RU2458474C2 - Reducing quadrature imbalance using unbiased training sequences - Google Patents

Abstract

FIELD: information technology.

SUBSTANCE: system and method are provided for transmitting an unbiased communication training sequence. The method generates an unbiased training sequence in a quadrature modulation transmitter. The unbiased training sequence represents uniform accumulated power evenly distributed in the complex plane. As a result, training information in the time domain is sent via an in-phase (I) modulation path having accumulated power. Training information in the time domain is sent via a quadrature (Q) modulation path having accumulated power equal to the I modulation path power. Also provided are a system and a method for calculating an unbiased channel estimate from a received unbiased training sequence.

EFFECT: reducing imbalance between quadrature paths to eliminate errors.

71 cl, 14 dwg

Description

Priority claim in accordance with 35 U.S.C §120

This patent application is a partial continuation of patent application No. 11 / 684,566, under the name QUADRATURE MODULATION ROTATING TRAINING SEQUENCE, filed March 9, 2007, which is under examination, registration number of patent attorney No. 060395, and transferred to its assignee and thereby explicitly given here as a reference material.

State of the art

FIELD OF THE INVENTION

The present invention generally relates to estimating a data channel and, more particularly, to systems and methods for using an unbiased quadrature modulation training sequence in training receiver channel estimates.

Description of the Related Art

In FIG. 1 shows a block diagram of a conventional receiver pre-processing device (prior art). A conventional wireless receiver includes an antenna that converts the emitted signal into a signal transmitted over the wires. After some initial filtering, the signal transmitted over the wires is amplified. After obtaining a sufficient power level, the carrier frequency of the signal can be converted by mixing the signal (down-conversion) with the local oscillator signal. Since the received signal is quadrature modulated, the signal is demodulated using separate paths I and Q before combining. After frequency conversion, the analog signal can be converted to a digital signal using an analog-to-digital converter (ADC, ADC) for processing in the main passband. Processing may include fast Fourier transform (FFT, FFT).

There are many errors that can be made to the receiver that negatively affect channel estimates and useful signal recovery. Errors can be made from mixers, filters, and passive components such as capacitors. Errors are exacerbated if they lead to an imbalance between the Q and I paths. When attempting to estimate a channel and thereby nullify some of these errors, communication systems may use a message format that includes a training sequence, which can be a repeatable or predefined data character. Using, for example, an orthogonal frequency division multiplexing (OFDM) system, the same signal constellation IQ point can be transmitted in repetition for each subcarrier.

In an attempt to save energy in portable, battery-powered devices, some OFDM systems use only one modulation symbol for training. For example, a single direction is induced in the signal constellation (for example, path I), while another direction (for example, path Q) is not induced. The same type of unidirectional training can also be used with pilot tones. Note: the scrambling of a single modulation channel (for example, channel I) with a symbol value of ± 1 does not rotate the point of the signal constellation and does not provide the possibility of induction for the quadrature channel.

In the presence of an imbalance of the quadrature path, which is often found in systems with a large bandwidth, the training sequence described above, used to save energy, leads to a biased channel estimate. A biased channel estimate may well align the IQ of the signal constellation in one direction (i.e., in the I path), but leads to a quadrature imbalance in the orthogonal direction. Preferably, any imbalance is equally distributed between the two channels.

2 is a diagram illustrating a quadrature imbalance on the receiver side (prior art). Although not shown, the imbalance on the side of the transmitter is similar. Suppose that the Q path is a reference channel. The shape of the incident wave is cos (ωt + θ), where θ represents the phase of the channel. The Q path is downconverted using -sin (ωt). The I path is downconverted using (1 + 2ε) cos (ωt + 2Δφ). 2Δφ and 2ε represent the phase error and the amplitude error of the hardware imbalance, respectively. Low-pass filters H ₁ and H _Q are different for each path. Filters introduce additional amplitude and phase distortion. However, these additional distortions are concentrated inside 2Δφ and 2Δε. Note: these two filters are real and affect both + ω and -ω in the same way.

Assume the errors are small:

(1 + 2ε) cos (ωt + 2Δφ)) ≈ (1 + 2ε) cos (ωt) -2Δφ.sin (ωt).

The first component on the right side, cos (ωt), represents the ideal path I, to a small extent scaled. The second component, 2Δφ.sin (ωt), is a small leak from the Q path. After conversion with decreasing frequency of the incident wave:

in the path I: (1 + 2ε) cos (θ) + 2ε.sin (θ);

in the path Q: sin (θ).

Errors lead to an incorrect interpretation of the positions of symbols in the signal constellation of quadrature modulation, which, in turn, leads to incorrectly demodulated data.

SUMMARY OF THE INVENTION

Wireless receivers are prone to errors due to lack of tolerances for hardware components associated with mixers, amplifiers, and filters. In quadrature demodulators, these errors can also lead to an imbalance between paths I and Q, resulting in incorrect data processing.

The training signal can be used to calibrate the receiver channels. However, the training signal, which does not simultaneously induce both paths I and Q, does not solve the problem of imbalance between the two paths.

In accordance with this, a method for transmitting an unbiased training communication sequence. The method generates an unbiased training sequence in a quadrature modulated transmitter. An unbiased training sequence is a homogeneous accumulated power uniformly distributed in a complex plane. More explicitly, training information in the time domain is sent via the common mode modulation path (I) having stored power. The training information in the time domain is sent via a quadrature (Q) modulation path having a total power equal to the power of the modulation path I.

In one aspect, an unbiased training sequence is generated as a pair of signals including a complex reference signal (p) at a frequency of + f and a complex mirror signal (p _m ) at a frequency of -f. The method allows to reduce the product (p · p _m ) to zero.

A method for calculating an unbiased channel estimate is also provided. In this method, an unbiased training sequence is received at a quadrature demodulation receiver. An unbiased training sequence includes predetermined reference signals (p) representing a uniform stored power uniformly distributed in a complex plane. The method processes an unbiased training sequence and generates processed symbols (y) representing complex plane information in an unbiased training sequence. The processed symbols (y) are multiplied by the conjugate value of the corresponding reference signal (p *) and an unbiased channel estimate (h _u ) is obtained.

Additional details of the above methods, systems for generating unbiased training sequences and calculation of unbiased channel estimates and options for these systems and methods are presented below.

Brief Description of the Drawings

In FIG. 1 shows a block diagram of a conventional receiver pre-processing device (prior art).

In FIG. 2 is a diagram illustrating a quadrature imbalance on the receiver side (prior art).

In FIG. 3 is a block diagram showing an example data transmission system.

In FIG. 4 shows a block diagram of a system or device for transmitting an unbiased training sequence for transmitting data.

In FIG. 5A is a diagram showing an unbiased training sequence represented in both the time domain and the frequency domain.

In FIG. 5B and 5C are diagrams representing uniform storage of power uniformly distributed in a complex plane.

In FIG. 6 is a diagram showing an unbiased training sequence used as a sequence of pilot tones in a time domain.

In FIG. 7 is a diagram showing an unbiased training sequence used as a preamble preceding undefined transmission data.

In FIG. 8 is a diagram showing an unbiased training sequence used by averaging characters for multiple messages.

In FIG. 9 is a block diagram showing a processing device for transmitting an unbiased training sequence for transmitting data.

In FIG. 10 shows a block diagram of a system for calculating an unbiased channel estimate.

In FIG. 11 is a block diagram showing a processing device for calculating an unbiased channel estimate.

In FIG. 12 shows the performance achieved through the application of the above algorithms for the WiMedia UWB standard.

In FIG. 13 is a flowchart illustrating a method for transmitting an unbiased training sequence for transmitting data.

In FIG. 14 is a flowchart illustrating a method for calculating an unbiased channel estimate.

DETAILED DESCRIPTION OF THE INVENTION

Various embodiments are described below with reference to the drawings. In the following description, for purposes of explanation, various specific details are set forth in order to provide a thorough understanding of one or more aspects. However, it is obvious that such an option (s) can be practiced without these specific details. In other instances, well-known structures and devices are presented in block diagram form in order to facilitate describing these embodiments.

Used in this application, the terms "processor", "processing device", "component", "module", "system", etc. designed to refer to a computer-related object, presented either in the form of hardware, hardware-implemented software, a combination of hardware and software, software or software in runtime. For example, a component may be, but is not limited to, a process running in a processor; generation; CPU; an object; executable element; processing stream; program and / or computer. By way of illustration, both an application running in a computer device and a computer device can be a component. One or more components may reside within a process and / or processing stream, and the component may be localized in one computer and / or may be distributed between two or more computers. In addition, these components can be executed from various computer-readable recording media on which various data structures are stored. Components can communicate with each other using local and / or remote processes, such as, in accordance with a signal having one or more data packets (for example, data from one component interacting with another component in a local system, distributed system and / or through a network such as the Internet, with other systems using a signal).

Various embodiments will be described in the form of systems, which may include many components, modules, and the like. It should be understood that various systems may include additional components, modules, etc. and / or may not include all of these components, modules, etc., described with reference to the drawings. A combination of these approaches can also be used.

The various logical blocks, modules, and circuits presented above for illustration can be implemented or can be implemented using a general-purpose processor, a digital signal processor (DSP, DSP), specialized integrated circuits (ASIC, SIMS), a programmable valve matrix (FPGA, PPVM) or other programmable logic device, discrete keys or transistor logic, discrete hardware components, or any combination thereof designed to perform op the functions described here. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or using any other such configuration.

The methods or algorithms described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The program module can be in RAM (RAM, random access memory), in flash memory, in ROM (ROM, read-only memory), in EPROM (EPROM, erasable programmable read-only memory), in EEPROM (EEPROM, electrically erasable programmable read-only memory), in registers, on a hard disk, on a removable disk, on a CD-ROM (read-only CD), or in any other form of recording medium known in the art. The recording medium may be connected to the processor so that the processor can read information from it and write information to the recording medium. Alternatively, the recording medium may be integrated into the processor. The processor and the recording medium may be implemented in an ASIC (integrated circuit specialized for solving a specific problem). The ASIC can be installed in a host or in another location. Alternatively, the processor and the recording medium may reside as discrete components in a node or elsewhere in an access network.

