Pseudo-code sequence estimation method and system in multipath channel based on RAKE reception and MLSE estimation

Abstract

The present invention relates to the field of signal processing technology, and more particularly to a method and system for estimating a pseudo-code sequence in a multipath channel based on RAKE reception and MLSE estimation. The method processes a received signal in a multipath channel to obtain a pseudo-code period estimate, and performs a rough estimate of the time delay and amplitude of each path of the received signal. A multipath channel received signal model is constructed based on the rough estimate of the time delay and amplitude of each path, so that the multipath channel received signal model performs signal symbol detection and pseudo-code estimation using self-interference cancellation (SIC) and a RAKE reception detection algorithm. A cost function for jointly estimating information symbols and pseudo-code sequences is set based on the multipath received signal model. Based on the cost function and using maximum likelihood estimation (MLSE), the multipath channel received 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 output as the final estimation results. The present invention is applicable to signal processing requirements under low signal-to-noise ratios 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, including low signal-to-noise ratio (SNR), strong interference immunity, low interception rate (ROR), and ability to suppress multipath effects. Long code DSSS (LC-DSSS) signals modulate multiple information symbols with a periodic pseudo-code sequence, severely disrupting the periodicity of the pseudo-code sequence. Furthermore, in non-cooperative communications, knowledge of the pseudo-code sequence used for spreading is required to blindly despread the intercepted signal. Therefore, blind estimation of the pseudo-code sequence of LC-DSSS signals has become a hot topic in DSSS communication system parameter estimation. One approach to blindly estimating the pseudo-code sequence of LC-DSSS signals in unknown multipath channels is to treat the DSSS signal as a regular signal and perform channel estimation and equalization to eliminate or mitigate the effects of the channel, thereby achieving pseudo-code sequence estimation. However, this approach is relatively effective at high SNRs and is inapplicable at low SNRs due to the difficulty in convergence of the equalization algorithm. Another approach is to model the long code signal as a short code DSSS signal under a missing data model based on the signal subspace, and then process it according to the method of the short code DSSS signal. However, the approximate processing error of 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 problems of limited application of existing LC-DSSS signal pseudo code sequence estimation in low signal-to-noise ratio and 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 in 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 in 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 estimates of each path's time delay and amplitude, so that the multipath channel receiving signal model uses self-interference cancellation (SIC) and a 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 is set to jointly estimate the information symbols and pseudo-code sequences. 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 in a multipath channel based on RAKE reception and MLSE estimation of the present invention, a process of iteratively optimizing and estimating a multipath channel reception signal model based on a cost function further comprises:

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

[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 channel powers;

[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, it returns to the initialization step and loops through the initialization, symbol detection, and pseudocode sequence estimation steps for a specified number of times. The pseudocode sequence and signal symbol sequence corresponding to the iteration result with the minimum cost function value when the model converges are selected as the final estimation results and output. If the model does not converge, the pseudocode sequence estimation should be judged and the symbol detection and pseudocode sequence estimation steps are re-executed.

[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, including:

[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 and interference cancellation and re-correlated using the maximum likelihood estimation criterion to obtain the information waveform estimate and the corresponding path signal amplitude;

[0014] Obtaining weighted information waveforms of detected paths based on signal amplitude and phase difference between each path's information waveform and a main path's information waveform, and performing hard symbol decisions on the weighted results. The main path is the channel path prioritized for symbol detection based on power sorting.

[0015] Based on the symbol hard decision, the reconstructed waveforms of other paths are updated, and the next path information sequence is estimated until all path information sequence estimates are completed. The symbol hard decision is then performed on the weighted results of the path information waveforms 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] The pseudo code sequence waveform of the corresponding path is obtained based on the current information waveform and the pseudo code sequence estimation, and the pseudo code waveforms obtained by the other path estimations 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 weightedly superimposed by amplitude. The final pseudo code waveform is obtained based on the weighted superposition result, and the pseudo code sequence is obtained 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] Compare the cost function value of the current round 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 coarse estimation module is used to perform carrier timing synchronization and chip interval sampling on the received signal in the multipath channel to obtain pseudo code period estimation; and to perform coarse estimation of 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 symbols and pseudo-code sequences based on the multi-channel received signal model, iteratively optimize and estimate the multipath channel received signal model based on the cost function and using the maximum likelihood estimation (MLSE), and output 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.

