Pseudo code sequence estimation method and system under multipath channel based on RAKE receiving and MLSE estimation

Abstract

The invention relates to the technical field of signal processing, in particular to a multi-path channel pseudo code sequence estimation method and system based on RAKE receiving and MLSE estimation, and the method comprises the steps: carrying out the signal processing of a received signal under a multi-path channel, obtaining pseudo code period estimation, and carrying out the rough estimation of the time delay and amplitude of each path of the received signal; constructing a multi-path channel receiving signal model based on the time delay and amplitude coarse estimation of each path, so that the multi-path channel receiving signal model performs signal symbol detection and pseudo code estimation by using a self-interference cancellation (SIC) and RAKE receiving detection algorithm; an information symbol and pseudo code sequence joint estimation cost function is set based on a multi-channel receiving signal model, and iterative optimization estimation is carried out on the multi-channel receiving signal model by using maximum likelihood estimation (MLSE) based on the cost function. And taking the pseudo code sequence and the information symbol sequence corresponding to the iteration result with the minimum cost function value during model convergence as final estimation results and outputting the final estimation results. The method can be suitable for signal processing requirements under the conditions of low signal-to-noise ratio and various parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a method and system for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation. Background Art

[0002] Direct sequence spread spectrum (DSSS) signals have been widely used due to their advantages such as low working signal-to-noise ratio, strong anti-interference, low interception rate, and ability to suppress multipath effects. Long code DSSS (LC-DSSS) signals use a one-period pseudo code sequence to modulate multiple information code elements. The periodicity of the pseudo code sequence is severely damaged, and in non-cooperative communication, the pseudo code sequence used for spread spectrum must be known to achieve blind despreading of the intercepted signal. Therefore, the blind estimation of the pseudo code sequence of LC-DSSS signals has become a research hotspot for parameter estimation of DSSS communication systems. One idea to solve the blind estimation of the pseudo code sequence of LC-DSSS signals in unknown multipath channels is to treat the DSSS signal as a regular signal and estimate and equalize the channel to eliminate or weaken the impact of the channel and thus achieve pseudo code sequence estimation. However, this idea is relatively effective under high signal-to-noise ratios, and cannot be applied under low signal-to-noise ratios because the equalization algorithm is difficult to converge. Another idea is to model the long code signal as a short code DSSS signal under the missing data model based on the signal subspace, and then process it according to the method of short code DSSS signal. However, the approximate processing error on this model is closely related to the signal parameters and channel conditions, and cannot fully adapt to the signal processing requirements under various parameters. Summary of the invention

[0003] To this end, the present invention provides a method and system for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation, which solves the problem that the existing LC-DSSS signal pseudo code sequence estimation is limited in low signal-to-noise ratio application and has poor adaptability to signal parameter processing requirements.

[0004] According to the design scheme provided by the present invention, on the one hand, a pseudo code sequence estimation method under a multipath channel based on RAKE reception and MLSE estimation is provided, comprising:

[0005] Carrier timing synchronization and chip interval sampling are performed on the received signal under the multipath channel to obtain pseudo code period estimation; and a rough estimate of the delay and amplitude of each path of the received signal after interval sampling is performed;

[0006] Constructing a multipath channel receiving signal model based on rough estimation of each path delay and amplitude, so that the multipath channel receiving signal model uses self-interference cancellation SIC and RAKE receiving detection algorithm to perform signal symbol detection and pseudo code estimation;

[0007] Based on the multi-channel receiving signal model, a cost function for jointly estimating information symbols and pseudo-code sequences is set. Based on the cost function and using the maximum likelihood estimation (MLSE), the multipath channel receiving signal model is iteratively optimized and estimated. The pseudo-code sequence and information symbol sequence corresponding to the iterative result with the minimum cost function value when the model converges are taken as the final estimation results and output.

