WO2008151518A1

WO2008151518A1 - The method and device for detecting information in the ofdm system

Info

Publication number: WO2008151518A1
Application number: PCT/CN2008/001119
Authority: WO
Inventors: Zhendong Luo; Dawei Huang
Original assignee: Lucent Technologies Inc.
Priority date: 2007-06-08
Filing date: 2008-06-10
Publication date: 2008-12-18
Also published as: CN101320994A; CN101320994B

Abstract

The method for detecting the information in the OFDM system includes the following steps. A) The vector including several received signals is inputted into the receiver, when one symbol of several symbols to be detected is equal to one of candidate values, the vector would obey the multi-dimensional complex Gaussian distribution when the conditions about the channels and the other data are unknown. B) The posterior probability of every data that is equal to every candidate values can be calculated with the probability density function of the multi-dimensional complex Gaussian distribution. So the posterior probability of the data can be calculated accurately when the channel conditions would not be estimated. The performance of the detection can be close to the optimum results, and the complexity is in direct ratio with the number of carriers.

Description

Signal detection method and device for OFDM system

The present invention relates to signal detection in an OFDM system, and in particular to a detection method and apparatus for an OFDM system capable of generating a posterior probability of transmitted data symbols directly from a received signal with higher computational efficiency. Background technique

Due to the use of Fast Fourier Transform (FFT) and Cyclic Prefix (CP), Orthogonal Frequency Division Multiplexing (OFDM) technology can effectively combat multipath fading with low complexity. Currently, OFDM technology has been adopted in many wireless communication systems such as IEEE 802.11, 802.16, 802.20, 3GPP LTE, and the like. Of particular note is that OFDM technology is considered one of the most important physical layer technologies in next-generation mobile communication (4G) systems.

1 shows a schematic block diagram of a transmitter and receiver of an OFDM system according to the prior art, wherein dashed boxes 11 and 27 represent channel coding units and channel coding units for the coding system. That is, for an uncoded system, the OFDM system does not include the two parts described above.

As shown in FIG. 1(a), the transmitter includes: a channel encoder 11, a symbol mapper 12, a pilot insertion unit 13, a power distribution unit 14, an inverse FFT unit 15, an inserted CP unit 16, and a radio frequency/intermediate frequency (RF/). IF) Modulator 17 and modules such as transmit antennas. First, the channel coder 11 encodes the data information from the source to generate coded bits, and then the symbol mapper 12 maps the coded bits into corresponding data symbols in the signal constellation, and then the pilot insertion unit 13 inserts a guide into the data symbols. The frequency symbol is further adjusted by the power distribution unit 14 for the transmission power of each transmitted symbol, and then processed by the inverse FFT unit 15 and processed by the insertion of the CP unit 16 to generate a baseband transmission signal, which is then modulated by the RF/IF modulator. Finally, it is transmitted by the transmitting antenna.

As shown in Figure 1(b), the receiver includes: a receiving antenna, an RP/IF demodulator 21, and a time-frequency. The synchronization unit 22, the CP removal unit 23, the FFT unit 24, the channel trick unit 25, the signal detector 26, the channel decoder 27, and the like. First, the radio frequency receiving signal is received by the receiving antenna, and then processed by the RF/IF demodulator 21 to generate a baseband received signal, and the time frequency synchronizing unit 22 keeps the time and frequency of the receiver consistent with the received signal, in the CP removing unit. 23 and FFT unit 24 performs FFT and CP removal processing on the baseband received signal to obtain a baseband received signal on each subcarrier, and channel estimation unit 25 estimates channel state information (CSI) using the pilot signal, and signal detector 26 A hard decision result (for an uncoded system and a hard decision decoding system) that produces data symbols from the received signal using the estimated CSI, or soft information that produces data symbols (for a soft decoding system). For an uncoded system, signal detector 26 produces the final transmitted data; for an encoding system (including a hard decision decoding system and a soft decoding system), channel decoder 27 utilizes the information provided by the signal detector to ultimately Restore the transmitted data. Finally, the recovered transmission data is sent to the sink.