[0025] Beneficial effects of the present invention:

[0026] The present invention makes a rough estimate of the time delay and amplitude of each path, and constructs a joint estimation architecture of information symbols and pseudocode sequences based on serial self-interference cancellation and RAKE detection. By randomly initializing the pseudocode seeds multiple times, it realizes iterative optimization estimation of information sequences and pseudocode sequences, improves the pseudocode sequence estimation performance, and is suitable for 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 Schematic diagram of the pseudo code sequence estimation process in a multipath channel based on RAKE reception and MLSE estimation in an embodiment;

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

[0029] Figure 3 Schematic diagram of the demodulation algorithm architecture based on SIC+RAKE in the embodiment;

[0030] Figure 4 Schematic diagram of the MLSE-based pseudo code sequence estimation architecture in an embodiment;

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

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

[0033] Figure 7 Schematic diagram of the change of pseudo code estimation NMSE with data volume 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 with reference to 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 of these different paths after different delays, polarity inversion, 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 response to the problems of limited application in the existing pseudocode sequence estimation process, an embodiment of the present invention provides a pseudocode sequence estimation method under 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 signals. According to the LC-DSSS signal model, its multipath is expressed as

[0038]

[0039] Among them 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 reception 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 both require the completion of power estimation of each signal path first. 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 this preprocessing and rough estimation are not high here. Specifically, the LC-DSSS-CDMA signal multi-user delay blind estimation algorithm can be used as a reference. Figure 2 As shown in Figure 2, 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 periods, 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 of the time delay and power ranking of each path in the multipath channel can also be obtained at the same time, which corresponds to Figure 2 Based on this, symbol detection and pseudo code estimation based on RAKE reception and SIC can be realized.

[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. The phase difference between the estimated value of the other path information waveform and 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 the large power difference between the paths, the interference cancellation part adopts a serial structure; Equation (0.5) corresponds to RAKE reception.

[0049] Figure 3In the illustrated architecture, serial symbol detection is performed sequentially based on the power ranking of each path. The interference signal is reconstructed and then correlated using the previous detection result sequence and the estimated pseudo-code sequence. The detection results are then summed and judged after adjusting for phase and delay, and the information symbol sequence is dynamically updated in real time. Phase adjustment can be performed using correlation to obtain phase difference, while delay information can be estimated based on previous calculations.

[0050] S103. A cost function is set for jointly estimating information symbols and pseudo-code sequences based on the multi-channel received signal model. Based on the cost function and using maximum likelihood estimation (MLSE), the multipath channel received 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.

[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 received 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;

[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 channel powers;

[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, it returns to the initialization step and loops through the initialization, symbol detection, and pseudocode sequence estimation steps for a specified number of times. The pseudocode sequence and signal symbol sequence corresponding to the iteration result with the minimum cost function value when the model converges are selected as the final estimation results and output. If the model does not converge, the pseudocode sequence estimation should be judged and the symbol detection and pseudocode sequence estimation steps are re-executed.

[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 and interference cancellation and re-correlated using the maximum likelihood estimation criterion to obtain the information waveform estimate and the corresponding path signal amplitude;

[0058] Obtaining weighted information waveforms of detected paths based on signal amplitude and phase difference between each path's information waveform and a main path's information waveform, and performing hard symbol decisions on the weighted results. The main path is the channel path prioritized for symbol detection based on power sorting.