[0008] As a pseudo code sequence estimation method under a multipath channel based on RAKE reception and MLSE estimation of the present invention, further, a process of iteratively optimizing and estimating a multipath channel reception signal model based on a cost function comprises:

[0009] Initialization, the initialization includes: randomly initializing the pseudo code sequence, and initializing the signal symbol sequence and each path amplitude to zero value;

[0010] Symbol detection and pseudo code sequence estimation, the symbol detection and pseudo code sequence estimation comprising: sorting the power of each channel according to the rough estimation results of each path delay and amplitude, and performing serial symbol detection and pseudo code sequence estimation in sequence according to the sorting of the power of each channel;

[0011] The cost function value of the current round is calculated according to the cost function to determine whether the model converges based on the cost function value. If the model converges, return to the initialization step, and loop the initialization and symbol detection and pseudocode sequence estimation steps for a specified number of times, and select the pseudocode sequence and signal symbol sequence corresponding to the iterative result with the minimum cost function value when the model converges as the final estimation result and output them. If the model does not converge, the pseudocode sequence estimation should be judged and go to the symbol detection and pseudocode sequence estimation steps for re-execution.

[0012] As a pseudo code sequence estimation method under a multipath channel based on RAKE reception and MLSE estimation of the present invention, further, serial symbol detection is performed in sequence according to the power sorting of each channel, comprising:

[0013] Based on the multipath channel receiving signal model, the previous symbol detection sequence and the pseudo code estimation sequence, the interference signal is reconstructed, the interference is cancelled and re-correlated using the maximum likelihood estimation criterion to obtain the information waveform estimation and the corresponding path signal amplitude;

[0014] Obtaining a weighted result of the information waveform of the detected path based on the signal amplitude and the phase difference between the information waveform of each path and the information waveform of the main path, and performing a hard symbol decision on the weighted result, wherein the main path is a channel path for which symbol detection is performed preferentially according to power sorting;

[0015] The reconstructed waveforms of other paths are updated according to the symbol hard decision, and the next path information sequence is estimated until all path information sequence estimations are completed. The symbol hard decision is performed on the weighted results of each path information waveform to obtain the final symbol detection sequence.

[0016] As a pseudo code sequence estimation method under a multipath channel based on RAKE reception and MLSE estimation of the present invention, further, pseudo code sequence estimation is performed in sequence according to the power sorting of each channel, including:

[0017] Based on the current information waveform and the pseudo code sequence estimation, the pseudo code sequence waveform of the corresponding path is obtained, and the pseudo code waveforms obtained by the other path estimation are correlated with the pseudo code waveform of the main path estimation to obtain the phase difference;

[0018] According to the phase difference, the pseudo code models of other paths are adjusted to be in phase with the main path pseudo code waveform, and the pseudo code waveforms of each path are weighted superimposed by amplitude, and the final pseudo code waveform is obtained based on the weighted superposition result, so as to obtain the pseudo code sequence by making a hard decision on the final pseudo code waveform.

[0019] As a pseudo code sequence estimation method under a multipath channel based on RAKE reception and MLSE estimation of the present invention, further, judging whether the model converges according to the cost function value includes:

[0020] The cost function value of the current round is compared with the cost function value of the previous round of iteration. If the two cost function values ​​are equal, the model is determined to have converged.

[0021] On the other hand, the present invention also provides a pseudo code sequence estimation system under a multipath channel based on RAKE reception and MLSE estimation, comprising: a signal coarse estimation module, a signal modeling module and a joint estimation module, wherein:

[0022] The signal rough estimation module is used to perform carrier timing synchronization and chip interval sampling on the received signal under the multipath channel to obtain pseudo code period estimation; and to perform rough estimation on the delay and amplitude of each path of the received signal after interval sampling;

[0023] A signal modeling module is used to construct a multipath channel receiving signal model based on the rough estimation of each path delay and amplitude, so that the multipath channel receiving signal model uses self-interference cancellation SIC and RAKE receiving detection algorithm to perform signal symbol detection and pseudo code estimation;

[0024] The joint estimation module is used to set the cost function for jointly estimating the information symbol and the pseudo code sequence based on the multi-channel receiving signal model, iteratively optimize and estimate the multipath channel receiving signal model based on the cost function and using the maximum likelihood estimation MLSE, and take the pseudo code sequence and information symbol sequence corresponding to the iterative result with the minimum cost function value when the model converges as the final estimation result and output it.