In order to obtain excellent reception performance in an OFDM system, accurate channel estimation is performed before signal detection. Some effective channel estimation methods have been proposed, such as pilot assisted estimation, semi-blind estimation, and blind estimation. However, it is not possible to obtain ideal channel state information (CSI) by channel estimation, which greatly limits the performance of the OFDM system.

In order to improve the performance loss due to inaccurate channel estimation, joint channel estimation and signal detection methods have been proposed. For example, Non-Patent Document 1 (T. Cui and , C. Tellambura, "Joint Data Detection and Channel Estimation for OFDM Systems," IEEE Trans. Commun., vol. 54, no. 4, pp. 902-915, Apri. 2006 A robust hard decision algorithm is proposed, which combines channel estimation with hard decision detection to improve the performance of OFDM receivers. However, the method proposed in the above Non-Patent Document 1 is so complicated that it cannot be applied to an actual OFDM system. In addition, the method cannot provide soft information of data symbols, so it cannot interface with a soft channel decoder such as a turbo decoder to improve reception performance.

In addition, Non-Patent Document 2 (SY Park, Y. G Kim, and CG ang, "Iterative receiver for joint detection and channel estimation in OFDM systems under mobile radio channels," IEEE Trans. Vehicular Technology, vol. 53, no. 2 , pp. 450-460, Mar. 2004) proposed soft iterative joint channel estimation, A combination of detection and decoding, which effectively improves the performance of the OFDM system by Turbo processing between the channel estimator, the detector and the decoder. Nonetheless, the method essentially utilizes inaccurate CSI estimates generated by the channel estimator to generate soft information, the performance of which is still compromised by channel estimation errors. Summary of the invention

SUMMARY OF THE INVENTION An object of the present invention is to provide a detection method and apparatus for an OFDM system capable of generating a posterior probability of a transmitted data symbol directly from a received signal with higher computational efficiency without performing a channel estimation operation.

In an aspect of the present invention, a signal detecting method for an OFDM system is provided, comprising the steps of: a) inputting a receiving vector including a plurality of received signals, wherein, one of a plurality of data symbols to be detected is to be detected When the symbol is equal to one of its candidate values, the received vector is considered to be subject to a multi-dimensional complex Gaussian distribution under the condition that the channel and other data symbols are unknown; b) the probability density function of the multi-dimensional complex Gaussian distribution is used to calculate the known reception Under the condition of the vector, each data symbol to be detected is equal to the posterior probability of its respective candidate value.

In another aspect of the present invention, a signal detecting apparatus for an OFDM system is provided, comprising: means for inputting a reception vector including a plurality of received signals, wherein one of a plurality of data symbols to be detected is to be detected When the data symbol is equal to one of its candidate values, the received vector is considered to be subject to a multi-dimensional complex Gaussian distribution under the condition that the channel and other data symbols are unknown; the probability density function for calculating using the multi-dimensional complex Gaussian distribution is known A device in which each data to be detected is equal to the posterior probability of its respective candidate value under the condition of receiving a vector.

According to the method and apparatus of the present invention, the posterior probability of the transmitted data symbols can be accurately calculated without the need for channel estimation in the OFDM system. In addition, the detection method and apparatus of the present invention can achieve near optimality with a complexity proportional to the number of subcarriers.

