Signal detection method of low-complexity adaptive algorithm based on expected propagation

Abstract

The invention belongs to the field of signal detection, and relates to a signal detection method of a low-complexity adaptive algorithm based on expected propagation, which comprises the following steps: constructing a short-wave communication system which comprises a transmitting end, a receiving end and a signal detection device; the sending end is used for processing a signal to be sent and then sending the signal to the receiving end; the receiving end carries out reduction processing on the received signal and sends the reduced signal to the signal detection device; the signal detection device detects the restored signal through a low-complexity adaptive algorithm to obtain a detection result; according to the invention, a low-complexity adaptive algorithm is adopted to detect the restored signal, so that the detection efficiency and accuracy are improved.

Description

Technical Field

[0001] The invention belongs to the field of signal detection, and in particular relates to a signal detection method based on a low-complexity adaptive algorithm of expected propagation. Background Art

[0002] Due to various interferences in the communication process, the received signal cannot be received correctly. At this time, the detection algorithm is needed to make a decision and restore the original signal. Maximum Likelihood (ML) detection is a signal detection method based on probability theory, which is used for signal detection in a noisy environment. It makes decisions by maximizing the likelihood function of the observed data under given assumptions. It is the optimal detection algorithm in a statistical sense. However, when the signal model is more complex, the computational complexity is high. Zero-forcing detection suppresses the interference signal by calculating the inverse matrix of the channel matrix to obtain the desired signal, and the computational complexity is lower than that of the maximum likelihood detection algorithm. However, when the channel matrix is ​​close to a singular matrix, it will cause noise amplification and affect the performance of signal detection. The linear minimum mean square error algorithm estimates the received signal through linear transformation and improves the accuracy of the estimation by minimizing the mean square error between the signal and the estimated value. That is, the signal is estimated through the statistical information (mean and variance) of the channel matrix and noise. This algorithm takes into account the influence of noise and has a lower mean square error and higher detection accuracy than the zero-forcing detection algorithm at low signal-to-noise ratio. The iterative detection approach through soft information exchange has better performance and higher monitoring accuracy, but the number of iterations is usually difficult to determine. Too high an iteration number will cause redundancy, while a low number will not achieve the optimal performance. Summary of the invention

[0003] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a signal detection method based on a low-complexity adaptive algorithm of expected propagation, the method comprising: constructing a shortwave communication system, the system comprising a transmitting end, a receiving end and a signal detection device; the transmitting end is used to process the signal to be sent and then send it to the receiving end; the receiving end restores the received signal and sends the restored signal to the signal detection device; the signal detection device detects the restored signal through a low-complexity adaptive algorithm to obtain a detection result.

[0004] Beneficial effects of the present invention:

[0005] The algorithm complexity of the present invention is in is the average number of iterations of the algorithm, while the computational complexity of the non-adaptive algorithm is In the case of a single antenna with a small amount of data, the improvement is small, but for large-scale antenna arrays, the complexity will be significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 It is a flow chart of the low-complexity adaptive algorithm based on expected propagation of the present invention;

[0007] Figure 2 LDPC check flow chart of the present invention;

[0008] Figure 3 A flow chart of data processing by the transmitting end of the present invention;

[0009] Figure 4 It is a comparison chart of the number of iterations of the adaptive algorithm and the non-adaptive algorithm of the present invention;

[0010] Figure 5 This is a comparison chart of the bit error rate performance between the adaptive algorithm and the non-adaptive algorithm of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] In order to determine the number of iterations of the expected propagation module in the detection algorithm, a new method is proposed in combination with LDPC to reduce the number of iterations while ensuring good performance. The log-likelihood ratio is calculated by the soft information output by each iteration of the expected propagation, and the convergence of the average amplitude of the log-likelihood ratio is used to determine whether the iteration needs to continue.

[0013] A signal detection method based on a low-complexity adaptive algorithm of expected propagation, the method comprising: constructing a shortwave communication system, the system comprising a transmitting end, a receiving end and a signal detection device; the transmitting end is used to process the signal to be sent and then send it to the receiving end; the receiving end performs restoration processing on the received signal and sends the restored signal to the signal detection device; the signal detection device detects the restored signal by using a low-complexity adaptive algorithm to obtain a detection result.