[0059] Based on the symbol hard decision, the reconstructed waveforms of other paths are updated, and the next path information sequence is estimated until all path information sequence estimates are completed. The symbol hard decision is then performed on the weighted results of the path information waveforms 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] The pseudo code sequence waveform of the corresponding path is obtained based on the current information waveform and the pseudo code sequence estimation, and the pseudo code waveforms obtained by the other path estimations 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 weightedly superimposed by amplitude. The final pseudo code waveform is obtained based on the weighted superposition result, and the pseudo code sequence is obtained 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 from formulas (0.6) to (0.8):

[0064]

[0065]

[0066] Equation (0.6) corresponds to the interference cancellation structure, and Equation (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 here 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 error accumulation and propagation caused by the deviation of the power estimation value during the cancellation 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 participating 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 estimated from the other paths are correlated with the main path estimate to obtain a phase difference. Based on this phase difference, the pseudo-code waveform is adjusted to be in phase with the main path waveform. Finally, the pseudo-code waveforms from each path are weighted and superimposed by amplitude according to equation (0.7). After the pseudo-code waveform is calculated according to equation (0.8), a hard decision is made to obtain the pseudo-code sequence.

[0068] In the multipath long code DSSS signal pseudo code sequence estimation architecture based on SIC+RAKE, such as Figure 5 As shown in the figure, after carrier synchronization and chip-interval sampling, the received signal is first estimated for each path delay. Information such as the power of each path is also estimated. By randomly initializing the pseudocode seed multiple times, iterative optimization estimation of the information sequence and pseudocode sequence is achieved, and the converged estimate corresponding to the minimum cost function is selected as the final estimate. The pseudocode of the overall multipath LC-DSSS signal pseudocode 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 propagation path amplitude are

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

[0072] Step 4: According to the power order in step 2, the information waveform is estimated by formula (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). After making a hard decision on the symbol, the reconstructed waveform of the other paths is updated. Then the information sequence of the next path is estimated. The process ends 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. If the current cost function value is equal to the cost function value of the previous iteration, the algorithm is considered to have converged and the process goes to step 7. Otherwise, a hard decision is made on the pseudo code sequence estimate and the process goes 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. Under the premise of reasonably setting the number of random initialization seeds, better pseudocode estimation performance can be obtained.

[0077] Furthermore, 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 coarse estimation module is used to perform carrier timing synchronization and chip interval sampling on the received signal in the multipath channel to obtain pseudo code period estimation; and to perform coarse estimation of 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 symbols and pseudo-code sequences based on the multi-channel received signal model, iteratively optimize and estimate the multipath channel received signal model based on the cost function and using the maximum likelihood estimation (MLSE), and output 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.

[0081] 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 is clear that in a multipath channel, the algorithm complexity will increase significantly, 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 is 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 shown in the following table: Figure 6 and 7 The algorithm performance was also tested under a multipath channel, where the simulated channel was a 3-path chip-interval channel with 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 noise resistance performance of the pseudo code estimation algorithm in this case is demonstrated under two parameter conditions. Figure 6 As shown in (a), under Gaussian white noise channel, the performance of the proposed algorithm is close to the theoretical boundary and outperforms 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 remains essentially the same, demonstrating that the pseudocode sequence length has little impact on algorithm performance. Furthermore, in multipath channels, the two compared algorithms essentially fail. In comparison, while the algorithm performance of our proposed solution degrades to some extent, it still maintains high estimation accuracy, demonstrating the effectiveness of the proposed multipath algorithm.

[0087] Figure 7 It shows how the algorithm performance changes with the amount of data. Figure 7 As can be seen, the proposed algorithm performs close to the theoretical bound even in Gaussian white noise channels with relatively small data volumes, outperforming the segmentation method by approximately 2dB and the differential method by over 5dB. While performance degrades somewhat in multipath channels, it still far outperforms the comparison algorithm, demonstrating the algorithm's comprehensive advantages.

