Communication waveform performance evaluation method based on OTFS

Abstract

The invention discloses a communication waveform performance evaluation method based on OTFS, and belongs to the technical field of mobile communication. According to the method, waveform parameters, channel parameters and receiving end parameters are selected as input indexes, an improved variable coefficient method and an improved analytic hierarchy process are used as main and objective weighting modes, weighting results of the two parameters are combined through the game theory, and a weighted communication waveform performance evaluation index system is constructed; a good and inferior solution distance method is adopted as an evaluation method, according to the difference between a communication waveform performance scheme to be evaluated and an empowerment communication waveform performance evaluation index system, the fitting degrees between different schemes and an ideal solution and between different schemes and a negative ideal solution are calculated, and the higher the fitting degree is, the better the communication waveform performance under the scheme is. And different schemes are evaluated and sorted according to the fitting degree data, so that rapid comparison of the transmission performance is realized. The method is applied to communication scenes with complex channel environments and diversified performance requirements, and rapid comparison of the transmission performance is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of mobile communications, and particularly relates to a method for evaluating the communication waveform performance based on OTFS. Background Art

[0002] In the fourth-generation mobile communication system, the orthogonal frequency division multiplexing (OFDM) technology has been widely adopted due to its excellent spectral efficiency and multipath interference resistance. However, in high-speed mobile scenarios, the OFDM technology faces severe challenges, mainly manifested as severe inter-symbol interference (ISI) and inter-carrier interference (ICI), which are mainly caused by Doppler frequency shift and time-selective fading. To address these problems, an innovative orthogonal time frequency space (OTFS) modulation technology has been proposed. The core advantage of the OTFS technology lies in its ability to perform data modulation in the delay-Doppler (DD) domain, effectively resisting the time-selective fading caused by Doppler frequency shift, thereby maintaining the reliability and stability of communication in high-speed mobile environments.

[0003] The OTFS technology transforms the time-varying multipath channel into the DD domain, enabling all symbols in the transmission unit to experience almost the same and slowly varying sparse channel, thus significantly improving the system performance. In addition, the peak-to-average power ratio (PAPR) of the OTFS signal is lower than that of OFDM, and the bit error rate is better than that of OFDM. Given the obvious performance advantages of the OTFS technology, it has become a current research hotspot.

[0004] Therefore, it is particularly important to develop a method that can accurately and quickly evaluate the communication performance of the OTFS system. This patent proposes a method for evaluating the performance of communication waveforms applicable to complex scenarios. Taking OTFS as an example, the feasibility and accuracy of this method are verified through experiments. This method can not only provide a theoretical basis for the performance comparison of the OTFS system, but also has important practical significance for guiding its further communication system design and deployment.

[0005] At the current stage, the main metric for evaluating the communication performance of the OTFS system is the bit error rate (BER). However, the current performance evaluation criteria have the following drawbacks. First, in the face of the diversification of technical requirements, relying solely on the BER as the evaluation criterion is relatively single and has limitations. Second, the process of obtaining the BER of the OTFS system takes a long time and cannot quickly reflect the performance differences of different OTFS systems. Therefore, it is particularly necessary to conduct a comprehensive analysis and performance evaluation of the OTFS system based on the BER. This comprehensive analysis method can not only evaluate the system performance from multiple dimensions, complement the deficiencies of the BER evaluation, but also guide the optimal configuration of the OTFS system parameters according to the channel conditions to achieve better communication performance, and provide theoretical support for the improvement and optimization of the system.

[0006] The literature "Cloud computing security evaluation and countermeasure based on AHP-fuzzy comprehensive evaluation" proposed an evaluation model based on the analytic hierarchy process and fuzzy comprehensive evaluation method to evaluate the advantages and disadvantages of the network platform security. This evaluation model first divides the platform cloud computing security into three parts: data security, virtual security, and application security according to modules, and then constructs an evaluation index system. Secondly, the analytic hierarchy process is applied to obtain the subjective weights of each index. Finally, the advantages and disadvantages evaluation results are obtained based on the element membership degree of the fuzzy comprehensive evaluation method.