The above features and advantages of the present invention will become more apparent from the Detailed Description Display, where:

1(a) and 1(b) are schematic block diagrams showing a transmitter and a receiver of an OFDM system according to the prior art;

2 shows a schematic block diagram of a transmitter and a receiver of a 0; FDM system in accordance with an embodiment of the present invention;

3(a) and 3(b) illustrate the operation of a detector in a receiver of an OFDM system in different situations according to an embodiment of the present invention;

4 illustrates BER performance of a detector in the case of a QPSK constellation and 16 pilot symbols per data block, in accordance with an embodiment of the present invention;

5 illustrates BER performance of a detector in the case of a 16QAM constellation and 16 pilot symbols per data block, in accordance with an embodiment of the present invention;

6 illustrates BER performance of a detector in a QPSK constellation diagram and 4 pilot symbols per data block, in accordance with an embodiment of the present invention;

Figure Ί shows the BER performance of a detector in the case of a 16QAM constellation and 4 pilot symbols per data block, in accordance with an embodiment of the present invention. detailed description

Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The details of some of the techniques that are known in the art are omitted in the following description, as the detailed description of these known techniques will result in some of the features and advantages of the present invention.

In the following, the superscripts ^τ , and ^H represent the transpose, conjugate and conjugate transpose of the matrix, respectively; det (.) represents the determinant of the matrix; Ϊ represents the identity matrix; diag (.) represents the diagonalization of the vector; _V a ) represents the variance of the random variable; Bu | represents the modulus of the complex number.

The input-output relationship on each subcarrier is equivalent to a flat fading channel and can be expressed as:

Where K ^ and respectively represent the transmitted symbol complex channel gain, received signal and noise on the "subcarriers" in the first time slot, N. Represents the total number of subcarriers, representing the power amplification factor of the symbol, | | = 1, and | f = σ ² . Note: also It is called channel state information (CSI).

A set of N transmitted symbols is defined as a block of data. One data block can contain W symbols on any time slot and subcarrier. However, it is generally considered that one data block is composed of one or more consecutive OFDM symbols. Each OFDM symbol is composed of transmitted symbols on each subcarrier. Each transmitted symbol may be a data symbol, a pilot symbol, or a mixture of both. The detector according to the present embodiment will operate on every data block, i.e., each data block is treated as a basic processing unit.

Thus, the input-output relationship of any data block can be simply expressed as -

Vn = n~K ^X n + ^ η = 1, 2, ... , W (2) where is the ^th transmitted symbol in the data block, h _n , y _{n >} ε _η and respectively represent and corresponding Complex channel gain, received signal, noise, and power amplification factor. In addition, ^ 6 C(n) ₅ where C(n) = { ,... _MW } is the candidate value set, the candidate value in C(n), and the number of candidate values in C(n), and 1

. Since each operation is in one data block, in equation (2), η is directly indicated that the symbol is indexed in the data block, and the subscript indicating the time of equation (1) is omitted.

The above relationship in the data block can also be written in matrix form -

Where y y! ···

H = diag(h) , and

As described above, in order to obtain good performance, it is necessary to perform accurate channel estimation first before detection. The channel estimation method is generally divided into pilot assisted estimation, semi-blind estimation, and blind estimation. Pilot-assisted estimation is widely used in practical systems and in various standards. A system that uses pilot assisted estimation is called a pilot assist system. Each data block in a system has K inserted symbols. Thus, the relationship on the pilot subcarriers can be expressed as - ' = H'P + ε' = P'X'h' + ε' (4) where '

·■· Vn , ε' = [ε _η] -- e _n , Χ' = dg(x') , H' = diag(h') ₅ and P ^J = (., ^., [^. is the first pilot symbol in the data block; and 1.

_Pnt , y _{3⁄4 5} and respectively represent the corresponding transmit power and received signal. Complex channel gain and noise. The detector of this embodiment can be used in any OFDM system, not just for pilot assisted OFDM systems.