[0025] Beneficial effects of the present invention:

[0026] The present invention makes a rough estimate of the time delay and amplitude of each path, constructs a joint estimation architecture of information symbols and pseudocode sequences based on serial self-interference cancellation and RAKE detection, and realizes iterative optimization estimation of information sequences and pseudocode sequences by randomly initializing pseudocode seeds multiple times, thereby improving the estimation performance of pseudocode sequences. The present invention is applicable to signal processing requirements under low signal-to-noise ratio and various parameters, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the pseudo code sequence estimation process in a multipath channel based on RAKE reception and MLSE estimation in the embodiment;

[0028] Figure 2 This is a schematic diagram of the rough estimation result of multipath channel delay in the embodiment;

[0029] Figure 3 The demodulation algorithm architecture based on SIC+RAKE in the embodiment is shown;

[0030] Figure 4 This is a schematic diagram of a pseudo code sequence estimation architecture based on MLSE in an embodiment;

[0031] Figure 5 This is a schematic diagram of a pseudo code sequence estimation architecture for a multipath long code DSSS signal based on SIC+RAKE in an embodiment;

[0032] Figure 6 Schematic diagram of the variation of the pseudo code estimation NMSE with the signal-to-noise ratio in the embodiment;

[0033] Figure 7 It is a schematic diagram showing the variation of the pseudo code estimation NMSE with the amount of data in the embodiment. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention is further described in detail below in conjunction with the accompanying drawings and technical solutions.

[0035] RAKE reception is a receiving technology that uses the sharp autocorrelation characteristics of the pseudocode sequence to achieve multipath diversity gain in DSSS signal detection. It uses the sharp autocorrelation characteristics of the pseudocode sequence to distinguish signals from different paths at the receiving end, and merges the signals from different paths after different delays, polarity inversions, and alignment, thereby turning the original interference signal into a useful signal. This technology cleverly utilizes the excellent characteristics of the DSSS signal itself and has been widely used in DSSS system demodulation. In view of the problems of limited application in the existing pseudocode sequence estimation process, an embodiment of the present invention provides a pseudocode sequence estimation method in a multipath channel based on RAKE reception and MLSE estimation, such as Figure 1 As shown, including:

[0036] S101, performing carrier timing synchronization and chip interval sampling on a received signal in a multipath channel to obtain a pseudo code period estimation; and performing a rough estimation of the time delay and amplitude of each path of the received signal after the interval sampling.

[0037] The long code signal model at the receiving end under multipath channel can be expressed as the delayed superposition of conventional LC-DSSS signal. According to the LC-DSSS signal model, its multipath is expressed as

[0038]

[0039] Where L h Indicates the number of multipaths; h l , τ l They represent the amplitude and delay of the lth propagation path respectively, A is the signal amplitude, b m represents the mth information symbol, τ represents the signal receiving delay, represents the number of information symbols under the lth path, M represents the number of pseudo code cycles, L is the pseudo code sequence length, G is the spreading factor, q(n) represents the shaping filter waveform, represents the pseudo code sequence, and v(n) is the channel Gaussian white noise. Traditional SIC and RAKE receiving technologies require that the power estimation of each signal path be completed first. In order to accurately model the signal, it is necessary to first estimate the delay of each path in the multipath channel and roughly sort the power of each path to provide model initialization information. It should be pointed out that the accuracy requirements for the preprocessing and rough estimation are not high here, and the specific requirements can be referred to the LC-DSSS-CDMA signal multi-user delay blind estimation algorithm. Figure 2 As shown in the figure, the delay estimation results of the LC-DSSS signal with signal parameters (L, G) = (80, 47) under a typical multipath channel, where the channel used is a pseudo code chip interval channel, the channel coefficient is h = [0.6 0 0 1 0 0 0.9], the signal length is 50 pseudo code cycles, and the signal-to-noise ratio is E b / N0=0dB.

[0040] Depend on Figure 2 It can be seen that the multi-user delay estimation method for LC-DSSS-CDMA signals can also have good resolution in the single-user multipath channel delay estimation method. Figure 2 As can be seen from the result (b) in the figure, the information such as the delay and power ranking of each path in the multipath channel can also be obtained at the same time, which corresponds to Figure 2 The amplitude of (b) in the figure can be obtained by using the RAKE reception and SIC-based symbol detection and pseudo code estimation.