In FIG. 3 is a block diagram showing an exemplary data communication system 300. The baseband processor 302 has an input on line 304 to receive digital information from a medium access control (MAC) layer. In one aspect, the baseband processor 302 includes an encoder 306 having an input on line 304 for receiving digital information (MAC) and an output on line 308 for providing encoded digital information in the frequency domain. An interleaver 310 can be used to interleave encoded digital information, to output information after interleaving in the frequency domain along line 312. An interleaver 310 is a device that converts a single input signal having a high speed into a plurality of parallel streams with a low speed, where each of the streams has lower speed is associated with a particular subcarrier. An inverse fast Fourier transform (IFFT, IFFT) unit 314 receives information in the frequency domain, performs an IFFT operation for the input information, and provides a digital signal in the time domain on line 316. A digital-to-analog converter 318 converts the digital signal of line 316 into an analog signal in the main band the transmission of line 320. As described in more detail below, the transmitter 322 modulates the signal in the main passband and provides a signal in the form of modulated carriers, as an output signal, on line 324. Note: alternative circuits e configuration, allowing to perform the same function as described above will be known to those skilled in the art. Although not explicitly represented, the receiver system could be composed of a similar set of components for reverse processing information received from the transmitter.

In FIG. 4 shows a block diagram of a system or device for transmitting an unbiased training sequence for transmitting data. System 400 comprises a transmitter or transmission means 402 having an input on line 404 for receiving digital information. For example, information may be issued from the MAC layer. Transmitter 402 has an output on line 406 to provide an unbiased training sequence with quadrature modulation representing uniform accumulated power uniformly distributed in the complex plane.

Transmitter 402 may include a transmitter subsystem 407, such as a radio frequency (RF, RF) transmitter subsystem that uses an antenna 408, for communication through a radio channel or vacuum environment. However, it should be understood that the invention is applicable to any data transmission medium (for example, wireless, cable, optical) that allows the transfer of quadrature modulated information. The transmitter subsystem 407 includes an in-phase 410 modulation path (I) or means for generating I modulation training information in a time domain having stored power. The transmitter subsystem 407 also includes a quadrature modulation path (Q) 412 or means for generating training information Q modulation in a time domain having an accumulated power equal to the power of the modulation path I. Channel I information on line 404a is up-converted in mixer 414 using carrier fc, while channel Q information on line 404b is up-converted in mixer 416 using phase shift carrier (fc + 90 °). Path 410 I and path 412 Q are summed in combining unit 418 and output on line 420. In some aspects, the signal is amplified in amplifier 422 and output to antenna 408 on line 406 from which unbiased training sequences are emitted. Alternatively, paths I and Q may be called channels I and Q. An unbiased training sequence may also be called a rotary training signal, a quadrature balanced training sequence, a balanced training sequence, or an unbiased training signal.

For example, an unbiased training sequence may be initially transmitted through a modulation path 410 I with training information subsequently transmitted through a Q modulation path 412. Thus, the training signal may include information such as a symbol or a repeating sequence of characters transmitted only through the modulation path I, after which the transmission of the symbol or repeating sequence of characters is performed only through the modulation path Q. Alternatively, training information may be transmitted initially through a modulation path Q and subsequently through a modulation path I. In the case where single characters are transmitted alternately through paths I and Q, the transmitter transmits a rotary training signal. For example, the first character can always be (1,0), the second character can always be (0, 1), the third character (-1, 0) and the fourth character (0-1).

However, there is no need to simply alternate the transmission of the symbols through the modulation paths I and Q to allow rotation of the symbol as described above. For example, a transmitter can transmit training information simultaneously through both modulation paths I and Q and combine modulated signals I and Q.

The type of rotation of the unbiased training sequence described above, in which the training signal is initially transmitted through (only) the modulation path I, can be performed by supplying power to the modulation path I without supplying power to the modulation path Q. The transmitter then transmits the training signal through the modulation path Q by supplying power to the modulation path Q after transmitting the training information through the modulation path I. Training symbols can also be rotated by issuing symbols, each of which simultaneously has components I and Q, as is usually the case with quadrature modulation.

Typically, transmitter 402 also transmits quadrature modulated (undefined) communication data. An unbiased training sequence is used in a receiver (not shown) to form unbiased channel estimates, which allows more accurate recovery of undefined communication data. In one aspect, quadrature modulated communication data is transmitted after transmitting an unbiased training sequence. In another aspect, an unbiased training sequence is transmitted simultaneously with communication data in the form of pilot signals. The system is not limited to any particular temporal relationship between the training signal and quadrature modulated communication data.

In order to be unbiased, the symbol values associated with any particular subcarrier may change periodically. The simplest means of evenly distributing information in a complex plane, when there is an even number of characters per message, is to rotate the character value 90 degrees each period. As used here, a message is a grouping of characters in a predefined format. A message has a duration of several periods of characters. One or more characters may be transmitted in each symbol period. Some messages include a preamble that precedes the main body of the message. For example, a message may be formed as a long packet containing multiple OFDM symbols. Each OFDM symbol contains multiple subcarriers. In some aspects, the message preamble includes an unbiased training sequence. In other aspects, an unbiased training sequence is a sequence of pilot signals that transmit simultaneously with undefined communication data.

If an odd number of characters are used in the training message sequence, a technique that rotates the character phase 90 degrees each period is not always useful. For a sequence of 3 characters, you can use a rotation of 60 degrees or 120 degrees to evenly distribute the value | _[r1] character in the complex plane. For 5 characters, you can use a rotation of 180/5 degrees or 360/5 degrees. If the number of characters in the training sequence is a prime, you can use solutions that are a combination. For example, if the total message contains 7 characters, then you can use a 90 degree rotation for the first 4 characters and a 120 (or 60) degree rotation for the next three characters. In another aspect, an unbiased training sequence may be averaged over more than one message. For example, if a message includes 3 training characters, then a combination of 2 messages includes 6 characters. In the context of a 6-character training signal, you can use a 90-degree rotation between characters.

Since the power is a measure depending on the square of the complex value of the symbol, the power associated with the symbol vector at an angle θ represented in the complex space can also be considered as the power at (θ + 180). Therefore, the accumulated power at an angle of 60 degrees is the same as the power at an angle of 240 degrees. In other words, the power associated with the symbol at an angle θ can be added to the power at an angle (θ + 180). As a result of summing the power at angles θ and (θ + 180), the complex space, if viewed when projecting onto the power, covers only 180 degrees. For this reason, uniform power storage is evenly distributed in the complex space when an unbiased training sequence consists of only 2 orthogonal symbols or 3 symbols separated by 60 degrees.

In FIG. 5A is a diagram showing an unbiased training sequence represented in both the time domain and the frequency domain. In one aspect, the transmitter generates a pair of signals including a complex reference signal (p) at a frequency of + f and a complex mirror signal (p _m ) at a frequency of -f, with a zero product (p · p _m ). For example, at time i = 1, the product (p ₁ · p _1m ) = 0. As noted above, p and pm are complex values with amplitude and phase components. In another aspect, the transmitter generates i instances of the reference signal (p) and the mirror signal (p _m ) and nullifies the sum of the products (P _i · P _im ). In other words, the sum (P _i · P _im ) = 0 for i from 1 to N. It should be noted that the “dot” symbol between P _i · P _im represents the usual operation of multiplication between scalar numbers.

Similarly, when the transmitter generates i instances of the reference signal and the mirror signal, the values of p and p _{m of the} signal pair can, but need not, change for each instance. For example, a transmitter can nullify the sum of the products (p _i · p _im ) by generating information as a complex value that remains constant for each instance to represent p. To represent p _{m, the} transmitter can generate information as a complex value that rotates 180 degrees in each instance. However, there are an almost infinite number of other ways in which the product (p _i · p _im ) can be made equal to zero.

In another aspect, the transmitter generates i instances of a reference signal (p) and a mirror signal (p _m ) and a product (p _i · p _im ) for each instance. The transmitter pairs these instances and makes the sum of the products from each instance of the pair equal to zero.

For example, one or more messages may comprise a time sequence of N pilot tones for a given subcarrier f, with N pilot tones for a mirror subcarrier -f. As noted above in the description of FIG. 5A, to form an unbiased training sequence using this pilot tone, the general solution is that the sum (p _i · p _im ) = 0, for i from 1 to N. For one specific solution, pilot tones are paired for i = 1 and 2. Thus, p ₁ · p _1m + p ₂ · p _2m = 0. Similarly, pilot tones for i = 3 and 4 can be paired as follows: p ₃ · p _3m + p ₄ · p _4m = 0. Such pairing can continue up to i = N. If each pair has a sum equal to zero, then the total amount will also be zero, that is, the sum p _i · p _im = 0. Pairing simplifies the zeroing problem. Instead of searching for N pilot signals that satisfy sum p _i · p _im = 0, it is enough that 2 pairs of pilot signals can be reset.

As described above, simple examples of generating an unbiased training sequence include either rotating the characters 90 degrees in the time domain or in the frequency domain, maintaining the reference symbol by + f, replacing the sign of the mirror signal with -f. In both of these examples, 2 tone pairs are used and they satisfy the equation p ₁ · p _1m + p ₂ · p _2m = 0.

Alternatively, an unbiased training sequence may include:

Time 1: p ₁ for + f and p _1m for -f;

Time 2: p ₂ for + f and p _2m for -f;

Time 3: p ₃ for + f and P _3m for -f; and

Time 4: p ₄ for + f and p _4m for -f.

An unbiased training sequence can be obtained by averaging. The principle of an unbiased training sequence dictates that the pilot signal must satisfy the following equation:

p ₁ · p _1m + p ₂ · p _2m + p ₃ · p _3m + p ₄ · p _4m = 0.

Alternatively, an unbiased training sequence may be organized as follows:

p ₁ · p _1m + p ₂ · p _2m = 0 and p ₃ · p _3m + p ₄ · p _4m = 0.

In FIG. 5B and 5C are diagrams representing uniform storage of power uniformly distributed in a complex plane. The complex plane can be used to represent real axis (R) and imaginary axis (I) information. The circle represents the boundary of uniform power or energy with a normalized value of 1. In FIG. 5B, an unbiased training sequence is formed of 3 characters: the first character (A) at 0 degrees; second character (B) at 120 degrees; and the third character (C) at 240 degrees. The same power distribution is obtained when the first character (A) remains at 0 degrees, the second character (B ') is under 60 degrees, and the third character (C') is at 120 degrees. The power associated with each symbol is 1.

In FIG. 5C an unbiased training sequence is formed of 5 characters: 2 characters at 0 degrees, each with a power of 0.5, so that the stored power is 1; character at 90 degrees with a power of 1; symbol at 180 degrees with a power of 1; and a symbol at 270 degrees with a power of 1.