Figure 2 shows a schematic block diagram of a transmitter and receiver of an OFDM system in accordance with an embodiment of the present invention. As shown in FIG. 2, a receiver according to an embodiment of the present invention includes: a receiving antenna, an RF/IF demodulator 21, a time-frequency synchronization unit 22, a CP removing unit 23, an FFT unit 24, a signal detector 26', and a channel translation. Module 27 and other modules. Compared to a general OFDM receiver, the receiver does not have a channel estimation unit. The signal detector 26 does not require an estimate of the CSI to directly generate a posteriori probability of the data symbol. For an uncoded system and a hard decision decoding system, the signal The detector 26' produces a hard decision result of the data symbols based on the maximum a posteriori probability criterion. For the soft decoding system, the signal detector 26' inputs the posterior probability of the generated data symbols to the soft decoder for subsequent soft translation. code. The other modules of the receiver are identical to the traditional receiver.

As can be seen from FIG. 2, the receiver according to the embodiment of the present invention is different from the prior art in that the channel estimation unit is removed, and the signal detector 26' directly performs a soft iterative detection operation based on the output of the FFT unit 24 to generate The posterior probability (APP) of a data symbol, also known as soft information. This soft information will be used for subsequent hard decisions or input to the soft channel decoder for soft decoding, such as Turbo decoding. Note that the channel decoder in Figure 2 is only available in the case of an encoding system. That is, for an uncoded system, there is no channel coder and channel decoder 27.

The signal input to the signal detector is a baseband signal demodulated by OFDM, that is, a received signal on each subcarrier. For pilot-assisted OFDM systems, these signals are the superposition of received data signals and noise or the superposition of pilot signals and noise; for systems using embedded pilots (also known as semi-blind systems), these signals are data Signals, pilot signals, and noise are added together.

The detector according to the present embodiment does not require channel estimation, directly calculates the posterior probability distribution of the data symbols, and outputs the final result after a plurality of iterations. If the system uses a soft decoder, the detector outputs a posterior probability distribution for each data symbol. If the system is not programmed The code is either a hard decision decoder, and the detector outputs the decision result of the data symbol obtained according to the maximum posterior probability criterion.

The operation of the detector according to an embodiment of the present invention is described below. The key to the detector according to an embodiment of the invention is a core detection algorithm for calculating the APP of the data symbol without channel estimation.

The APP of the data symbol is given by the following formula (5)

among them

The probability density function of the received signal vector y, which is often referred to as a likelihood function; is a prior probability density function of the received signal vector, which is constant for different m;

= indicates the posterior probability of = _m when the received signal vector is y. Usually, if not specified, P( = _m ) = l/M(n).

) can be calculated as follows - p(j = _m )= ^ _de / _R . ~ rexp(-y ^ff (R _{In= m} ) ) (6)

I

among them

And

R _h = E(hh ^H ).

Each element of the received signal vector y is a random variable formed by a channel, a data symbol or a pilot symbol, and noise. Given that one of the data symbols (eg, x _n = s _{n < m} ) and the channel and other data symbols are unknown, y is considered to obey (or approximately obey) the multidimensional complex Gaussian distribution. Using the probability density function of the multidimensional complex Gaussian distribution, the formula for calculating the likelihood function p(y = _m ) is obtained, that is, the equation (6). Note that the subscript ^ ^ is expressed under the condition of :^ = _Sn ^ .

Substituting equation (6) into equation (5), you can get:

In this paper, "oc" means "for different, ... proportional to... Note that due to equation (8), the detector only needs to calculate for different m, proportional to APP: y) is a constant, so usually in calculation Not be considered.

P( =s _n , _m ) is constant for different m, p _n = _s ) = l/M(n)

I fruit k _m | is different for the difference, ie: k _m | = i, Bay ¹ J

In fact, equation (9) is a simplified form of equation (7) under the condition that the transmitted data symbols are a priori, and equation (10) is a simplification of equation (7) under the condition that the data symbols are constant modulus. form.

The above formula contains the basic principles of the proposed core detection algorithm, but the complexity of using them directly is high. The simplified algorithm is described in detail below.