[0014] In this embodiment, if Figure 3 As shown, the transmitting end processes the signal to be transmitted including: encoding the signal to be transmitted; constructing check information, interleaving the check information and the encoded information; and constellation mapping the interleaved information to obtain symbol information.

[0015] The transmitter is based on a single-carrier single-antenna transmitter structure, using 4QAM modulation. The coding uses LDPC coding with a code rate of 1 / 2, and the generating polynomial G = [576, 1152]. The information bits per frame are 576 bits. Check information is added to the coded information, interleaved together, and then constellation mapping is performed, and finally symbol output is performed. The initial number of iterations is 5. The performance of the two algorithms is evaluated by simulating the adaptive algorithm and the non-adaptive algorithm. The results are shown in Figure 4 and Figure 5 As shown in the simulation diagram, after the introduction of LDPC check, the computational complexity of the adaptive algorithm is in is the average number of iterations of the algorithm, and the algorithm complexity without introducing LDPC check is While ensuring that the bit error performance remains roughly unchanged, the computational complexity of the adaptive algorithm has been significantly improved.

[0016] In this embodiment, the detection of the restored signal using a low-complexity adaptive algorithm includes: Input: Initialize the approximate distribution factor According to the initialized approximate distribution factor, q(x) is calculated, where S is the number of iterations of EP.

[0017] for i=1,...,I ​​and l=1,...,S do

[0018] Compute the following probability distribution:

[0019]

[0020] The above probability distribution is called the approximate marginal probability distribution, which is obtained by approximating the probability distribution.

[0021] Compute the following probability distributions and corresponding moments:

[0022] Update the approximate probability distribution of the next iteration through "moment" matching

[0023]

[0024] end for

[0025] Output: approximate probability distribution

[0026] When calculating the approximate distribution during the algorithm, it is necessary to calculate the mean vector μ and the covariance matrix Σ.

[0027]

[0028]

[0029] When calculating the covariance matrix Σ, the matrix needs to be inverted. The complexity of the algorithm also depends on this. Its complexity is Where S is the number of iterations, N t is the number of transmitting antennas, and K is the size of a frame of data. Whether it is a single antenna or multiple antennas, reducing the number of iterations is crucial to reducing complexity. The low-complexity adaptive algorithm can correctly determine whether to stop iteration based on the LDPC check situation, thereby avoiding redundancy in the number of iterations. The LDPC check situation is as follows:

[0030] Input: Set the maximum number of iterations S max , amplitude threshold λ;

[0031] At the end of the i-th iteration, the mean of the average amplitude E[L i ].

[0032]

[0033] Calculate the change between the mean magnitude of the current iteration and the mean magnitude of the previous iteration

[0034]

[0035] If the current mean amplitude change is less than the preset threshold, the iteration number S is increased by one.

[0036] |E[L i ]-E[L i-1 ]|<λE[L i-1 ]

[0037] If S>S max , the iteration ends, otherwise proceed to the next iteration.

[0038] The verification process is as follows Figure 2 shown.

[0039] In this embodiment, Figure 1 This is a low-complexity adaptive algorithm flow chart based on expected propagation. The figure records the principle of adaptive detection of the number of iterations in the expected propagation algorithm. γ and Λ are parameter pairs that need to be transmitted during the algorithm iteration process. The parameter pair must be updated at the beginning of each algorithm. The iteration can be terminated after passing the LDPC check or reaching the maximum number of iterations, otherwise the number of iterations is increased by one. This process shows that the present invention has a high degree of adaptability, and the number of iterations is automatically adjusted through the LDPC check, which effectively reduces the computational complexity.

[0040] Figure 2This is the LDPC verification flow chart, which records the variables required in the verification process and the mean value E[L i ] calculation. Then calculate the change between the mean amplitude of the current iteration and the mean amplitude of the previous iteration. If the change of the current mean amplitude is less than the preset threshold, the iteration number S is increased by one. If S>S max , the iteration ends, otherwise proceed to the next iteration.

[0041] Figure 4 It is a comparison chart of the number of iterations of the adaptive algorithm and the non-adaptive algorithm, which shows that as the signal-to-noise ratio increases, the iterative convergence speed of the adaptive algorithm accelerates. Especially in the case of high signal-to-noise ratio, the average number of iterations of the adaptive algorithm is approximately 1, and the average number of iterations is Significantly reduced.