As used above, the term “uniform power storage” can exactly correspond to the storage in each direction of the complex plane, since in many circumstances it is possible to receive and transmit an unbiased training sequence with zero error. Thus, the training sequence will be shifted by 100%. In other words, the sum p _i · p _im = 0, as described above. When using the worst case analysis, average L pilot symbols, each of which has a uniform stored power, as follows:

| sum p _i · p _im | = sum | p _i | ^{2 =} L.

If L is 100%, and if | sum p _i · p _im | = L / 4, then the error (of uniform total power) is 25%. An unbiased training sequence with a 25% error still gives excellent results. If L / 2 is used (50% error), a good result is obtained since the IQ interference in the channel estimation is still reduced by 6 dB.

In FIG. 6 is a diagram showing an unbiased training sequence made as a sequence of pilot tones in a time domain. The transmitter may generate an unbiased training sequence by outputting P pilot symbols per symbol period in a plurality of symbol periods. Each pulse in the drawing represents a symbol. The transmitter generates (N-P) quadrature modulated communication data symbols per symbol period and simultaneously outputs N symbols per symbol period in a plurality of symbol periods. In many data transmission systems, such as those that comply with the IEEE 802.11 and UWB standards, pilot tones are used to train the channel.

In FIG. 7 is a diagram showing an unbiased training sequence formed as a preamble preceding communication data not predetermined. The transmitter generates quadrature modulated communication data and provides an unbiased training sequence in the first set of symbol periods (e.g., during 1-4), followed by quadrature modulated communication data in the second set of symbol periods (e.g., from 5 to N). Again, the pulses in the drawing represent the symbols.

For example, in an ultra-wideband radio communication system (UWB, UWB), 6 symbols are used that are transmitted before transmitting data or a beacon signal. Therefore, 3 consecutive symbols can be generated in the modulation path I, followed by 3 consecutive symbols in the modulation path Q. Using this processing, the Q channel needs to be activated for a short period of 3 characters before returning to standby mode. However, there are many other combinations of symbols that can be used to generate an unbiased training sequence.

In FIG. 5B or 5C, it can be seen that the transmitter generates a time sequence of symbols of the complex plane with equal accumulated power in a plurality of directions (in the complex plane). As used herein, the term “direction” refers to the addition of vectors at each angle θ and (θ + 180). For example, the power associated with a symbol at 0 degrees is accumulated with a symbol power at 180 degrees, and 0 and 180 degrees represent the same direction. Due to this relationship, the temporal sequence of symbols in an unbiased training sequence has a common power associated with real axis information in the time domain and an equal total power associated with imaginary axis information in the time domain, as transmitted in a plurality of symbol periods using a transmitter. In another aspect, an unbiased training sequence representing a uniform stored power uniformly distributed in a complex plane can be expressed as a time sequence i of complex symbols (a) in the time domain as follows:

sum a _i (k) a _i (k) = 0,

where k is the number of samples per character period. It should be noted that the “point” between the characters a _i and a _{i is} intended to represent the usual operation of multiplication between scalar numbers.

Therefore, the symbol a _i typically represents a subcarrier with a periodic waveform, and does not have any one specific meaning. Thus, a _i changes over time and can be represented as a _i (t). However, if t samples are received, the symbol can be represented as a _i (kT) or a _i (k), assuming that T is normalized to 1. For systems in the time domain, addition over k disappears. When using only one selection per symbol, the symbol and the selection become the same, and the equation can be written as follows:

sum a _i · a _i = 0.

To illustrate, using a simple 2-character orthogonal unbiased training sequence, if the first character (i = 1) has an angle of 0 degrees, an equal amount of power must exist at an angle of 180 degrees to satisfy the equation. Similarly, if the second symbol is at 90 degrees, an equal amount of power should exist at an angle of 270 degrees. Other more complex examples may require summing the characters over the i indices to get a null final result.

If we consider alternatively, the formula sum a _i · a _i = 0 refers to the fact that if the projection is performed in any direction of the complex plane and the power is calculated, this power always remains the same, regardless of the angle. Power in the direction φ is:

This power remains constant for all φ if and only if sum a _i · a _i = 0.

It can be shown that the formula in the frequency domain (sum p _i · p _im = 0) is equivalent to sum a _i · a _i = 0. The signal in the time domain corresponding to p _i and p _im is:

since p _i modulates + f and p _im modulates -f.

Within one symbol i, the time integral a _i · a _i represents:

since exp (j4πft) rotates several times and disappears when integrated into one character.

Then a _i · a _i collected in one character is equal to p _i · p _im . If you sum up all the characters:

In FIG. 8 is a diagram showing an unbiased training sequence formed by averaging characters over a plurality of messages. A symbol (or more than one symbol not shown) is generated in the first symbol period in the first message. The symbol is generated in the second symbol period in the second message following the first message. More generally, training information symbols are generated in a plurality of (n) messages. The transmitter generates an unbiased training sequence, forming equal power in many directions in the complex plane, as accumulated over many messages. Although a preamble type training sequence is shown in the same manner as in FIG. 7, the same type of analysis is applied to an unbiased training pilot symbol sequence.

In FIG. 9 is a block diagram showing a processing device for transmitting an unbiased training sequence for transmitting data. The processing device 900 includes a transmitter module 902 for receiving digital information on line 904 and generating an unbiased training sequence with quadrature modulation on line 906. The unbiased training sequence represents uniform power storage uniformly distributed in a complex plane. The functions associated with the processing device 900 are similar to the transmitter described above with reference to FIG. 3-8, and will not be repeated here for brevity.

In FIG. 10 is a block diagram of a system for calculating an unbiased channel estimate. System 1000 comprises a quadrature demodulation receiver or receiving means 1002 having an input on line 1004 for receiving an unbiased training sequence. As with the transmitter of FIG. 4, the receiver 1002 may be an RF device coupled to an antenna 1005 for receiving radiated information. However, the receiver may alternatively receive an unbiased training sequence through a cable or optical medium (not shown). An unbiased training sequence includes predetermined reference signals (p) representing a uniform stored power uniformly distributed in a complex plane, as defined above.

Receiver 1002 generates processed symbols (y) along line 1006 representing complex plane information in an unbiased training sequence, which is transmitted to multiplier 1008. Since p is predetermined, multiplier 1008 is capable of multiplying each processed symbol (y) by a (predetermined) conjugate the corresponding reference signal (p *), and issuing an unbiased estimate (hu) of the channel to the output on line 1010. Information about the conjugate value can, for example, be stored in memory 1012 and can be issued to multiplier 1008 along line 1014.

In one aspect, receiver 1002 receives an unbiased training sequence with a plurality of simultaneously received predetermined reference signals (p _n ). For example, the receiver may receive a message with P pilot symbols (per symbol period), see FIG. 6. The receiver 1002 generates a plurality of processed symbols (y _n ) from the corresponding plurality of reference signals, multiplies each processed symbol by its corresponding conjugate value of the reference signal, obtains a plurality of channel estimates (h _un ) and averages the channel estimate (h _un ) for each value n . Using the example shown in FIG. 6, P unbiased channel estimates are obtained. A technique for determining a channel estimate is well known in the art. However, the receiver in accordance with the present invention is configured to calculate an extremely accurate channel estimate of an unbiased type using predetermined data.

In another aspect, receiver subsystem 1016 has a common mode (I) demodulation path 1018 or a means for receiving training information I demodulation in a time domain having stored power. The quadrature (Q) demodulation path 1020 or the demodulation training information Q receiving means in the time domain has an accumulated power equal to the power in the modulation path I.

When comparing FIG. 10 from FIG. 6, it can be seen that receiver 1002 receives an unbiased training sequence with a time sequence of n predetermined reference signals (p _n ). The receiver 1002 generates a time sequence of n processed symbols (y _n ) from the time sequence of the reference signals and multiplies each processed symbol in the time sequence by its corresponding conjugate value of the reference signal. In FIG. 6 P processed characters (y) are generated each character period. The receiver 1002 obtains a time sequence of n channel estimates (h _un ) and averages n channel estimates.

In one aspect, receiver 1002 receives an unbiased training sequence as a pair of signals including a reference signal (p) with a complex value at frequency + f and a mirror signal (p _m ) with a complex value at frequency -f, where the product (p · p _m ) is zero, see FIG. 5. In addition, the receiver can receive an unbiased training sequence as i instances of the reference signal (p) and the mirror signal (p _m ), where the sum of the products (p _i · p _im ) is zero. In one embodiment, the receiver 1002 receives i instances of the reference signal and the mirror signal, where the values of p and p _{m of the} signal pair change for each instance. In other embodiments, the receiver receives an unbiased training sequence as i instances of a reference signal (p) and a mirror signal (p _m ) and generates a product (p _i · p _im ) for each instance. The receiver makes pairs of copies and generates the processed symbol by zeroing the sum of the products of each copy made in pairs. For example, a receiver may receive a pair of signals where the sum of the products (p _i · p _im ) is reset to zero as follows. Information is taken as a complex value that remains constant for each instance representing p. Information representing p _m is taken as a complex value that rotates 180 degrees for each instance.

When comparing FIG. 10 and 6, in one aspect, it can be seen that the receiver receives the unbiased training sequence as P pilot symbols per symbol period in a plurality of symbol periods and obtains P unbiased pilot channel estimates. The receiver simultaneously receives (NP) quadrature modulated symbols of communication data in each symbol period, generating a processed symbol (y _c ) for communication data in each symbol period. In this way, (NP) processed symbols are generated. The receiver extrapolates the channel estimates for each processed symbol (y _c ) obtained from the unbiased estimates of the pilot channel, and multiplies each processed symbol by an extrapolated channel estimate to obtain the transmitted symbol (x). The symbol x is an unknown symbol value that is transmitted as communication data. The extrapolation of channel estimates for data channels based on unbiased channel estimates or adjacent pilot channels will be apparent to those skilled in the art.

When comparing FIG. 10 and 7, it can be seen that the receiver 1002 receives quadrature modulated communication data in symbol periods after receiving an unbiased training sequence. The receiver generates a processed symbol (y _c ) for each symbol of the communication data and multiplies each processed symbol by an unbiased channel estimate to output the transmitted symbol (x).

As noted above in the description of the transmitted unbiased training sequence, the receiver receives a time sequence of symbols of the complex plane with equal accumulated power (as defined above) in many directions in the complex plane. In this case, the time sequence of symbols of the unbiased training sequence has an accumulated power associated with information of the real axis in the time domain and an equal accumulated power associated with information of the imaginary axis in the time domain.