Define R^£^y^. R ₌ ^ can be thought of as the matrix R when ^-^Jf. R and R _r „ _{=Snim differ} only in the elements on the nth and nth columns. With det(R) and y3⁄4" as intermediate variables, the rank 1 update formula and the block matrix are further inferred using the determinant and matrix inversion. Determinant and inversion formula,

The proposed simplified algorithm is thus obtained. Det(R) and y ^ff R- can be regarded as constants, which can be omitted from the calculation and do not need to calculate their results. Depending on the mean of the data symbols ( : , the simplification algorithm is divided into . 0 and 3⁄4 = 0).

In the first case, when ≠0,

Exp y

among them

And the element of column j, is the nth element of is R_. '

By substituting the equations (il) and (12) into equation (7), the core detection algorithm in the case of ^≠ 0 can be obtained, as follows:

If | _stvn | = l for any, then equation (13) can be reduced to ν _η (14)

In the second case, when = 0,

Where is the nth diagonal element of _Rh P3⁄43⁄4- PR _h , but the nth element of H _h P3⁄4 ^ff R-.

By substituting equations (15) and (16) into equation (7), the core detection algorithm for = 0 can be obtained, as follows:

If | =1 for any m, then the above equation can be simplified to:

Equations (13) and (17) are simplified core detection algorithms for ≠0 and = 0, respectively, and equations (14) and (18) are simplified algorithms when the data symbols are constant.

In order to further improve the computational efficiency, a fast calculation method for some parameters in the above simplified algorithm is given below. To implement the core detection algorithm, you need to determine ϋ-Υ, ø = [ø, ... and 77 = ^ ... _3⁄4 f. By performing singular value decomposition (SVD), we can get ijR _h = UD'u where uec" , υ ³ υ = ϊ, D is the diagonal matrix of x, usually much smaller than V. In fact, if the data block contains only 1 OFDM symbols, indicating the number of multipaths in a multipath channel. Using the Sherman-Morrison- Woodbm formula, you can get -

(19) where 3⁄4 = ^( ), and V = σ ² Ι + Ρ ² diag(var (a;] .

From equation (19), the complexity of calculating RV, Θ, and is (N ² ). So the total complexity of the algorithm is 0 (M?+NM) per data block, or each data symbol

0(L ² + M)o here,

3 is a flow chart showing a soft iterative detection process performed by a signal detector in a receiver of an OFDM system in accordance with an embodiment of the present invention. In the figure, i represents the index of the number of iterations, and N represents the total number of iterations.

Figure 3(a) shows the detection flow for an uncoded system or a hard decision decoding system. After the detection starts, i = l in step S21, which means that the first iteration starts; then in step Step S12 performs the core detection algorithm by using an initial prior probability distribution of the data symbols; determining whether _3⁄4 is equal to ^ in step Si3; if + l (ie, the next iteration starts), and executing the core again based on the updated condition The detection algorithm; if _3⁄4 = N _{i ;} then a hard decision is made on the data symbols according to the maximum a posteriori probability criterion in step S14. Note that when performing the core detection algorithm, _P ( = _Sn , _m ) is always the original prior probability, and the so-called updated condition refers to using the APP of the data symbol generated by the detector in the previous iteration to calculate the current The mean and variance of the data symbols required in the iteration.

Figure 3(b) shows the flow for a soft decoding system. After the start of the detection, let z = l in step S21, that is, the first iteration begins; then the core detection algorithm is performed using the initial prior probability distribution of the data symbols in step S22, and the generated is generated in step S23. The posterior probability of the data symbol is sent to the soft decoder; after the soft decoding process, the APP of the original data (data before encoding) and the APP of the data symbol (encoded and mapped data) are generated; Whether it is equal to N _; if _3⁄4≠ /\^, let i = i + i (that is, the next iteration begins), and then execute the core detection algorithm again based on the updated condition, and then send the APP of the generated data symbol to the soft Decoder; if = Nj, then perform a hard decision according to the maximum a posteriori probability criterion to recover the original data in step S25. Note that when performing the core detection algorithm, p (= _m ) is always the original prior probability, so-called The updated condition refers to the calculation of the mean and variance of the data symbols required in the current iteration using the APP of the data symbols generated by the decoder in the previous iteration.