[0042] Figure 5 The figure records the bit error rate performance of the adaptive algorithm and the non-adaptive algorithm. It can be seen that the performance of the adaptive algorithm has slightly decreased, which is caused by the LDPC check overhead. This shows that the performance of the adaptive algorithm after the introduction of LDPC check is almost equivalent to that of the original algorithm.

[0043] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation modes of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A signal detection method based on a low-complexity adaptive algorithm for expected propagation, characterized in that: include: Constructing a shortwave communication system, which includes a transmitting end, a receiving end and a signal detection device; The transmitting end is used to process the signal to be sent and then send it to the receiving end; the receiving end restores the received signal and sends the restored signal to the signal detection device; the signal detection device detects the restored signal through a low-complexity adaptive algorithm to obtain a detection result.

2. The signal detection method according to claim 1, characterized in that: The transmitting end is a transmitter structure based on a single carrier and a single antenna.

3. The signal detection method based on a low-complexity adaptive algorithm for expected propagation according to claim 1, characterized in that: The transmitting end processes the signal to be transmitted by: encoding the signal to be transmitted; constructing check information, interleaving the check information and the encoded information; and constellation mapping the interleaved information to obtain symbol information.

4. The signal detection method based on the low complexity adaptive algorithm of expected propagation according to claim 3 is characterized in that: The encoding process of the signal to be transmitted includes: information sequence a = [a1, ..., a k ] T It consists of K information bits, and the information sequence is encoded into b = [b1, ..., b v ] T ; Wherein, the code rate is the ratio of the information sequence length to the encoded sequence length, that is, K information bits are encoded into V information bits; a k is the kth information bit, T is the transposition, K is the number of information bits, V is the number of encoded information bits, b v is the vth information bit in the encoded sequence.

5. The signal detection method based on the low complexity adaptive algorithm of expected propagation according to claim 3, characterized in that: Interleaving the check information and the coded information includes: randomly interleaving the coded information sequence b = [b1, ..., b v ] T Interleave to generate a random permutation index P = [p1, ..., p n ], where p i ∈{1,...,n}, and all p i Uniquely, map the i-th element of the original data to p i The interleaved sequence c = [c1,...,c v ] T .

6. The signal detection method based on the low complexity adaptive algorithm of expected propagation according to claim 3, characterized in that: The constellation mapping of the interleaved information includes: the interleaved data sequence is modulated by an M-order constellation to obtain a symbol sequence x=[x1,...,x N ] T , where N = [V / log2M], the symbol sequence x = R(x) + jI(x) through the channel h = [h1, ..., h L ] transmission, each symbol is represented by in, represent where V is the number of coded information bits, N is the length of the modulated symbol sequence, M is the modulation order, R(x) is the real part of the complex signal, and I(x) is the imaginary part of the complex signal.

7. The signal detection method based on a low-complexity adaptive algorithm of expected propagation according to claim 1, characterized in that: The receiving end performs restoration processing on the received signal, including: performing mapping on the mapping operation of the sending end, de-interleaving on the interleaving operation, and decoding on the encoding operation.

8. The signal detection method based on the low complexity adaptive algorithm of expected propagation according to claim 1, characterized in that: The restored signal is detected using a low-complexity adaptive algorithm, including: initializing an approximate distribution factor, calculating an approximate posterior probability distribution based on the initialized approximate distribution factor; calculating a mean vector and a covariance matrix based on the posterior probability; calculating the complexity based on the covariance matrix, and verifying the complexity using LDPC. The detection is completed after the verification passes, otherwise the above process is repeated.

9. The signal detection method based on the low complexity adaptive algorithm of expected propagation according to claim 8, characterized in that: The complexity check using LDPC includes: Step 1: Set the maximum number of iterations S max and amplitude threshold λ; Step 2: At the end of the i-th iteration, calculate the mean of the average amplitude E[L i ]; Where k is the number of received symbols, j is the index of the current transmitted symbol, and L i (j) is the log-likelihood ratio of the jth symbol; Step 3: Calculate the change between the mean amplitude of the current iteration and the mean amplitude of the previous iteration Step 4: If the current mean amplitude change is less than the preset threshold, the iteration number S is increased by one; |E[L i ]-E[L i-1 ]|<λE[L i-1 ] If S>S max , end the iteration, otherwise proceed to the next iteration.

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