[0007] The literature "Effectiveness evaluation of early-warning aircraft based on hierarchy TOPSIS" proposed an evaluation model for the effectiveness evaluation of early-warning aircraft using the distance from the ideal solution method for evaluating the advantages and disadvantages. The entropy weight method was used to calculate the objective weights, and the evaluation results were obtained by applying the method of approaching the ideal solution layer by layer. However, in this model, the weights were assigned only based on the information carried by the index data itself, and the weight assignment results were not complete, failing to reflect the degree of influence of each index on the system performance theoretically. The literature "Link-16 anti-jamming performance evaluation based on grey relational analysis and cloud model" proposed an evaluation model for the Link-16 data link. First, the entropy weight method and the analytic hierarchy process were used to obtain the subjective and objective weights of each index. Secondly, the combined weight was assigned by linear addition. Finally, the performance of the data link was judged based on the grey relational analysis method. The evaluation models in the literature did not consider the horizontal comparison of the parameters between the indexes in the objective weight assignment module, and it was impossible to ensure that the indexes had good independence. In the subjective weight assignment module, the analytic hierarchy process was applied, and high-complexity calculations were required to ensure its consistency. To address these problems, this patent improves the objective weight assignment method using the correlation coefficient and improves the analytic hierarchy process using the optimal transfer matrix, thereby reducing the calculation complexity while ensuring good independence between the indexes. Summary of the Invention

[0008] The object of the present invention is to propose a Hierarchical-Game-TOPSIS Integrated Decision Analysis Model (HGTIM) from the comprehensive analysis dimension to address the problems of single index and long time consumption in the current communication waveform performance evaluation system. This model selects waveform parameters, channel parameters, and receiver parameters as input indexes, uses the improved coefficient of variation method and the improved analytic hierarchy process as the subjective and objective weight assignment methods, combines the two weight assignment results through game theory, uses the distance from the ideal solution method for evaluating the advantages and disadvantages as the evaluation method, and obtains evaluation results adapted to the BER results, thereby realizing the rapid comparison of transmission performance. Taking the OTFS waveform as an example, this model can compare the performance of each scheme under different waveform parameters, different channels, and different receiving algorithms, obtain the optimal transmission scheme, and use this as a basis to guide the selection of system parameters and improve the transmission efficiency.

[0009] The present invention provides a method for evaluating the performance of a communication waveform based on OTFS, including the following steps:

[0010] Step 1: Obtain different waveform parameters, channel parameters, and receiving algorithm parameters of the communication waveform of OTFS, construct a communication waveform performance evaluation index system, and introduce the transmitted waveform index, channel index, and receiving algorithm index into the communication waveform performance evaluation index system;

[0011] Step 2: The weight values of each evaluation index are mainly subjectively and objectively weighted by the improved analytic hierarchy process and the improved coefficient of variation method, and the two weighted results are combined through game theory to obtain the weighted communication waveform performance evaluation index system;

[0012] Step 3: Use the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate the communication waveform performance, input the communication waveform performance scheme to be evaluated, and the scheme includes waveform parameters, channel parameters, and receiving algorithm parameters; calculate the fitness between different schemes and the ideal solution and the negative ideal solution through the waveform parameters, channel parameters, receiving algorithm parameters, and the weighted results, and take the scheme with the higher fitness as the optimal scheme for the communication waveform performance, and evaluate and rank different schemes.

[0013] Further, the waveform index includes the modulation mode index D I and the system time-frequency resource index MN I ; the modulation mode index D I is the calculated score for signals with different modulation orders and different geometric characteristics, and the system time-frequency resource index MN I is the calculated score for the communication performance with different numbers of subcarriers and sub-symbols;

[0014] D I =(D b +D t )·Q

[0015] where Q is the number of modulation constellation points; D b is the box dimension; D t is the information dimension;

[0016]

[0017] where N δ is the minimum number of grids covering the spectrum X(m); X is the signal sequence; δ is the minimum abscissa interval; m is the number of signal spectrum sampling points (m = 1, 2,..., N);

[0018]

[0019] where P k is the frequency at which the signal appears at each sampling point;

[0020]

[0021] where M is the number of subcarriers of the system; N is the number of sub-symbols of the system.