Also, note that at the first iteration, usually = 0, so equations (15) through (18) should be used. In other iterations, it is usually ≠0, so equations (11) through (14) should be used. In fact, when the iterative detection algorithm of the present embodiment is applied to a pilot-assisted OFDM system, the first iteration can be greatly simplified as follows.

At the first iteration, ρ( ) is usually a constant. For pilot assisted OFDM systems, there are:

^ in y = ∑ / _n (y')1. Vector y receives signals from the received signal of the symbol to be detected and the pilot Composition. For the first iteration, the posterior probability of a given ^^^ is equal to the = _m posterior probability of the given received signal y, ie: p{x _n = s _n>m |y) = p (x _n ^ s _n , _m | _y .).

Using the determinant and inversion formula of the block matrix, you can get -

its

n

( (h' ) is a subarray consisting of a matrix column and elements of the ..., _3⁄4 rows.

Η of the first column of R _h n _l .., _¾ two elements.

Therefore, during the first iteration, the core detection algorithm becomes;

Ρ

If for any m, 1, then equation (23) can be simplified to

The complexity of the first iteration after simplification is 0 (Nf + NM). Where f is the number of pilot symbols in each data block.

In addition, actual systems may need to perform some operations using channel state information (CSI), such as power control, adaptive modulation and coding (AMC), and so on. This requires the receiver to also output an estimate of the CSI. Using the generated soft information, the detector of this embodiment is also capable of outputting accurate channel estimates as follows:

(25) where and V are determined by the APP generated by the detector or soft decoder at the last iteration.

Another channel estimation method is: h = R _h ^H P (PtR _h p + σ ² Ι) - y (26) Where = dm.g(x _v ... , x _N ) and ^ is the result of the decision.

For uncoded systems,

The posterior probability (ΑΡ'Ρ) generated at the last iteration.

For an encoding system, it can be generated by encoding and mapping the data bits output by the decoder.

If = X , the estimated performance of equation (26) is better than the estimated performance of equation (25). Note: The decision result can be verified by Cyclic Redundancy Check (CRC).

As described above, the proposed iterative detection algorithm needs to know the channel correlation matrix R _h and the noise variance _σ ² . Wherein σ ² can be calculated by the method proposed in Non-Patent Document 3 (IEEE Std 802.16e-2005 and IEEE Std 802.16-2004/Cor 1-2005 (Amendment and Corrigendum to IEEE Std 802.16-2004), Feb. 2006). This document is incorporated by reference in its entirety. The following discusses how to determine _Rh .

Time average

Available, as well as 1) and 0. According to Non-Patent Document 4 (D. J.

Rabideau, "Fast, rank-adaptive subspace tracking," IEEE Trans. Signal Processing, vol. 44, pp. 2229-2244, Sept. 1996) and Non-Patent Document 5 (A. avcic and B. Yang, "A new efficient Subspace tracking algorithm based on singular value decomposition," in Proc. 1994 IEEE Int. Conf. Acoustics, Speech, and Signal Processing, vol. IV, 1994, pp.

IV/485-IV/488), the fast rank 1 singular value decomposition update algorithm for subspace tracking can be applied to the calculation of U and D. Their computational complexity is only 0 (H). Since the channel is statistically invariant over a relatively long period of time, the estimated parameters are relatively relatively Precise.

Figures 4 through 7 show the simulation results of the above algorithm in several cases. In the simulation, an uncoded pilot assisted OFDM system with 256 subcarriers is considered. The channel is a 6-path typical city (TU) fading channel (COST207) with a channel bandwidth of 10 MHz and an OFDM symbol duration of 32^ with a cyclic prefix of 6.4^. Without loss of generality, it is assumed that the data block is composed of one OFDM symbol, and the pilot symbols are equally spaced in the frequency domain. All transmitted symbols (including data symbols and pilot symbols) have the same transmit power. In Figures 4 and 5, the pilot symbols for each data block are 16. In Figures 6 and 7, there are only four pilot symbols for each data block. The constellation used in Figures 4 and 6 is QPSK, and the constellation used in Figures 5 and 7 is 16QAM. .