[0041] S102: construct a multipath channel receiving signal model based on the rough estimation of each path delay and amplitude, so that the multipath channel receiving signal model uses self-interference cancellation SIC and RAKE receiving detection algorithm to perform signal symbol detection and pseudo code estimation.

[0042] Based on formula (0.1), the multipath LC-DSSS signal can be modeled as follows:

[0043]

[0044] in Indicates the waveform of sending information. Represents the pseudo code waveform of period extension, the two can be expressed as

[0045]

[0046] The information waveform estimation can be obtained using the maximum likelihood estimation criterion

[0047]

[0048] Equations (0.4) and (0.5) correspond to the RAKE receiving structure, where It represents the phase difference between the information waveform detected by each path and the main path information waveform. The main path information waveform can be estimated first according to the power sorting, and the phase difference between the estimated value of the other path information waveform and the estimated value of the main path information waveform can be measured by relevant methods. p1 represents the power parameter, that is, Used to perform power normalization on the estimated waveform. Figure 3 The demodulation algorithm architecture based on SIC+RAKE is shown. Equation (0.4) corresponds to the interference cancellation structure. Considering that the power difference between the paths may be large, the interference cancellation part adopts a serial structure; Equation (0.5) corresponds to RAKE reception.

[0049] Figure 3In the architecture shown, serial symbol detection is performed in sequence according to the power sorting of each path. During the detection process, the interference signal is reconstructed, the interference is cancelled and re-correlated using the previous detection result sequence and pseudo code sequence estimation. The detection result is adjusted by phase, delay, etc., and then the judgment is superimposed and the information symbol sequence is dynamically updated in real time. Among them, the phase adjustment can use the correlation method to obtain the phase difference, and the delay information can be estimated based on the previous period.

[0050] S103, setting a cost function for jointly estimating information symbols and pseudo-code sequences based on a multi-channel received signal model, iteratively optimizing and estimating the multipath channel received signal model based on the cost function and using maximum likelihood estimation (MLSE), and taking the pseudo-code sequence and information symbol sequence corresponding to the iterative result with the minimum cost function value when the model converges as the final estimation result and outputting them.

[0051] Among them, the cost function can be expressed as: Where N is the total number of sampling points, They are amplitude estimation, transmitted information waveform estimation, and pseudo code sequence estimation.

[0052] Specifically, the process of iteratively optimizing and estimating the multipath channel receiving signal model based on the cost function can be designed to include:

[0053] Initialization, the initialization includes: randomly initializing the pseudo code sequence, and initializing the signal symbol sequence and each path amplitude to zero value;

[0054] Symbol detection and pseudo code sequence estimation, the symbol detection and pseudo code sequence estimation comprising: sorting the power of each channel according to the rough estimation results of each path delay and amplitude, and performing serial symbol detection and pseudo code sequence estimation in sequence according to the sorting of the power of each channel;

[0055] The cost function value of the current round is calculated according to the cost function to determine whether the model converges based on the cost function value. If the model converges, return to the initialization step, and loop the initialization and symbol detection and pseudocode sequence estimation steps for a specified number of times, and select the pseudocode sequence and signal symbol sequence corresponding to the iterative result with the minimum cost function value when the model converges as the final estimation result and output them. If the model does not converge, the pseudocode sequence estimation should be judged and go to the symbol detection and pseudocode sequence estimation steps for re-execution.

[0056] The serial symbol detection is performed in sequence according to the power ranking of each channel, which may include:

[0057] Based on the multipath channel receiving signal model, the previous symbol detection sequence and the pseudo code estimation sequence, the interference signal is reconstructed, the interference is cancelled and re-correlated using the maximum likelihood estimation criterion to obtain the information waveform estimation and the corresponding path signal amplitude;

[0058] Obtaining a weighted result of the information waveform of the detected path based on the signal amplitude and the phase difference between the information waveform of each path and the information waveform of the main path, and performing a hard symbol decision on the weighted result, wherein the main path is a channel path for which symbol detection is performed preferentially according to power sorting;

[0059] The reconstructed waveforms of other paths are updated according to the symbol hard decision, and the next path information sequence is estimated until all path information sequence estimations are completed. The symbol hard decision is performed on the weighted results of each path information waveform to obtain the final symbol detection sequence.