[0088] The iterative optimization algorithm requires multiple random initialization parameters 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, obtained by minimizing the cost function value after convergence for a large number of randomly initialized seeds. The average number of iterations for various seeds is also calculated. Table 2 shows the convergence probability and average number of iterations for a single randomly initialized seed for 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 three-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] Table 2 shows that the convergence probability increases with increasing signal-to-noise ratio and data volume, while the average number of iterations decreases with increasing signal-to-noise ratio. Comparing signals 1 and 2, we find that for a given code width, a larger pseudo-code sequence length decreases the convergence probability and increases the average number of iterations. In multipath channels, convergence deteriorates compared to Gaussian channels, 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 shows that this scheme is applicable to pseudo-code sequence estimation under different signal-to-noise ratios and can adapt to signal processing requirements under various parameters, showing 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] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0095] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of function in the above description. 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 performed by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk. Alternatively, all or part of the steps in the above embodiment can be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or software functional modules. The present invention is not limited to any specific combination of hardware and software.

[0097] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection 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 in 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; A multipath channel receiving signal model is constructed based on the rough estimation of each path delay and amplitude, so that the multipath channel receiving signal model uses the self-interference cancellation SIC and RAKE receiving detection algorithm to perform signal symbol detection and pseudo code estimation; 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 reception 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, v(n) is the channel Gaussian white noise; Based on the multi-channel received signal model, the information symbol and pseudo code sequence are jointly estimated with the cost function, which 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; based on the cost function and using maximum likelihood estimation MLSE, the multipath channel receiving signal model is iteratively optimized and estimated, and 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, wherein: 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; 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 channel powers; 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, it returns to the initialization step and loops through the initialization, symbol detection, and pseudocode sequence estimation steps for a specified number of times. The pseudocode sequence and signal symbol sequence corresponding to the iteration result with the minimum cost function value when the model converges are selected as the final estimation results and output. If the model does not converge, the pseudocode sequence estimation should be judged and the symbol detection and pseudocode sequence estimation steps are re-executed.

3. The method for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation according to claim 2, wherein: 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 and interference cancellation and re-correlated using the maximum likelihood estimation criterion to obtain the information waveform estimate and the corresponding path signal amplitude; Obtaining weighted information waveforms of detected paths based on signal amplitude and phase difference between each path's information waveform and a main path's information waveform, and performing hard symbol decisions on the weighted results. The main path is the channel path prioritized for symbol detection based on power sorting. Based on the symbol hard decision, the reconstructed waveforms of other paths are updated, and the next path information sequence is estimated until all path information sequence estimates are completed. The symbol hard decision is then performed on the weighted results of the path information waveforms to obtain the final symbol detection sequence.

4. The method for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation according to claim 2, wherein: The pseudo code sequence is estimated in sequence according to the power ranking of each channel, including: The pseudo code sequence waveform of the corresponding path is obtained based on the current information waveform and the pseudo code sequence estimation, and the pseudo code waveforms obtained by the other path estimations 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 weightedly superimposed by amplitude. The final pseudo code waveform is obtained based on the weighted superposition result, and the pseudo code sequence is obtained by making a hard decision on the final pseudo code waveform.

5. The method for estimating a pseudo code sequence in a multipath channel based on RAKE reception and MLSE estimation according to claim 2, wherein: Judging whether the model has converged is based on the cost function value, including: Compare the cost function value of the current round 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.

6. A pseudo code sequence estimation system in a multipath channel based on RAKE reception and MLSE estimation, characterized in that: It includes: signal coarse estimation module, signal modeling module and joint estimation module, among which, The signal coarse estimation module is used to perform carrier timing synchronization and chip interval sampling on the received signal in the multipath channel to obtain pseudo code period estimation; and to perform coarse estimation of the delay and amplitude of each path of the received signal after interval sampling; The 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 can use the self-interference cancellation SIC and RAKE receiving detection algorithm to perform signal symbol detection and pseudo code estimation; 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 reception 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, v(n) is the channel Gaussian white noise; 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 received signal model. 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; based on the cost function and using maximum likelihood estimation MLSE, the multipath channel receiving signal model is iteratively optimized and estimated, and 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.

7. 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 5.

8. 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 5 can be implemented.

Images