In another aspect, the unbiased training sequence received by the receiver can be expressed as a time sequence of i complex characters (a) in the time domain as follows:

where k is the number of samples per character period.

When comparing FIG. 10 and 8, it can be seen that the receiver can receive an unbiased training sequence as symbols in a plurality of messages having equal power in a plurality of directions in a complex plane, accumulated over a plurality of messages.

In FIG. 11 is a block diagram showing a processing device for calculating an unbiased channel estimate. The processing device 1100 comprises a quadrature demodulation receiving module 1102 having an input on line 1104 for receiving an unbiased training sequence having predetermined reference signals (p) representing a uniform total power uniformly distributed in a complex plane. The receiver module 1102 generates processed symbols (y) representing complex plane information in an unbiased training sequence outputted along line 1106. Multiplication module 1108 multiplies the processed symbols (y) by the conjugate value of the corresponding reference signals (p *) and provides an unbiased estimate (h _u ) an output channel on line 1110. Many of the properties of the processing device 1100, which are the same as the receiver shown in FIG. 10 will not be repeated here for brevity.

The training sequences, either generated in the preamble or as pilot signals, are similar in that the content of the communication data information is typically predetermined or is “known” data, which allows the receiver to calibrate and perform channel measurements. When receiving communication data (undefined), there are 3 unknowns: the data itself, the channel and the noise. In this case, the receiver cannot calibrate the noise, since the noise changes randomly. Channel measurements are usually associated with delay and multipath. For relatively short periods of time, errors resulting from multipath can be measured if predetermined data such as training or pilot signals are used. After the channel becomes known, such measurements can be used to eliminate errors in the received communication data (undefined). Therefore, some systems provide a training signal for channel measurement before decoding the data.

However, the channel may change, for example, due to the fact that either the transmitter or the receiver moves in space, or due to the departure of the clock frequency. Therefore, many systems continue to transmit additional “known” data along with “unknown” data to track slow changes in the channel.

Although not specifically shown, the transmitter of FIG. 3 and the receiver of FIG. 10 may be combined to form a transceiver. In fact, the transmitter and receiver in such a transceiver can share elements such as an antenna, a baseband processor, and a MAC layer circuit. The above explanations are intended to describe a transceiver that simultaneously transmits unbiased training sequences and calculates unbiased channel estimates based on the reception of unbiased training sequences from other transceivers in the device network.

Functional Description

Modern high-speed data transmission systems transmit signals through two separate channels - common mode and quadrature channels (I and Q). These two channels form a 2D (two-dimensional) signal constellation in the complex plane. Examples of signal constellations: QPSK (QPSK, quadrature phase shift keying) and QAM (QAM, quadrature amplitude modulation). Channels I and Q can be formed using radio frequency equipment, which may not be perfectly balanced due to variations in the RF components, resulting in IQ imbalance. In direct conversion systems that are becoming more widespread, the problem of imbalance becomes even greater. IQ imbalance distorts the signal constellation, resulting in mutual interference between Q and I channels: the signal interferes with itself. An increase in transmission power does not help, since self-generated interference increases with increasing signal power. The signal-to-noise ratio (SINR) reaches the upper limit, which sets the limit for the highest data rate achievable for a given RF equipment. To increase the data transfer speed, an expensive solution is used, consisting in the use of higher quality, more expensive equipment. Perhaps a less expensive solution is to digitally evaluate and compensate for IQ imbalance. The concept of digital estimation and compensation algorithms has been previously implemented in the art. However, these decisions tend to increase costs because they are not based on a special type of training sequence. Such decisions often only take into account the imbalance on one side, usually on the receiver side.

The following are examples that focus on orthogonal frequency division multiplexing (OFDM) based on time-domain systems that study end-to-end imbalance, from transmitter to receiver. In addition, in OFDM, an imbalance is modeled as a function of frequency, taking into account variations in the frequency response of the filters.

Two types of improvements are presented: one with zero cost, which eliminates the interference of channel estimation using an unbiased training sequence. At the same time, a significant gain is achieved due to the fact that the channel estimation error often has a greater negative impact on performance than the error in the data itself. The second, which is relatively inexpensive, compensates for data distortion if even greater gains are required.

The IQ imbalance model is presented below. An analysis is presented here to show how a conventional channel estimate using an unbiased training sequence can reduce part of the IQ imbalance. Then a direct extension is presented to calculate the IQ imbalance parameters, provided that efficient algorithms are used. Using parameter estimates, a simple compensation algorithm is presented to reduce data distortion. Also presented are the simulation results for UWB WiMedia, as well as suggestions for changing the standard.

Imbalance IQ Model

IQ imbalance occurs when the power (amplitude) or orthogonality (phase) balance between in-phase (I) and quadrature (Q) channels is not maintained. Therefore, the IQ imbalance is characterized by an amplitude imbalance of 2ε and a phase imbalance of 2∆φ.

Signals in the time domain

The complex symbol x is transmitted and received via channels I and Q. In an ideal noise-free channel, the symbol x is received unchanged. But in the presence of an IQ imbalance, a noisy or distorted version is likely to be adopted.

Where

are complex quantities that simulate the imbalance α≈1 and β≈0. The nonlinear model ( 1 ) is linearized through a vector form:

B is an imbalance matrix. The second line is unnecessary because it is a duplicate of the first line. But it provides the same size and type for input and output, so you can concatenate the imbalance blocks in the receiver and transmitter, as described below. The imbalance matrix in the transmitter is determined by B _t , and in the receiver is determined by B _r .

Single Branch Channel

Consider a single branch channel suitable for OFDM. Channel h with one branch in the appropriate matrix form is the following:

принятого сигнала выражается как конкатенация линейных блоковWith an imbalance in the transmitter and receiver, and with an average white Gaussian noise (AWGN) n, the vector form N =

the received signal is expressed as the concatenation of linear blocks

The overall result is that IQ imbalance and channel are combined to form a global channel h ', taking into account unwanted distortions or interference, characterized by global imbalance parameter β'. The global imbalance parameter β 'changes when the channel changes, and it may be necessary to regularly evaluate it.

Then consider the condition in which the symbol x is limited to a given axis (1D) instead of covering the entire complex plane. For example, an axis may be associated with BPSK modulation (DPSK, binary phase shift keying), a real axis, an imaginary axis, or any axis between them. In this case, x * = kx can be written, where k is a complex constant (rotation) and

If x is limited to a unique axis, IQ imbalance disappears, becoming an integral part of the overall response of the channel.

Frequency Signals

While the previous model is applicable to signals in the time domain, we consider below a modification in which the signal x of interest will be given in the frequency domain at frequency f. This signal is transferred to the time domain using a complex tone, xe ^J2πft . After replacing the terms in equation ( 1 ), the following is obtained:

With OFDM, interference resulting from an IQ imbalance does not occur at the same frequency f, but rather at a mirror frequency -f, and vice versa. What is transmitted at a frequency of -f generates interference at a frequency of + f. If the signal x _m is a signal transmitted at a frequency of -f, where the index m denotes a value at a mirror frequency of -f, then at a frequency of -f the following is obtained:

Here we used a generalization of equations in the time domain. The imbalance parameters α and β IQ here represent a frequency function. This models the imbalance due to differences in low-pass filters (in the main passband) or band-pass filters (IF, IF, intermediate frequency) in the system. Paths I and Q cannot have exactly the same filters, and therefore the imbalance changes with frequency. In systems in the time domain, there is this kind of imbalance, but its compensation is associated with very high costs. Equalizers and model expansion are required so that it works with different convolutions on different channels. Thus, in the time domain, a general or average imbalance is used. Systems operating in the frequency domain are configured to take advantage of the flat equalizer structure and model the imbalance for each frequency.

If the output of equations ( 7 ) and ( 8 ) is combined for each subcarrier, the following is observed:

If subcarriers are excluded (automatically processed by FFT), the function of the linear signal model at + f and -f can be written as:

In the model in the frequency domain, the second row is no longer unnecessary. This model works in one frame with a pair of specular frequencies. Channel h with one branch at a frequency f and h _m at a frequency of -f are modeled using a matrix

шумов. Модель из конца в конец представляет собой:AWGN noise n at frequency f and n _m at frequency -f form a vector N =

noise. The end-to-end model is:

where h ', h _m ' are global channel leads, and β ', β _m ' are global imbalance parameters. The imbalance parameters change with changing channels and may require regular evaluation.

Since IQ imbalance generates interference exclusively from the specular frequency, two interesting cases are worth noting. If a signal is not transmitted at the specular frequency or attenuation occurs in the channel, interference will not form. On the other hand, if the signal or channel is strong, interference can be strong. Therefore, with OFDM, the effect of IQ imbalance is more problematic.

Normal Channel Rating

Before testing compensation algorithms, it is shown how half of the problem can be solved at no cost, simply by using an unbiased training sequence. An unbiased training sequence completely eliminates interference from the channel estimate, significantly improving performance. In fact, an error in channel estimation often leads to stronger negative consequences than an error in data, since channel estimation tends to form bias in the signal constellation.

Model (12) is induced using pilot tones. At frequency + f, pilot signal p is transmitted, and at frequency -f, pilot signal p _m . Assuming, without loss of generality, that the pilot signals have a unit norm (the channel transfers effective power), the usual channel estimate at frequency f is obtained by eliminating the rotation using p *:

As a result of averaging several observations of the channel, noise is automatically reduced (for clarity of presentation, elimination of noise rotation is not presented here). For the member β ′ _m p _m * p *, many OFDM systems (e.g. WiMedia UWB) use a training sequence that is simply a repeating character. Therefore, this term does not disappear during averaging. Applying scrambling +1 or -1 to the entire OFDM symbol does not help, since nothing changes when the sign is inverted, both p * and p _m *. Instead, they do the following: after accumulating a number of observations, the amount of works is zeroed:

Often the training sequence consists of an even number of characters, and this is enough to ensure that the sum of each pair is zero

Table 1
Examples of unbiased training sequences P ₂ = jp ₁ The second training symbol represents a 90 degree rotation of the first training symbol P ₂ = p ₁ ,
p _2m = -p _1m For positive frequencies, a fixed pilot signal is maintained; for negative frequencies, the sign is constantly inverted

Examples of simple sequences that satisfy this condition are presented in Table 1. These types of training sequences are designated as unbiased training sequences, because, on the one hand, they form unbiased channel estimates, and, on the other hand, training signals uniformly cover I and Q measurements in complex plane in the time domain. For example, an unbiased training sequence does not concentrate only along the real axis.