[0060] The pseudo code sequence estimation is performed in sequence according to the power ranking of each channel, which may include:

[0061] Based on the current information waveform and the pseudo code sequence estimation, the pseudo code sequence waveform of the corresponding path is obtained, and the pseudo code waveforms obtained by the other path estimation are correlated with the pseudo code waveform of the main path estimation to obtain the phase difference;

[0062] According to the phase difference, the pseudo code models of other paths are adjusted to be in phase with the main path pseudo code waveform, and the pseudo code waveforms of each path are weighted superimposed by amplitude, and the final pseudo code waveform is obtained based on the weighted superposition result, so as to obtain the pseudo code sequence by making a hard decision on the final pseudo code waveform.

[0063] In pseudo code estimation, the maximum likelihood sequence estimation idea is adopted, which can be obtained by equations (0.6) to (0.8):

[0064]

[0065]

[0066] Formula (0.6) corresponds to the interference cancellation structure, and formula (0.7) corresponds to the RAKE receiving structure, where p2 represents the power normalization parameter, which can be obtained by Calculated. This structure is based on the maximum likelihood estimation theory. By canceling the signal of the interference path, the single-path pseudo-code sequence is estimated and weighted superposition is performed on each path by amplitude. It should be noted that in order to reduce the impact of local errors and obtain a more stable estimate, the final pseudo-code sequence estimate must be hard-determined. At the same time, Figure 3 The symbol detection structure shown is different. Under the premise that the channel delay is much smaller than the information code width, there is a strong correlation between the information waveforms. Therefore, when estimating the pseudo code according to the power of each path, in order to avoid the accumulation and propagation of errors caused by the power estimation deviation during the offset process, the pseudo code sequence estimation value does not need to be updated in real time and fed back to the waveform reconstruction end. In addition, in order to reduce the impact of the signal component of the weaker power path on the pseudo code estimation, it is necessary to set a threshold for the multipath component involved in the pseudo code estimation in the pseudo code estimation structure, that is, when the path amplitude is greater than When can participate in pseudo code estimation, where hmax Indicates the channel gain of the path with the highest power in a multipath channel.

[0067] like Figure 4 The pseudo code estimation algorithm process shown in the figure can be specifically described as follows: first, based on the current information waveform and the pseudo code sequence iteration result, the pseudo code sequence waveform of the i-th path (i = 1, 2..L h ). The pseudo code waveforms obtained by estimating other paths are correlated with the main path estimation value to obtain the phase difference, and the pseudo code waveform is adjusted to be in phase with the main path waveform according to the phase difference. Finally, the pseudo code waveforms of each path are weighted superimposed by amplitude according to formula (0.7). After the pseudo code waveform is calculated according to formula (0.8), a hard decision is made to obtain the pseudo code sequence.

[0068] In the SIC+RAKE-based multipath long code DSSS signal pseudo code sequence estimation architecture, such as Figure 5 As shown in the figure, after the received signal is synchronized with the carrier and sampled at the chip interval, the delay of each path is estimated first, and the power of each path and other information are estimated at the same time. By randomly initializing the pseudo code seed multiple times, the iterative optimization estimation of the information sequence and the pseudo code sequence is realized, and the convergence estimation value corresponding to the minimum cost function is selected as the final estimation. The overall pseudo code of the multipath LC-DSSS signal pseudo code sequence estimation algorithm can be described as follows:

[0069] Step 1: Carrier estimation, down-conversion and carrier synchronization, timing synchronization and extraction are performed on the received signal, and the pseudo code period L is estimated;

[0070] Step 2: Use the delay estimation algorithm to calculate the ξ value; take the position greater than 0.3 times the maximum value as the multipath delay and sort it by power. Assume that the channel delays in descending order of the amplitude of each propagation path are

[0071] Step 3: Randomly initialize the pseudo code sequence, the information symbol sequence and the amplitude of each path, wherein the information symbol sequence and the amplitude of each path can be initialized to zero;

