. et al. Costello Jr. , “Error Control Coding, 2nd Edition”, Prentice Hall, New York, USA, 2003 and Kitty Wong, “The Soft Output M-algorithm and its applications, Queen of Canada, University of Canada, King of Canada” A metric correction term almost identical to that used in the corrected SOVA algorithm described in August applies to the soft output M algorithm.

  [0092] Note that the techniques described herein for low complexity receivers need not be limited to systems that use OFDM modulation.

Benefits of embodiments of the invention
[0093] One benefit of embodiments of the present invention is the overall complexity with which MaxLogMAP can be implemented in N and B iterative decoding settings that are not practically feasible or practical using current technology. It is an object of the present invention to provide a method for a high performance inner joint demapper with a soft output. Such embodiments include one or more of the following elements.
1. The complexity reduction of SOMA-based detectors is performed adaptively with respect to the quality of each tone, resulting in the best overall complexity for certain performance levels, OFDM-based with one MIMO detector per tone system.
2. A SOMA detector for each tone, wherein the M and T values are adaptively selected based on tone quality, and the number of iterations of iterative decoding is also adaptively selected based on tone quality.
3. The M value can vary across the decoding tree.
4). Adaptive resource and complexity allocation can also be performed across successive OFDM symbols, ie over time.
5). The MaxLogMAP metric is changed to the corrected metric in the bit LLR calculation.

  [0094] While numerous alternatives and modifications of the present invention will no doubt become apparent to those skilled in the art after reading the foregoing description, any particular embodiment shown and described by way of example is in no way possible. It should be understood that it is not intended to be considered limiting. Accordingly, references to details of various embodiments are not intended to limit the scope of the claims, which are themselves regarded as essential to the invention. Enumerate only.

Claims (2)

A device used in a wireless communication system having a transmitter,
A receiver for receiving an information-bearing signal transmitted wirelessly from the transmitter using OFDM and bit interleaved coded modulation, wherein the receiver is soft to perform joint inner demapping for each subtone. An inner decoder structure having a SOMA-based MIMO joint demapper that uses an output M-algorithm (SOMA) based multiple-in multiple-out (MIMO) detection process, wherein the SOMA-based MIMO joint demapper is Operable to identify the best candidate among a plurality of candidates by searching a detection tree under control of a parameter representing a total number of paths to be extended, and a plurality of best alternatives from all levels of the tree Only things are deployed, and the SOMA platform Scan of MIMO detection process adapts one or more of the parameters based on the tone quality, the device comprising the receiver. Evaluating the quality of individual OFDM tones received by a receiver in a wireless communication system;
Performing a first decoding operation to produce a first set of output data representing maximum likelihood transmitted bit estimates and information about the reliability of each of these estimates, comprising:
Identifying the best candidate among multiple candidates by searching the detection tree under the control of a parameter representing the total number of paths extended from each level, and controlling the parameter representing the number of early-terminated paths. Performing a SOMA-based MIMO detection process for each subtone for joint inner demapping by calculating all bit reliability information below, wherein one or more parameters are A method comprising: determining the OFDM quality based on the estimated quality of an OFDM tone.

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