For performance comparison, two important performance metrics are provided, including 'optimal performance, and 'ML-MMSE^ optimal performance, which represents the performance of the maximum likelihood detector using ideal CSI, which is the theoretically optimal OFDM. Receiving plan. "ML-MMSE" represents the performance of a maximum likelihood detector using minimum mean square error (MMSE) channel estimation, which is a very common OFDM reception scheme in practice. In Figs. 4 to 7, <first iteration', 'second iteration', ..., 'eighth iteration' represent the BER performance of the method according to the present embodiment at different number of iterations.

In addition, it can be seen from the figure that the performance of ML-MMSE is always far from the optimal performance, which is caused by the error of channel estimation. In the first iteration, the performance of the above iterative detection method is similar to ML-MMSE. However, as the number of iterations increases, its performance quickly approaches optimal performance. As shown in Figures 6 and 7, although the inserted pilot symbols are sparse, the soft iterative detection method according to an embodiment of the present invention can still achieve near optimal performance.

The above is only the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of changes or substitutions within the technical scope of the present invention. All should be covered by the scope of the present invention. Therefore, the scope of the invention should be determined by the scope of the claims.

Claims

Claim

A signal detecting method for an OFDM system, comprising the steps of: a) inputting a receiving vector comprising a plurality of received signals, wherein when one of the plurality of data symbols to be detected is equal to one of its candidate values When the channel and other data symbols are unknown, the received vector is considered to be subject to a multi-dimensional complex Gaussian distribution;

b) using the probability density function of the multidimensional complex Gaussian distribution to calculate a posterior probability that each of the data symbols to be detected is equal to its respective candidate value under the condition of a known received vector.

2. The signal detecting method according to claim 1, further comprising the steps of:

c) performing a hard decision based on the posterior probability that the to-be-detected symbol is equal to its respective candidate value.

3. The signal detecting method according to claim 2, wherein for the uncoded system or the hard decision decoding system, step b) is repeatedly performed to reach a predetermined number of iterations, and in the process of repeatedly performing step b), the previous time is used The posterior probability of the data symbols obtained in step b) is performed to calculate the mean and variance of the data symbols required to perform step b) at a time.

4. The signal detecting method according to claim 2, wherein for the soft decoding system, step b) and soft decoding are repeatedly performed to reach a predetermined number of iterations, and in the process of repeatedly performing step b) and soft decoding, The mean and variance of the data symbols required to perform step b) at the current time are calculated using the posterior probability of the data symbols obtained by the previous soft decoding.

The signal detecting method according to claim 2, wherein each time the step b) is performed, the a priori probability that the data symbol to be detected is equal to the candidate value is equal to the initially predetermined prior probability value.

6. The signal detecting method according to claim 3, wherein in the case where step b) is performed for the first time, the posterior probability value is calculated using a determinant of the block matrix and an inversion formula.

7. The signal detecting method according to claim 3, wherein the posterior probability value is calculated using a rank 1 and a matrix inversion of a rank 1 斩 formula and a determinant and an inversion formula of the block matrix.

8\ A signal detection device for an OFDM system, comprising:

Means for inputting a reception vector including a plurality of received signals, wherein when one of the plurality of to-be-detected data symbols is equal to one of its candidate values, under the condition that the channel and other data symbols are unknown, The receiving vector is subject to a multidimensional complex Gaussian distribution;

Means for utilizing the probability density function of the multidimensional complex Gaussian distribution to calculate a posterior probability that each of the data symbols to be detected is equal to its respective candidate value under the condition of a known received vector.