[0072] Step 4: According to the power order in step 2, the information waveform is estimated by equation (0.4), and the signal amplitude of the path is measured at the same time. The weighted result of the information waveform of the measured path is obtained by using formula (0.5), and the reconstructed waveform of other paths is updated after the symbol hard decision is made, and then the information sequence of the next path is estimated, until the end, and the final weighted result hard decision sequence is obtained by calculating formula (0.5);

[0073] Step 5: Calculate the main path (amplitude estimate greater than )'s pseudo code sequence weighted estimation result, and make a hard decision;

[0074] Step 6: Calculate the cost function and determine the convergence of the model. When the current cost function value is equal to the cost function value of the previous iteration process, the algorithm is considered to have converged and go to step 7. Otherwise, make a hard decision on the pseudo code sequence estimate and go to step 4.

[0075] Step 7: Repeat steps 3 to 6 multiple times, and select the pseudo code sequence and information symbol sequence corresponding to the iteration result with the minimum cost function value when the model converges as the final estimate of the signal pseudo code sequence and information symbol sequence.

[0076] In this algorithm, information waveform detection and pseudocode sequence estimation are both based on the maximum likelihood sequence estimation MLSE theory. At the same time, multiple random initialization optimizations are used to obtain better pseudocode estimation performance under the premise of reasonably setting the number of random initialization seeds.

[0077] Further, based on the above method, an embodiment of the present invention also provides a pseudo code sequence estimation system under a multipath channel based on RAKE reception and MLSE estimation, comprising: a signal coarse estimation module, a signal modeling module and a joint estimation module, wherein:

[0078] The signal rough estimation module is used to perform carrier timing synchronization and chip interval sampling on the received signal under the multipath channel to obtain pseudo code period estimation; and to perform rough estimation on the delay and amplitude of each path of the received signal after interval sampling;

[0079] A signal modeling module is used to construct a multipath channel receiving signal model based on the rough estimation of each path delay and amplitude, so that the multipath channel receiving signal model uses self-interference cancellation SIC and RAKE receiving detection algorithm to perform signal symbol detection and pseudo code estimation;

[0080] The joint estimation module is used to set the cost function for jointly estimating the information symbol and the pseudo code sequence based on the multi-channel receiving signal model, iteratively optimize and estimate the multipath channel receiving signal model based on the cost function and using the maximum likelihood estimation MLSE, and take the pseudo code sequence and information symbol sequence corresponding to the iterative result with the minimum cost function value when the model converges as the final estimation result and output it.

[0081] In order to verify the effectiveness of this solution, the following is a further explanation based on experimental data:

[0082] Under multipath channels, the computational complexity of the overall multipath LC-DSSS signal pseudo code sequence estimation algorithm is mainly concentrated in steps 2, 4, and 5. Among them, the computational complexity of step 2 is O(GL 4 ), where G is the information code width and L is the pseudo code period. Due to the use of interference cancellation architecture, its complexity is O(Nλ p MLLh 2 ),L h It indicates the number of paths in a multipath channel. It can be seen that in a multipath channel, the algorithm complexity will be significantly increased, and is closely related to the pseudo code period, the number of multipaths, etc.

[0083] In order to measure the performance of the decision-assisted pseudo code estimation algorithm in this case, two NPLC-DSSS signals with different parameters are selected for algorithm performance testing. The parameters are shown in Table 1. The pseudo code sequence uses m sequence or truncated m sequence. The relationship between the NMSE of the pseudo code estimation and the signal-to-noise ratio and the amount of data is as follows: Figure 6 and 7 As shown. The algorithm performance under multipath channel was also tested, where the simulated channel was a 3-path chip spacing channel h = [0.6 0 0 1 0 0 0.9]. The segmented singular value decomposition method (segmented SVD) and the differential singular value decomposition method (differential SVD) were selected for comparison.

[0084] Table 1 Experimental signal parameters

[0085]

[0086] Figure 6 The anti-noise performance of the pseudo code estimation algorithm in this case is demonstrated under two parameter conditions. Figure 6 As can be seen from (a) in the figure, under the Gaussian white noise channel, the performance of the proposed algorithm is close to the theoretical boundary and is better than the differential eigenvalue decomposition and piecewise eigenvalue decomposition methods at all signal-to-noise ratios. Figure 6 (b) shows the situation when the pseudo code period is large, and the results are consistent with Figure 6 (a) in the figure is basically the same, which shows that the pseudo code sequence length has little effect on the algorithm performance. At the same time, under the multipath channel, the two comparison algorithms are basically ineffective. In comparison, although the algorithm performance of this scheme has degraded to a certain extent, it still has a high estimation accuracy, which shows the effectiveness of the proposed multipath algorithm.