, посередине между p_i и p_im. Во временной области пилотный сигналы суммируют до 2a_icos(2πft+θ). Во временной области и в заданном символе OFDM, 2 зеркальных пилотных сигнала охватывают единственное направление, определенное комплексной константой a_i. Если передают L символов, общая (или средняя, или накопленная) мощность в направлении φ представляет собой

. Эта мощность является постоянной в любом направлении φ, если и только если

. При этом достигается равномерный охват в комплексной плоскости.As a proof, we consider a complex scalar number with the unit norm

, in the middle between p _i and p _im . In the time domain, the pilot signals are summed up to 2a _i cos (2πft + θ). In the time domain and in a given OFDM symbol, 2 mirrored pilot signals span a single direction defined by the complex constant a _i . If L symbols are transmitted, the total (either average or accumulated) power in the direction φ is

. This power is constant in any direction φ if and only if

. This ensures uniform coverage in the complex plane.

Imbalance IQ Estimation

After evaluating the global channel h ′, consider the estimate of the parameter β _m ′ of global imbalance. A thorough analysis of equation (12) shows that this parameter can be obtained using a method very similar to the conventional channel estimation method. Thus, β _m ′ can be considered as a “channel” along which the pilot signal p _m * is transmitted. Therefore, by eliminating the rotation at p _m, an imbalance estimate can be obtained. The condition for the unbiased unbalance estimate is identical to equation (14).

In general, using unbiased training sequences and two conventional channel estimates, good estimates are obtained for the channel from the end to the end and the imbalance parameter (Table 2).

table 2
Evaluation Algorithm H ' β _m ' Eliminate rotation on p * Eliminate the rotation by p _m

Adjacent Subcarrier Smoothing

In addition to averaging over neighboring OFDM symbols, the channel estimate can be smoothed over neighboring subcarriers within the same symbol. In OFDM, a cyclic prefix is designed to be short and it is assumed that the channel changes slowly from tone to tone. Similarly, the filters in the RF path must have a short time response, and their frequency response also changes slowly, that is, the IQ imbalance changes slowly in the subcarriers. The same channel smoothing technologies can be used to smooth and improve the estimation of the imbalance parameter. When using unbiased training sequences, there is no interaction between the channel estimate and the unbalance estimate. Each score can be smoothed independently.

If a unique OFDM symbol is used for evaluation, it becomes impossible to find an unbiased training sequence that satisfies equation (14). In this case, a training sequence close to unbiased can be obtained by applying the summation from equation (14) over groups of 2 or more neighboring subcarriers. Then smoothing automatically compensates for all or part of the interference from the mirror frequencies. One solution is to rotate the pilot signal 90 degrees on an adjacent subcarrier (moving in the mirror direction at positive and negative frequencies).

Optimal Estimator

The use of unbiased training sequences and the aforementioned conventional channel estimation leads to the least squares estimator (LS, NK). Of all LS estimators, the minimum significance is represented by the minimum standard error (MMSE, ISCED).

Least Squares Estimator

L gears X _i , L members of N _i noise and L observations Y _i can be correspondingly concatenated by a matrix of size 2 by L:

Then equation (12) becomes the following:

Unknown represents H '. Estimator LS is:

When condition (14) is satisfied, it is easy to verify that XX ^H is the diagonal (the transverse terms disappear). It is proportional to the unit matrix, since the pilot signals normalize to a unit norm. Then

represents exactly four conventional channel estimates with the elimination of a turn by p _i *, p _im , p _im * and p _i, as described in the previous section. Two estimates are obtained for frequency f and two estimates are obtained for mirror frequency -f.

Optimal Estimator

также представляет собой эстиматор LS. Ниже показано, что использование несмещенных обучающих последовательностей позволяет получить отличный эстиматор. Модель (17) можно рассматривать как неизвестную информацию H', переданную за 2 последовательные передачи более чем по 2 векторам (ряды

) в L-мерном пространстве. Мы обозначили как

_j,

_j и

_j соответственно ряд j из

,

и

, где j

{1,2}. Модели (12) и (17) могут быть записаны следующим образомUnbiased training sequences and conventional channel estimates are the LS estimator. But any estimator

also an LS estimator. It is shown below that the use of unbiased training sequences makes it possible to obtain an excellent estimator. Model (17) can be considered as unknown information H 'transmitted in 2 sequential transmissions of more than 2 vectors (series

) in an L-dimensional space. We designated as

_j

_j and

_j respectively row j of

,

and

where j

{1,2}. Models (12) and (17) can be written as follows

₁,

₂, и в которых каждый вектор переносит комплексную информацию амплитуды, оценка которой должна быть получена. Эстиматор LS состоит из проецирования на каждый вектор, параллельно другому вектору, для того, чтобы исключить интерференцию. Очень хороший результат получают, когда 2 вектора являются ортогональными, то есть когда скалярное произведение (14) равно нулю. Несмещенные обучающие последовательности по определению представляют собой обучающие последовательности, которые подтверждают это условие. В других последовательностях используют неортогональные векторы и им недостает рабочих функций угла между векторами

₁,

₂. В множестве систем OFDM в настоящее время используют очень скудное число типов обучающих последовательностей, где

являются коллинеарными, и при этом невозможно правильно оценить 4 записи в H'. Такие обучающие последовательности проявляют тенденцию оценки более шумных вариантов каналов h' и h'_m.2 gears were used here, in each of which 2 vectors were used

₁

₂ , and in which each vector carries complex amplitude information, an estimate of which must be obtained. The LS estimator consists of projecting onto each vector, parallel to another vector, in order to exclude interference. A very good result is obtained when 2 vectors are orthogonal, that is, when the scalar product ( 14 ) is equal to zero. Unbiased training sequences, by definition, are training sequences that support this condition. In other sequences, non-orthogonal vectors are used and they lack the working functions of the angle between the vectors

₁

₂ . Many OFDM systems currently use a very meager number of training sequence types, where

are collinear, and it is not possible to correctly evaluate 4 entries in H '. Such training sequences tend to evaluate the noisier channel options h 'and h' _m .

, это представляет собой матрицу размером 2×2, то есть содержащую 4 значения ошибки. Каждое значение может быть изолировано путем умножения левой и правой стороны на комбинацию векторов

и

. Если предположить, что

представляет собой единичную матрицу, или, в более общем случае, диагональную матрицу с элементами σ² и σ_m ², можно показать, что MSE

и

соответственно представляют собой первый и второй диагональные элементы

И для

и

MSE соответственно представляет собой первый и второй диагональные элементы

To calculate the mean square errors (MSE, RMS), the estimation error is

, this is a 2 × 2 matrix, i.e. containing 4 error values. Each value can be isolated by multiplying the left and right sides by a combination of vectors

and

. Assuming that

is a unit matrix, or, in a more general case, a diagonal matrix with elements σ ² and σ _m ² , it can be shown that MSE

and

respectively represent the first and second diagonal elements

And for

and

MSE respectively represents the first and second diagonal elements

. Теперь задача состоит в том, чтобы определить

, который сводит к минимуму

которое подвергают ограничению, состоящему в том, что общая мощность пилотного сигнала остается постоянной, то есть

. Используя собственное разложение, задача может быть записана как минимизация ∑1/λ_j при условии, что ∑λ_j является константой. Задачу решают с помощью множителей Лагранжа, и она типично имеет оптимальное решение, когда все собственные значения равны друг другу. Это означает, что

пропорционально единичной матрице.The total MSE is

. Now the challenge is to determine

which minimizes

which is subjected to the limitation that the total power of the pilot signal remains constant, i.e.

. Using our own expansion, the problem can be written as minimizing ∑1 / λ _j provided that ∑λ _j is a constant. The problem is solved with the help of Lagrange multipliers, and it typically has an optimal solution when all eigenvalues are equal to each other. It means that

in proportion to the identity matrix.

The overall MSE value has been minimized, and the resulting MSE value per element is either σ ² / L or σ _m ² / L. But this MSE per element is probably the best that can be obtained, even if they use a single vector transmission. MSE is unlikely to be improved for 2 vector transmissions, and therefore the MSE per element has been minimized. Unbiased training sequences plus a normal channel estimator represent the MMSE of all LS estimators.

Imbalance IQ Compensation

If the increase from the unbiased channel estimate is not enough, the unbalance IQ parameters can be estimated (as described above) and can be used to compensate for data distortions. The estimate H 'is obtained in model (12), Y = H'X + N. Now the focus is set on unknown X data. The model is used the same as in any channel with 2 leads, with cross-correlation. Here you can adapt any channel alignment algorithm. A simple alignment algorithm has been found to be suitable for the ubiquitous QAM with bit interleaving and channel attenuation.

The only problem when using the approach (ZF, OH), vanishing H ' ^-1 Y = X + H' ^-1 N, is that it increases noise when the mirror channel is weak until complex ones are taken into account painted noises. ZF is used in this solution, but only when the mirror channel is not weak. In equation (12), when x _{m is} replaced by its value, the following is obtained:

представляет собой увеличение шумов. Примечание: предполагается, что член дисбаланса второго порядка β'*β_m'<<h'h_m'*. Когда такая аппроксимация недействительна, рассматривают скорректированный канал , что приводит к получению точной оценки канала и параметров дисбаланса.Where

represents an increase in noise. Note: it is assumed that the second-order imbalance term is β '* β _m '<<h'h _m '*. When such an approximation is not valid, consider the adjusted channel , which leads to an accurate channel estimate and imbalance parameters.

Basically, ZF technology consists in calculating:

By subtracting the magnitude of the mirror frequency (B _m '/ h _m ') y _m from the received signal y, a simple channel model without IQ imbalance is obtained. The rest of the decoding chain remains unchanged.

Such a solution works well if the increase in noise is weaker than the initial interference due to IQ imbalance, that is, | n '| ² <| β _m 'x _m * | ² . If not, then use the original signal y instead of z with corrected imbalance. In this case, it is not necessary to carry out an estimate n 'in order to make a decision. Then you can choose a reliable improvement based on the average. Thus, given the expected values:

When the signal-to-noise ratio SNR _{m of the} mirror frequency is greater than 1, an unbalanced correction term z is used. Otherwise, the original signal y is retained. Due to inaccurate channel estimation and imbalance, it is safer to use a larger SNR, for example, SNR _m > 2 works well for WiMedia UWB. It should be noted that SNR _m can usually be obtained from the global SNR using the formula SNR _m = | h _m '| ² SNR.