[0022] Further, the channel metric h I is a comprehensive calculation score for different delay-Doppler, different multipath numbers, and different power attenuation conditions of each path:

[0023]

[0024] where τ is the delay of each path; fd is the Doppler shift; P is the power attenuation of each path; taps is the number of multipaths; and l is the l-th channel.

[0025] Further, the receiving algorithm metric B I is a calculation score for the communication performance of different receiving algorithms under different signal-to-noise ratios;

[0026]

[0027] where min(BER) is the optimal bit error rate result of multiple receiving algorithms under high signal-to-noise ratio conditions; max(BER) is the worst bit error rate result of multiple receiving algorithms under high signal-to-noise ratio conditions; and BER f is the bit error rate performance of a certain receiving algorithm under specific signal-to-noise ratio conditions.

[0028] Further, in step 2, the weights of each evaluation index are calculated according to the improved analytic hierarchy process. First, a judgment matrix is established based on the importance degree of relevant elements in the criterion layer and the scheme layer. The element b in the judgment matrix ij :

[0029]

[0030] where b is the comparison benchmark, b ∈ [1, 9]; r k and r j respectively represent the importance ranking indices of two different indices, and the calculation methods are both summing by column; r max and r min are respectively the maximum and minimum values in the calculated importance ranking indices;

[0031]

[0032] where the element a ij of the comparison matrix represents the importance degree of the i-th index relative to the j-th index;

[0033] Then, based on the judgment matrix, the optimal transfer matrix and the quasi-optimal consistent matrix are obtained;

[0034]

[0035] where the element c kjand the element d in the quasi-optimal consistent matrix kj ; n is the number of evaluation indicators;

[0036] Finally, the weights of each indicator are obtained:

[0037]

[0038] where w j is the subjective weight assignment result of an expert for the j-th indicator.

[0039] Furthermore, based on the cloud model theory, the weights assigned by multiple experts are integrated to obtain the subjective weight assignment result w hj :

[0040]

[0041] where w tj is the weight assignment result of the t-th expert for the j-th evaluation indicator in the subjective weight assignment.

[0042] Furthermore, in step 2, the improved coefficient of variation method is used. First, according to the indicator x in the evaluation indicator system ij calculate the mean value and the standard deviation s j of each indicator parameter, and obtain the coefficient of variation υ j of each indicator parameter:

[0043]

[0044] where x ij is the data of the j-th indicator under the i-th scheme; υ j is the coefficient of variation of the j-th indicator;

[0045] Then, after quantifying the conflict degree u j between indicators according to the Pearson correlation coefficient, the objective weight assignment result w ej of each indicator is obtained:

[0046]

[0047] where r kj is the correlation coefficient between the k-th interfering evaluation indicator and the j-th interfering evaluation indicator.

[0048] Furthermore, in step 2, a combined weight is constructed based on game theory

[0049]

[0050] where is the combined weight assignment result of the j-th element in the combined weight vector; β h and βe They are the linear coefficients of the subjective weight and the objective weight respectively.

[0051] Further, the specific steps in step 3 include the following steps:

[0052] Step 1: Obtain the normalized data matrix Y according to the waveform parameters, channel parameters, receiving algorithm parameters and the combined weighting result;

[0053]

[0054] where y ij is the weighted value of the jth evaluation index under the ith scheme;

[0055] Step 2: Determine the positive and negative ideal solutions. The calculation of the positive and negative ideal solutions for the benefit-type indicators and cost-type indicators is as follows:

[0056]

[0057] where J + and J - represent that the evaluation index is a benefit-type indicator or a cost-type indicator respectively; Y i + and Y i - are the optimal performance parameters and the worst performance parameters of the ith evaluation index respectively;

[0058] Step 3: Calculate the distance of each target to the positive ideal solution and the negative ideal solution:

[0059]

[0060] where L i + represents the distance of the scheme to the positive ideal solution, and L i - represents the distance of the scheme to the negative ideal solution; y ij represents the weighted value of the jth evaluation index under the ith scheme.

[0061] Step 4: Calculate the relative fitness of different parameter system schemes. Normalize the distances of each scheme to the positive ideal solution and the negative ideal solution to obtain the fitness, and sort the fitness from high to low.