[0087] Figure 7 It shows how the algorithm performance changes with the amount of data. Figure 7 It can be seen that the algorithm in this case can also be close to the theoretical boundary when the amount of data is small under the Gaussian white noise channel, which is about 2dB better than the segmentation method and more than 5dB better than the differential method. Although the algorithm performance in the multipath channel has some degradation, it is far better than the comparison algorithm, which shows the comprehensive advantages of the algorithm.

[0088] The iterative optimization algorithm needs to randomly initialize parameters multiple times to achieve the optimization of the global extreme value. The experiment mainly examines the convergence probability and average number of iterations of a single random initialization seed to explore the problem of setting the number of random initializations. Among them, the convergence probability is defined as the cost function J satisfies JJ min≤1%×J min The ratio of the number of seeds to the number of all randomly initialized seeds, where J min represents the cost function value under global convergence, which is obtained by the minimum cost function value after a large number of randomly initialized seeds converge. The average number of iterations of various seeds is also counted. Table 2 shows the convergence probability and average number of iterations of a single randomly initialized seed of the proposed algorithm under different signal-to-noise ratios and data volumes. The simulation signal parameters are shown in Table 1. The multipath channel is a 3-path chip-spaced channel, and the channel coefficient is h = [0.6 0 0 1 0 0 0.9].

[0089] Table 2 Convergence probability P of a single seed c and the average number of iterations λ p

[0090]

[0091] As can be seen from Table 2, the convergence probability increases with the increase of signal-to-noise ratio and the amount of data, and the average number of iterations decreases with the increase of signal-to-noise ratio. Comparing signals 1 and 2, it can be found that when the information code width is constant, the larger the pseudo code sequence length, the smaller the convergence probability and the larger the average number of iterations. In a multipath channel, the convergence is worse than that in a Gaussian channel and more iterations are required.

[0092] When the convergence probability of a single seed is low, the number of initialized seeds needs to be increased to obtain a stable global convergence estimation result. The above experimental data show that this scheme can be applied to pseudo code sequence estimation under different signal-to-noise ratios, can adapt to signal processing requirements under various parameters, and has good application prospects in the field of signal processing.

[0093] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0094] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0095] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0096] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0097] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A pseudo code sequence estimation method in a multipath channel based on RAKE reception and MLSE estimation, characterized in that: Include: Carrier timing synchronization and chip interval sampling are performed on the received signal under the multipath channel to obtain pseudo code period estimation; and a rough estimate of the delay and amplitude of each path of the received signal after interval sampling is performed; Constructing a multipath channel receiving signal model based on rough estimation of each path delay and amplitude, so that the multipath channel receiving signal model uses self-interference cancellation SIC and RAKE receiving detection algorithm to perform signal symbol detection and pseudo code estimation; Based on the multi-channel receiving signal model, a cost function for jointly estimating information symbols and pseudo-code sequences is set. Based on the cost function and using the maximum likelihood estimation (MLSE), the multipath channel receiving signal model is iteratively optimized and estimated. The pseudo-code sequence and information symbol sequence corresponding to the iterative result with the minimum cost function value when the model converges are taken as the final estimation results and output.

2. The method for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation according to claim 1, characterized in that: The multipath channel receiving signal model is expressed as: Among them, L h Indicates the number of multipaths, h l and τ l They represent the amplitude and delay of the lth propagation path, n is the sampling point, A is the signal amplitude, b m represents the mth information symbol, τ represents the signal receiving delay, represents the number of information symbols under the lth path, M represents the number of pseudo code cycles, L is the pseudo code sequence length, G is the spreading factor, q(n) represents the shaping filter waveform, represents the pseudo code sequence, and v(n) is the channel Gaussian white noise.

3. The method for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation according to claim 2, characterized in that: The cost function is expressed as: Where N is the total number of sampling points, They are amplitude estimation, transmitted information waveform estimation, and pseudo code sequence estimation.