Table 3 summarizes the ZF algorithm with the exception of increased noise.

Table 3
Compensation Algorithm SNR _m <1 + δ SNR _m > 1 + δ z = y z = y- (β _m '/ h _m ') y _m

Simulation results

In FIG. 12 shows the performance achieved by using the algorithms described above for the WiMedia UWB standard. The highest data transfer rate, 480 Mbps, was modeled in the CM2 channel model in accordance with the IEEE 802.15.3 standard (indoor picocell at a distance of about 4 meters). Shielding and switching lanes disabled. The IQ imbalance remained constant and equal to 2ε = 10% (0.8 dB) in amplitude and 2Δφ = 10 degrees in phase. The same imbalance was present in the transmitter and receiver. The figure shows the burst error rate (PER, chop) as a function of Eb / No. Performance deteriorated rapidly without any form of compensation. Table 4 presents the losses for various algorithms with respect to the ideal case.

Table 4
WiMedia UWB: IQ Imbalance Losses at PER 10 ^-2 Current standard Unbiased training Compensation 3.1 dB 1.1 dB 0.35 dB

End-to-end IQ imbalance and channel are combined to form a 2 by 2 global channel matrix. Using an unbiased training sequence provides significant cost-free benefits. Unbiased training sequences automatically compensate for self-generated end-to-end interference from channel estimation. In addition, such training sequences are ideal for evaluating IQ imbalance parameters, and a simple algorithm is set up to compensate for data distortions: zeroing insignificant coefficients with the exception of noise amplification.

WiMedia UWB, in particular, takes advantage of the following improvements: a conventional 6-character offset training sequence transmitted exclusively on channel I can be split into 2 halves to form an unbiased sequence. The first 3 characters are transmitted on channel I, and the last 3 characters are transmitted on channel Q. By uniformly covering the complex plane, an unbiased training sequence is formed with significant benefits for high data rates. For backward compatibility, this circuitry can be reserved for high data rate modes and can be transmitted as signals using beacon signals, or the type of training sequence can be blindly detected.

In OFDMA (e.g., WiMAX), the subcarriers f and -f can be assigned to different users. Significant interference can occur if power control puts one user at a high power level. Therefore, a good idea is to mix the pilot signals of different users into mirror subcarriers. These pilot signals must satisfy the criteria for an unbiased training sequence. Each user automatically gets an advantage without any extra effort. Pilot signals can switch to different locations while maintaining mirror positions.

Time domain formulas can be expanded to code division multiple access (CDMA, CDMA) with a comb equalizer in which multiple channels are combined with one branch. Unbiased training sequences automatically improve channel assignment for assignment. A simple unbiased training sequence for CDMA consists of a constant rotation of complex characters by 90 degrees.

In FIG. 13 is a flowchart illustrating a method for transmitting an unbiased training sequence for transmitting data. Although this method is presented, for clarity, as a sequence of numbered steps, such numbering does not necessarily dictate the order in which these steps are performed. It should be understood that some of these steps may be skipped, may be performed in parallel, or may be performed without having to maintain a strict sequence order. The method begins at block 1300.

At step 1302, an unbiased training sequence is generated in a quadrature modulated transmitter, wherein the unbiased training sequence represents a uniform accumulation of power uniformly distributed in the complex plane, as defined above. At 1304, an unbiased training sequence is transmitted. The terms “generate,” “output,” and “multiply” refer to processing that can be performed using computer-readable software instructions, hardware, or a combination of software and hardware.

In one aspect, generating an unbiased training sequence in step 1302 includes sub-steps. At step 1302a, training information in the time domain is transmitted via the in-phase (I) modulation path having stored power. At step 1302b, training information in the time domain is transmitted via the quadrature (Q) modulation path having a total power equal to the power of the modulation path I.

In another aspect, generating an unbiased training sequence in step 1302 includes the following sub-steps. At step 1302c, a pair of signals is generated including a reference signal (p) with a complex value at a frequency + f and a mirror signal (p _m ) with a complex value at a frequency -f. Step 1302d reduces the product (p · p _m ) to zero.

For example, i instances of the reference signal (p) and the mirror signal (p _m ) can be generated, and the sum of the products (p _i · p _im ) is reduced to zero. The generation of i instances of the reference signal and the mirror signal may include generating values p and p _{m of a} pair of signals that vary for each instance. In one aspect, the sum of the products (p _i · p _im ) can be reduced to zero by generating information as a complex value that remains constant for each instance to represent p. To represent p _m, information can be generated as a complex value that rotates 180 degrees in each instance.

As another example, i instances of a reference signal (p) and a mirror signal (p _m ) can be generated, and a product (p _i · p _im ) can be generated for each instance. Then pairs can be formed from the copies, and the sum of the pieces will be reduced to zero for each coupled copy.

In one aspect, generating an unbiased training sequence in step 1302 includes generating P pilot symbols per symbol period, in a plurality of symbol periods. Then, in step 1303, (N-P) quadrature modulated symbols of the communication data are generated for the symbol period. The transmission of the unbiased training sequence at 1304 includes the simultaneous transmission of N symbols per symbol period, in a plurality of symbol periods.

In another aspect, at 1303, quadrature modulated communication data is generated. At 1304, an unbiased training sequence is transmitted in a first set of symbol periods, followed by quadrature modulated communication data in a second set of symbol periods.

In another aspect, at 1302, a time sequence of symbols of a complex plane with equal accumulated power in a plurality of directions in a complex plane is generated. Thus, the temporal sequence of symbols has an accumulated power associated with real axis information in the time domain and an equal accumulated power associated with imaginary axis information in the time domain. Then, at 1304, a temporal sequence of symbols in a plurality of symbol periods is transmitted. In another aspect, at step 1302, an unbiased training sequence, expressed as a time sequence of I complex characters (a) in the time domain, is transmitted as follows:

sum a _i (k) · a _i (k) = 0;

where k is the number of samples per character period. In one aspect, at 1302, symbols are generated in a plurality of messages having equal power in a plurality of directions of a complex plane accumulated over a plurality of messages.

The block diagram described above can also be interpreted as an expression of a computer-readable storage medium on which instructions for transmitting an unbiased training sequence for transmitting data are stored. Instructions for transmitting a rotating training signal could correspond to steps 1300-1304, as explained above.

In FIG. 14 is a flowchart illustrating a method for calculating an unbiased channel estimate. The method begins at block 1400. At block 1402, an unbiased training sequence is received at a quadrature demodulation receiver, the unbiased training sequence having predetermined reference signals (p) representing a uniform stored power uniformly distributed in the complex plane. At 1404, an unbiased training sequence is processed to generate processing symbols (y) representing complex plane information in an unbiased training sequence. At step 1406, the processed symbols (y) are multiplied by the conjugate value of the corresponding reference signals (p *). At 1408, an unbiased channel estimate (h _u ) is obtained.

In one aspect, receiving an unbiased training sequence at 1402 includes receiving an unbiased training sequence with a plurality of simultaneously received predetermined reference signals (p _n ). Generating a processed symbol (y) in step 1404 includes generating a plurality of processed symbols (y _n ) from a corresponding plurality of reference signals. Multiplying the processed symbol (y) by the conjugate value of the reference signal (p *) in step 1406 includes multiplying each processed symbol by its corresponding conjugate value of the reference signal. Then, at 1408, a channel estimate is obtained by obtaining a plurality of channel estimates (h _un ) and averages the channel estimate (h _un ) for each value of n.

In another aspect, in step 1402, an unbiased training sequence is received by receiving training information in the time domain through the in-phase modulation path (I) having accumulated power, and training information in the time domain is received via the quadrature modulation path (Q) having a total power equal to (as defined above) the power of the path I modulation.

In another aspect, at 1402, an unbiased training sequence is received with a time sequence of n predetermined reference signals (p _n ) having accumulated power associated with real axis information in the time domain and with an equal amount of accumulated power associated with imaginary axis information in the time area. At 1404, a time sequence of n processed symbols (y _n ) is generated from a time sequence of reference signals. At 1406, each processed symbol is multiplied in a time sequence by its corresponding conjugate value of the reference signal. Then, obtaining the channel estimate h in step 1408 includes: obtaining a time sequence of n channel estimates (h _un ); and averaging n channel estimates.

In one aspect, in step 1402, an unbiased training sequence is received as a pair of signals including a complex value reference signal (p) at a frequency + f and a complex value mirror signal (p _m ) at a frequency -f, where the product (p · p _m ) equals zero. For example, i instances of the reference signal (p) and the mirror signal (p _m ) can be taken, where the sum of the product (p _i · p _im ) is zero. In addition, the values of p and p _{m are} pairs of signals that vary for each instance. In another embodiment, the sum of the products (p _i · p _im ) is zeroed out by accepting the information as a complex value that remains constant for each instance representing p; and accepting the information as a complex value that rotates 180 degrees for each instance representing p _m .

As another example, i instances of the reference signal (p) and the mirror signal (p _m ) can be taken, and the product (p _i · p _im ) is generated for each instance. The instances are then paired, and the sum of the products for each paired copy is reduced to zero.

In one aspect, at 1402, an unbiased training sequence is received as P pilot symbols per symbol period, in a plurality of symbol periods, and at 1408, P unbiased pilot channel estimates are obtained. At 1403, simultaneously receive (NP) quadrature modulated communication data symbols in each symbol period. At step 1405, a processed symbol (y _c ) is generated for the communication data in each symbol period. At step 1410, channel estimates are extrapolated for each processed symbol (y _c ) obtained from unbiased estimates of the reference channel. At step 1412, each processed symbol (y _c ) is multiplied by an extrapolated channel estimate to output the transmitted symbol (x).

In another aspect, at 1403, quadrature modulated communication data is received in symbol periods after receiving an unbiased training sequence. At 1405, a processed symbol (y _c ) is generated for each symbol of communication data, and at 1414, each processed symbol is multiplied by an unbiased channel estimate to extract the transmitted symbol (x).

In another aspect, at 1402, a time sequence of a complex plane with equal accumulated power in a plurality of directions in the complex plane is received. Alternatively, an unbiased training sequence can be expressed as a time sequence of i complex characters (a) in the time domain as follows:

sum a _i (k) -a _i (k) = 0,

where k represents the number of samples per character period.

In one aspect, receiving an unbiased training sequence at 1402 includes receiving symbols in a plurality of messages having equal power in a plurality of directions of the complex plane, as accumulated over a plurality of messages.