[0062] The beneficial effects of the present invention are as follows:

[0063] 1. The present invention applies an improved analytic hierarchy process in the subjective weighting module. The optimal transfer matrix avoids high-complexity calculations while ensuring its consistency. The cloud model unifies the judgment opinions of multiple experts and avoids singularity. At the same time, the present invention improves the objective weighting method using the correlation coefficient, thereby ensuring good independence between indicators.

[0064] 2. The present invention proposes a combined weighting evaluation model. The evaluation system combines the improved analytic hierarchy process and the improved coefficient of variation method in the weighting part, and applies game theory as the subjective and objective combined weighting method, which can retain the effective information of the subjective and objective weighting results to the greatest extent. The result is more reasonable and persuasive, avoiding the defects of the weighting result.

[0065] 3. The present invention divides the OTFS communication waveform into modules, extracts features according to different functional modules to construct an evaluation index system, making the evaluation of communication performance more targeted and comprehensive. The present invention is applied to communication scenarios with complex channel environments and diverse performance requirements to achieve rapid comparison of transmission performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flowchart of the communication waveform performance evaluation method based on OTFS of the present invention;

[0067] Figure 2 is a diagram of the communication waveform performance evaluation index system of the present invention;

[0068] Figure 3 is a data diagram of the group of better performance solutions in the embodiments of the present invention;

[0069] Figure 4 is a data diagram of the group of slightly worse performance solutions in the embodiments of the present invention;

[0070] Figure 5 is a control variable performance comparison diagram of the embodiments of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0071] The present invention will be further described below with reference to the accompanying drawings.

[0072] The present invention discloses a communication waveform performance evaluation method based on OTFS. As Figure 1 shown, waveform parameters, channel parameters, and receiver parameters are selected as input indicators. The improved coefficient of variation method and the improved analytic hierarchy process are used as subjective and objective weighting methods. The weighting results of the two are combined through game theory, and the technique for order preference by similarity to ideal solution (TOPSIS) is used as the evaluation method to obtain an evaluation result adapted to the BER result, thereby achieving rapid comparison of transmission performance.

[0073] The techniques used in the present invention include the correlation coefficient method, the cloud model, and the game theory combined weighting method.

[0074] A. Correlation Coefficient Method

[0075] The calculation formula of Pearson correlation coefficient is as follows:

[0076]

[0077] where r ij represents the correlation coefficient between the i-th interference evaluation index and the j-th interference evaluation index, and S ik and S jk respectively represent the data normalization values of the i-th interference evaluation index and the j-th interference evaluation index under the k-th interference scheme; in this patent, the correlation coefficient method is used to conduct horizontal comparison between various index coefficients, consider the conflict degree between indexes, and thus ensure that the indexes have good independence.

[0078] B. Cloud Model

[0079] The cloud model theory uses a set of mutually independent parameters to jointly describe a digital feature and realizes the transformation between qualitative concepts and quantitative representations. This set of parameters are the expectation E x , entropy E n and hyperentropy He respectively, and the calculation methods of these three parameters are as follows:

[0080]

[0081]

[0082] where w ij is the j-th data value under the i-th group of vectors. In this patent, the cloud model is used to combine the multi-expert review opinions to avoid the influence of the subjective factors of a single expert on the accuracy of the evaluation results.

[0083] C. Game Theory

[0084] The core of game theory is to solve the linear coefficient between two elements using the Nash equilibrium idea and then perform combined weighting. First, based on the Nash equilibrium idea, the following optimal linear coefficient judgment condition is generated:

[0085]

[0086] Secondly, according to the curve extreme value theorem, the following formula is listed and the subjective and objective weight linear coefficients are calculated:

[0087]

[0088] where Wh and We respectively represent the subjective weight and the objective weight; βh and βe respectively represent the proportion coefficients of the subjective weight and the objective weight in the combined weighting; game theory is used for combined weighting in this patent.

[0089] Several Receiving Algorithms under the D-OTFS System

[0090] Taking several receiving algorithms of the OTFS system as examples, the present invention compares their system transmission efficiency and evaluation results to prove the rationality of the HGTIM evaluation model proposed in this patent. The three receiving algorithms selected in the present invention are the Zero Forcing (ZF) algorithm, the Minimum Mean Square Error (MMSE) algorithm, and the Message Passing Algorithm (MP).