4. The method for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation according to claim 1, characterized in that: The process of iteratively optimizing and estimating the multipath channel receiving signal model based on the cost function includes: Initialization, the initialization includes: randomly initializing the pseudo code sequence, and initializing the signal symbol sequence and each path amplitude to zero value; Symbol detection and pseudo code sequence estimation, the symbol detection and pseudo code sequence estimation comprising: sorting the power of each channel according to the rough estimation results of each path delay and amplitude, and performing serial symbol detection and pseudo code sequence estimation in sequence according to the sorting of the power of each channel; The cost function value of the current round is calculated according to the cost function to determine whether the model converges based on the cost function value. If the model converges, return to the initialization step, and loop the initialization and symbol detection and pseudocode sequence estimation steps for a specified number of times, and select the pseudocode sequence and signal symbol sequence corresponding to the iterative result with the minimum cost function value when the model converges as the final estimation result and output them. If the model does not converge, the pseudocode sequence estimation should be judged and go to the symbol detection and pseudocode sequence estimation steps for re-execution.

5. The method for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation according to claim 4, characterized in that: Serial symbol detection is performed in sequence according to the power ranking of each channel, including: Based on the multipath channel receiving signal model, the previous symbol detection sequence and the pseudo code estimation sequence, the interference signal is reconstructed, the interference is cancelled and re-correlated using the maximum likelihood estimation criterion to obtain the information waveform estimation and the corresponding path signal amplitude; Obtaining a weighted result of the information waveform of the detected path based on the signal amplitude and the phase difference between the information waveform of each path and the information waveform of the main path, and performing a hard symbol decision on the weighted result, wherein the main path is a channel path for which symbol detection is performed preferentially according to power sorting; The reconstructed waveforms of other paths are updated according to the symbol hard decision, and the next path information sequence is estimated until all path information sequence estimations are completed. The symbol hard decision is performed on the weighted results of each path information waveform to obtain the final symbol detection sequence.

6. The method for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation according to claim 4, characterized in that: According to the power ranking of each channel, the pseudo code sequence is estimated in sequence, including: Based on the current information waveform and the pseudo code sequence estimation, the pseudo code sequence waveform of the corresponding path is obtained, and the pseudo code waveforms obtained by the other path estimation are correlated with the pseudo code waveform of the main path estimation to obtain the phase difference; According to the phase difference, the pseudo code models of other paths are adjusted to be in phase with the main path pseudo code waveform, and the pseudo code waveforms of each path are weighted superimposed by amplitude, and the final pseudo code waveform is obtained based on the weighted superposition result, so as to obtain the pseudo code sequence by making a hard decision on the final pseudo code waveform.

7. The method for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation according to claim 4, characterized in that: Judging whether the model has converged based on the cost function value includes: The cost function value of the current round is compared with the cost function value of the previous round of iteration. If the two cost function values ​​are equal, the model is determined to have converged.

8. A pseudo code sequence estimation system in a multipath channel based on RAKE reception and MLSE estimation, characterized in that: It includes: signal rough estimation module, signal modeling module and joint estimation module, among which, The signal rough estimation module is used to perform carrier timing synchronization and chip interval sampling on the received signal under the multipath channel to obtain pseudo code period estimation; and to perform rough estimation on the delay and amplitude of each path of the received signal after interval sampling; A signal modeling module is used to construct a multipath channel receiving signal model based on the rough estimation of each path delay and amplitude, so that the multipath channel receiving signal model uses self-interference cancellation SIC and RAKE receiving detection algorithm to perform signal symbol detection and pseudo code estimation; The joint estimation module is used to set the cost function for jointly estimating the information symbol and the pseudo code sequence based on the multi-channel receiving signal model, iteratively optimize and estimate the multipath channel receiving signal model based on the cost function and using the maximum likelihood estimation MLSE, and take the pseudo code sequence and information symbol sequence corresponding to the iterative result with the minimum cost function value when the model converges as the final estimation result and output it.

9. An electronic device, characterized in that: include: at least one processor, and a memory coupled to the at least one processor; The memory stores a computer program, and the computer program can be executed by the at least one processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 can be implemented.

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