The flowchart described above can also be interpreted as an expression of a computer-readable storage medium on which instructions for calculating an unbiased channel estimate are stored. Instructions for calculating an unbiased channel estimate may correspond to steps 1400-1414, as explained above.

Systems, methods, devices, and processors were presented that provide the ability to transmit and receive quadrature modulated unbiased training sequences in a data transmission device and to calculate unbiased channel estimates. Examples of specific protocols and data transmission formats have been presented to illustrate the invention. However, the invention is not limited to just these examples. Other changes and embodiments of the invention will be apparent to those skilled in the art.

Claims (71)

1. A method for transmitting a training sequence, comprising the steps of: generating a training sequence in a quadrature modulated transmitter, the training sequence representing a plurality of at least three characters, each symbol representing a complex value having an in-phase component value and a quadrature component value; moreover, the sum of the squares of the values of the in-phase components is equal to the sum of the squares of the values of the quadrature components, and transmit the training sequence. 2. The method according to claim 1, in which the transmission of the training sequence includes the transmission of: training information in the time domain containing the values of the in-phase components through the common-mode (I) modulation path having an accumulated power equal to the sum of the squares of the values of the in-phase components; and training information in a time domain containing quadrature component values through a quadrature (Q) modulation path having an accumulated power equal to the sum of the squares of the values of the quadrature components. 3. The method according to claim 1, wherein generating the training sequence comprises generating a first training sequence comprising a plurality of at least three reference symbols, each symbol representing a reference complex value, and a second training sequence comprising a plurality of at least three corresponding mirror symbols, with each corresponding mirror symbol representing a corresponding mirror complex value, the transmission of the training sequence win transmitting a first training sequence at frequency + f and transmitting the second training sequence in the frequency -f, and thus the product of each support and the corresponding complex value mirror integrated value is zero. 4. The method according to claim 1, wherein generating the training sequence comprises generating a first training sequence comprising a plurality of at least three reference symbols, each symbol representing a complex reference value, and a second training sequence comprising a plurality of at least three corresponding mirror symbols, with each corresponding mirror symbol representing a corresponding mirror complex value, while the transmission of the training sequence It comprises transmitting a first training sequence at frequency + f and transmitting the second training sequence in the frequency -f, and wherein the sum of products of complex values and the reference mirror corresponding complex values is zero. 5. The method according to claim 4, in which each reference symbol is different from neighboring reference symbols. 6. The method according to claim 4, in which each reference symbol is the same as adjacent reference symbols. 7. The method of claim 1, further comprising the steps of: generating quadrature modulated communication data and transmitting quadrature modulated communication data after transmitting the training sequence. 8. The method according to claim 1, in which the training sequence has an odd number of characters. 9. The method according to claim 1, wherein the training sequence comprises a first plurality of first characters, each of a plurality of first characters being the same, and a second plurality of second characters, each of a plurality of second characters being the same, and the first characters being different second characters. 10. The method of claim 1, wherein the training sequence comprises a first symbol having a first power and a second symbol having a second power, wherein the first power and the second power are different. 11. A method for calculating a channel estimate, comprising the steps of: receiving an unbiased training sequence in a quadrature demodulation receiver, the training sequence representing a plurality of at least three characters, each symbol representing a complex value having a common-mode component value and a quadrature component value ; moreover, the sum of the squares of the values of the in-phase components is equal to the sum of the squares of the values of the quadrature components, and a channel estimate (h _u ) is obtained based on the received training sequence. 12. The method according to claim 11, in which the reception of the training sequence includes receiving: training information in the time domain containing the values of the common-mode components, through the common-mode (I) modulation path having an accumulated power equal to the sum of the squares of the common-mode components; and training information in a time domain containing quadrature component values through a quadrature (Q) modulation path having an accumulated power equal to the sum of the squares of the values of the quadrature components. 13. The method according to claim 11, wherein receiving the training sequence comprises receiving a first training sequence comprising a plurality of at least three reference symbols at a frequency + f, each symbol representing a reference complex value, and receiving a second training sequence comprising a plurality of at least three corresponding mirror symbols at a frequency of -f, each corresponding mirror symbol representing a corresponding mirror complex value, the product of each op The complex complex value and the corresponding mirror complex value are zero. 14. The method of claim 11, wherein receiving the training sequence comprises receiving a first training sequence comprising a plurality of at least three reference symbols at a frequency + f, each symbol representing a reference complex value, and receiving a second training sequence comprising a plurality of at least three corresponding mirror symbols at a frequency of -f, each corresponding mirror symbol representing a corresponding mirror complex value, wherein the sum of the products of the supports complex complex values and the corresponding mirror complex values is zero. 15. The method according to 14, in which each reference symbol is different from neighboring reference symbols. 16. The method of claim 14, wherein each reference symbol is the same as adjacent reference symbols. 17. The method of claim 11, further comprising the steps of: receiving quadrature modulated symbols of the communication data and extracting a transmitted symbol for each symbol of the communication data based on the channel estimate. 18. The method according to claim 11, further comprising the steps of: receiving quadrature modulated communication data symbols after receiving the training sequence; generating a processed symbol for each symbol of the communication data and multiplying each processed symbol by a channel estimate to extract the transmitted symbol. 19. The method according to claim 11, in which the training sequence has an odd number of characters. 20. The method according to claim 11, in which the training sequence contains the first plurality of first characters, each of the plurality of first characters are the same, and the second plurality of second characters, each of the plurality of second characters are the same, and the first characters are different from second characters. 21. The method of claim 11, wherein the training sequence comprises a first symbol having a first power and a second symbol having a second power, wherein the first power and second power are different. 22. A system for transmitting a training sequence, comprising: a processor configured to generate a training sequence representing a plurality of at least three characters, each character representing a complex value having an in-phase component value and a quadrature component value; wherein the sum of the squared values of the in-phase components is equal to the sum of the squared values of the quadrature components; and a transmitter configured to transmit the training sequence. 23. The system of claim 22, wherein the transmitter is configured to transmit: training information in the time domain through an in-phase (I) modulation path having an accumulated power equal to the sum of the squares of the in-phase components; and
training information in the time domain through a quadrature (Q) modulation path having an accumulated power equal to the sum of the squared values of the quadrature components. 24. The system of claim 22, wherein the processor generates a training sequence by generating a first training sequence comprising a plurality of at least three reference symbols, each symbol representing a complex reference value, and a second training sequence comprising a plurality of at least three corresponding mirror symbols, wherein each corresponding mirror symbol represents a corresponding mirror complex value, wherein the transmitter transmits teaching th sequence by transmitting a first training sequence at frequency + f and transmitting the second training sequence in the frequency -f, and thus the product of each support and the corresponding complex value mirror integrated value is zero. 25. The system of claim 22, wherein the processor generates a training sequence through a first training sequence comprising a plurality of at least three reference symbols, each symbol representing a complex reference value, and a second training sequence comprising a plurality of at least three corresponding mirror symbols, each corresponding mirror symbol representing a corresponding mirror complex value, the transmitter transmitting the training sequence lnost by transmitting a first training sequence at frequency + f and transmitting the second training sequence in the frequency -f, and wherein the sum of products of complex values and the reference mirror corresponding complex values is zero. 26. The system of claim 25, wherein each reference symbol is different from neighboring reference symbols. 27. The system of claim 25, wherein each reference symbol is the same as adjacent reference symbols. 28. The system of claim 22, wherein the processor is further configured to generate quadrature modulated communication data and wherein the transmitter is further configured to transmit quadrature modulated communication data after the training sequence. 29. The method according to item 22, in which the training sequence has an odd number of characters. 30. The method according to item 22, in which the training sequence contains the first plurality of first characters, each of the plurality of first characters are the same, and the second plurality of second characters, each of the plurality of second characters are the same and the first characters are different from second characters. 31. The method of claim 22, wherein the training sequence comprises a first symbol having a first power and a second symbol having a second power, wherein the first power and the second power are different. 32. A system for calculating a channel estimate, comprising: a quadrature demodulation receiver configured to receive a training sequence representing a plurality of at least three characters, each symbol representing a complex value having an in-phase component value and a quadrature component value, the sum of the squared values in-phase components is equal to the sum of the squared values of the quadrature components; and a processor configured to obtain a channel estimate based on the received training sequence. 33. The system of claim 32, wherein the receiver is configured to receive the training sequence by receiving: training information in the time domain through the in-phase (I) demodulation path having an accumulated power equal to the sum of the squares of the in-phase components; and training information in the time domain through a quadrature (Q) demodulation path having an accumulated power equal to the sum of the squared values of the quadrature components. 34. The system of claim 32, wherein the receiver is configured to receive a training sequence by receiving a first training sequence containing a plurality of at least three reference symbols at a frequency + f, each symbol representing a reference complex value, and receiving a second training sequence, comprising a plurality of at least three corresponding mirror symbols at a frequency of -f, each corresponding mirror symbol representing a corresponding mirror complex value of, the product of each of the reference values and the corresponding integrated mirror integrated value is zero. 35. The system of claim 32, wherein the receiver is configured to receive a training sequence by receiving a first training sequence containing a plurality of at least three reference symbols at a frequency + f, each symbol representing a reference complex value, and receiving a second training sequence, comprising a plurality of at least three corresponding mirror symbols at a frequency of -f, each corresponding mirror symbol representing a corresponding mirror complex value s, the sum of products of complex values of the reference mirror and the respective complex values of zero. 36. The system of claim 35, wherein each reference symbol is different from neighboring reference symbols. 37. The system of claim 35, wherein each reference symbol is the same as adjacent reference symbols. 38. The system of claim 32, wherein the receiver is further configured to receive quadrature modulated symbols of communication data and in which the processor is further configured to extract a transmitted symbol for each symbol of communication data based on the channel estimate. 39. The system of claim 32, wherein the receiver is configured to receive quadrature modulated communication data after receiving the training sequence and in which the processor is configured to generate a processed symbol for each symbol of communication data and multiply each processed symbol by a channel estimate to extract the transmitted symbol. 40. The system of claim 32, wherein the training sequence has an odd number of characters. 41. The system of claim 32, wherein the training sequence comprises a first plurality of first characters, each of a plurality of first characters being the same, and a second plurality of second characters, each of a plurality of second characters being the same and the first characters being different second characters. 42. The system of claim 32, wherein the training sequence comprises a first symbol having a first power and a second symbol having a second power, wherein the first power and second power are different. 43. A computer-readable storage medium that stores instructions for transmitting a training communication sequence, instructions comprising: generating a training sequence in a quadrature modulated transmitter, the training sequence representing a plurality of at least three characters, each character representing a complex value having a value the in-phase component and the value of the quadrature component, while the sum of the squared values of the in-phase components is equal to the sum of the squared eny quadrature components; and transmission of the training sequence. 44. A computer-readable storage medium that stores instructions for calculating an unbiased channel estimate, instructions comprising: receiving a training sequence in a quadrature demodulation receiver, the training sequence representing a plurality of at least three characters, each character representing a complex value having a value the in-phase component and the value of the quadrature component, the sum of the squares of the values of the in-phase components is equal to the sum of the squares of the values of the quadrature Components; and obtaining a channel estimate based on the received training sequence. 45. An apparatus for transmitting an unbiased training communication sequence, comprising: generating means for generating a training sequence representing a plurality of at least three characters, each character representing a complex value having a common-mode component value and a quadrature component value, wherein the sum of the squares of the common-mode values components equal to the sum of the squared values of the quadrature components; and transmission means for transmitting the training sequence. 46. The device according to item 45, in which the transmission means includes: means for transmitting training information in the time domain through the common-mode (I) modulation path having an accumulated power equal to the sum of the squares of the common-mode components; and means for transmitting training information in the time domain through a quadrature (Q) modulation path having an accumulated power equal to the sum of the squares of the values of the quadrature components. 47. The apparatus of claim 45, wherein the generating means comprises means for generating a first training sequence comprising a plurality of at least three reference symbols, each symbol representing a complex reference value, and means for generating a second training sequence comprising a plurality of at least three corresponding mirror symbols, each corresponding mirror symbol representing a corresponding mirror complex value, the means for The transmission contains means for transmitting the first training sequence at a frequency of + f and means for transmitting a second training sequence at a frequency of -f, while the product of each reference complex value and the corresponding mirror complex value is zero. 48. The apparatus of claim 45, wherein the generating means comprises means for generating a first training sequence comprising a plurality of at least three reference symbols, each symbol representing a complex reference value, and means for generating a second training sequence comprising a plurality of at least three corresponding mirror symbols, each corresponding mirror symbol representing a corresponding mirror complex value, chi comprises means for transmitting a first training sequence at frequency + f and means for transmitting the second training sequence in the frequency -f, and wherein the sum of products of complex values and the reference mirror corresponding complex values is zero. 49. The device according to p, in which each reference symbol is different from neighboring reference symbols. 50. The device according to p, in which each reference symbol is the same as adjacent reference symbols. 51. The apparatus of claim 45, wherein the generating means comprises means for generating quadrature modulated communication data and wherein the transmission means comprises means for transmitting quadrature modulated communication data after transmitting the training sequence. 52. The device according to item 45, in which the training sequence has an odd number of characters. 53. The device according to item 45, in which the training sequence contains the first plurality of first characters, each of the plurality of first characters are the same, and the second plurality of second characters, each of the plurality of second characters are the same and the first characters are different from second characters. 54. The device according to item 45, in which the training sequence contains a first character having a first power, and a second character having a second power, while the first power and second power are different. 55. An apparatus for calculating a channel estimate, comprising: reception means for receiving a training sequence representing a plurality of at least three characters, each symbol representing a complex value having a common-mode component value and a quadrature component value, wherein the sum of the squares of the common-mode components is the sum of the squares of the values of the quadrature components; and acquisition means for obtaining a channel estimate based on the received training sequence. 56. The device according to item 55, in which the receiving means includes: means for receiving training information through the common-mode (I) demodulation path in the time domain having an accumulated power equal to the sum of the squares of the values of the common-mode components; and
means for receiving training information through a quadrature (Q) demodulation path in a time domain having an accumulated power equal to the sum of the squares of the values of the quadrature components. 57. The apparatus of claim 55, wherein the reception means comprises means for receiving a first training sequence comprising a plurality of at least three reference symbols at a frequency + f, each symbol representing a reference complex value, and means for receiving a second training sequence, comprising a plurality of at least three corresponding mirror symbols at a frequency of -f, each corresponding mirror symbol representing a corresponding mirror complex value, wherein e each reference integrated value and the respective complex value mirror is zero. 58. The device according to § 55, in which the reception means comprises means for receiving a first training sequence comprising a plurality of at least three reference symbols at a frequency + f, each symbol representing a reference complex value, and means for receiving a second training sequence, comprising a plurality of at least three corresponding mirror symbols at a frequency of -f, each corresponding mirror symbol representing a corresponding mirror complex value, the sum of The knowledge of the reference complex values and the corresponding mirror complex values is zero. 59. The device according to § 58, in which each reference symbol is different from neighboring reference symbols. 60. The device according to § 58, in which each reference symbol is the same as adjacent reference symbols. 61. The device according to § 55, in which the receiving means comprises means for receiving quadrature modulated symbols of communication data and in which the means for receiving comprises means for extracting a transmitted symbol (x) for each symbol of communication data based on a channel estimate. 62. The device according to item 55, in which the receiving means comprises means for receiving quadrature modulated symbols of communication data and the receiving means comprises means for generating a processed symbol ( _s ) for each symbol of communication data and means for multiplying each processed symbol by a channel estimate to extract the transmitted character (x). 63. The device according to § 55, in which the training sequence has an odd number of characters. 64. The apparatus of claim 55, wherein the training sequence comprises a first plurality of first characters, each of a plurality of first characters being the same, and a second plurality of second characters, each of a plurality of second characters being the same and the first characters being different second characters. 65. The apparatus of claim 55, wherein the training sequence comprises a first symbol having a first power and a second symbol having a second power, wherein the first power and second power are different. 66. A processing device for transmitting a training sequence, comprising: a generating module configured to generate a training sequence representing a plurality of at least three characters, each character representing a complex value having a common-mode component value and a quadrature component value, wherein the sum of the squared values in-phase components is equal to the sum of the squared values of the quadrature components. 67. A processing device for calculating a channel estimate, comprising:
a receiver module configured to receive a training sequence representing a plurality of at least three characters, each symbol representing a complex value having a common-mode component value and a quadrature component value, wherein the sum of the squares of the common-mode components is equal to the sum of the squares of the values of the quadrature components; and a acquisition module configured to estimate the channel based on the received training sequence. 68. A method for calculating an unbiased channel estimate, comprising the steps of: receiving an unbiased training sequence in a quadrature demodulation receiver, the unbiased training sequence including predetermined reference signals (p) representing a uniform stored power uniformly distributed in a complex plane; processing an unbiased training sequence, generating a sequence of processed symbols (y) representing complex plane information in an unbiased training sequence; multiply each processed symbol (y) by the conjugate value of the corresponding reference signal (p *) and obtain an unbiased channel estimate (h _u ), while receiving an unbiased training sequence includes receiving an unbiased training sequence with many simultaneously received predetermined reference signals (p _n ); wherein generating the processed symbol (y) includes generating a plurality of processed symbols (y _n ) from the corresponding plurality of reference signals; moreover, the multiplication of the processed symbol (y) by the conjugate value of the reference signal (p *) includes the multiplication of each processed symbol by its corresponding conjugate value of the reference signal, and obtaining a channel estimate includes: obtaining a plurality of channel estimates (h _un ) and averaging (h _un ) channel for each value of n. 69. A system for calculating an unbiased channel estimate, comprising: a receiver configured to receive an unbiased training sequence, wherein the unbiased training sequence includes predetermined reference signals (p) representing a uniform stored power uniformly distributed in a complex plane; a processor configured to process an unbiased training sequence generating a sequence of processed symbols (y) representing complex plane information in an unbiased training sequence; a multiplier configured to multiply each processed symbol (y) by the conjugate value of the corresponding reference signal (p *) and receiving means configured to obtain an unbiased channel estimate (h _u ), while the receiver is configured to receive an unbiased training sequence by receiving an unbiased training sequence with a plurality of simultaneously received predetermined reference signals (p _n ); wherein the processor is configured to generate a processed symbol (y) by generating a plurality of processed symbols (y _n ) from a corresponding plurality of reference signals; wherein the multiplier is configured to multiply the processed symbol (y) by the conjugate value of the reference signal (p *) by multiplying each processed symbol by its corresponding conjugate value of the reference signal, and the obtaining means is configured to obtain a channel estimate by: obtaining a plurality of channel estimates (h _un ) and averaging the channel estimate (h _un ) for each value of n. 70. A computer-readable storage medium that stores instructions for transmitting a training communication sequence, instructions comprising: receiving an unbiased training sequence in a quadrature demodulation receiver, wherein the unbiased training sequence includes predetermined reference signals (p) representing a uniform stored power uniformly distributed in the complex plane; processing an unbiased training sequence, generating a sequence of processed characters (y) representing complex plane information in an unbiased training sequence; multiplying each processed symbol (y) by the conjugate value of the corresponding reference signal (p *) and obtaining an unbiased channel estimate (h _u ), while receiving an unbiased training sequence includes receiving an unbiased training sequence with many simultaneously received predetermined reference signals (p _n ); wherein generating the processed symbol (y) includes generating a plurality of processed symbols (y _n ) from the corresponding plurality of reference signals; moreover, the multiplication of the processed symbol (y) by the conjugate value of the reference signal (p *) includes the multiplication of each processed symbol by its corresponding conjugate value of the reference signal, and obtaining a channel estimate includes: obtaining a plurality of channel estimates (h _un ) and averaging (h _un ) channel for each value of n. 71. A system for calculating an unbiased channel estimate, comprising: means for receiving an unbiased training sequence, wherein the unbiased training sequence includes predetermined reference signals (p) representing a uniform stored power uniformly distributed in a complex plane; means for processing an unbiased training sequence generating a sequence of processed symbols (y) representing complex plane information in an unbiased training sequence; means for multiplying each processed symbol (y) by the conjugate value of the corresponding reference signal (p *) and means for obtaining an unbiased channel estimate (h _u ), while the means for receiving comprises means for receiving an unbiased training sequence with a plurality of simultaneously received predetermined reference signals (p _n ); wherein the processing means comprises means for generating a plurality of processed symbols (y _n ) from the corresponding plurality of reference signals; moreover, the means for multiplying comprises means for multiplying the processed symbol (y) by the conjugate value of the reference signal (p *) by multiplying each processed symbol by its corresponding conjugate value of the reference signal and the means for obtaining comprises: means for obtaining a plurality of estimates (h _un ) channel and means for averaging estimates (h _un ) of the channel for each value of n.

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