[0091] The calculation formula of the ZF receiving algorithm is

[0092]

[0093] where H is the channel matrix, describing the mapping relationship between the transmitted signal and the received signal, and H H is the conjugate transpose of the channel matrix, and y is the received signal vector.

[0094] The calculation formula of the MMSE receiving algorithm is

[0095]

[0096] where x is the transmitted signal vector, y is the received signal vector, and K is the weighting coefficient matrix used to minimize the mean square error, and the calculation method is

[0097]

[0098] where R xx represents the autocorrelation matrix of the transmitted signal. This formula means that under the minimum mean square error criterion, the estimated value of the original signal is obtained by weighted averaging the received signal.

[0099] The MP receiving algorithm is an iterative detection algorithm that eliminates multipath interference by iteratively transmitting messages in the time-delay - Doppler domain. The iterative formula is

[0100] x (t+1) = x (t) + H T (y - Hx (t) ) (10)

[0101] where x (t) and x (t+1) respectively represent the estimated values of the information vector after the t-th iteration and the (t + 1)-th iteration, and H T is the transpose of the channel matrix. The iteration of the MP algorithm will continue until the change amount is less than the preset threshold or the maximum number of iterations is reached.

[0102] Example 1

[0103] A method for evaluating the performance of a communication waveform based on OTFS according to the present invention includes the following steps:

[0104] Step 1: As shown in Figure 2 , construct an evaluation index system, divide the OTFS system into three parts, and extract the transmitter index, channel index, and receiver index respectively. These are used as the parameter basis for comprehensively analyzing and evaluating the performance of the OTFS system.

[0105] Based on the modulation method and the time-frequency resources of the system in the transmission module, two index parameters D I and MN I are proposed, and the concept of fractal dimension is introduced. The fractal dimension is calculated from the signal sequence X and the minimum abscissa interval δ, that is

[0106]

[0107] where N δ is the minimum number of grids covering the spectrum X(m); X is the signal sequence; δ is the minimum abscissa interval; m is the number of signal spectrum sampling points (m = 1, 2,..., N); P m is the frequency at which the signal appears at each sampling point;

[0108] After obtaining the box dimension D b and the information dimension D t , based on this, calculate the modulation method index D I :

[0109] D I =(D b +D t )·Q (15)

[0110] where Q is the number of modulation constellation points.

[0111] In addition, considering that parameters such as the number of subcarriers and sub-symbols in the OTFS system will have a regular impact on the communication performance, the processed time-frequency resources are selected as the evaluation index MN I , and the specific calculation method is as follows:

[0112]

[0113] where M and N represent the number of subcarriers and sub-symbols of the system respectively.

[0114] In the channel part, select the Doppler frequency shift, delay parameter, number of multipaths, and power attenuation index of each path to represent the channel conditions.

[0115]

[0116] Among them, τ, fd, and P represent the delay of each path, Doppler frequency shift, and power attenuation of each path respectively, taps represents the number of multipaths, and l represents the l-th channel.

[0117] Regarding the problem that different anti-interference means are adopted for each receiving algorithm in the receiving module and it is impossible to extract the parameters applied to each receiving algorithm as evaluation indicators, this patent adopts the pre-storage method. Based on the performance of the bit error rate parameter, the communication performance of different receiving algorithms under different signal-to-noise ratios is calculated and scored and pre-stored correspondingly. The calculation formula is as follows:

[0118]

[0119] Among them, min(BER) represents the optimal bit error rate result of multiple receiving algorithms under high signal-to-noise ratio conditions, max(BER) represents the worst bit error rate result of multiple receiving algorithms under high signal-to-noise ratio conditions; BER f is the bit error rate performance of a specific receiving algorithm under specific signal-to-noise ratio conditions, and B calculated by formula (18) I The pre-storage result of the index is shown in Table 1:

[0120] Table 1 Pre-storage of receiving algorithm index parameters

[0121]

[0122] Combine the evaluation index D calculated above I , MN I , h I and B I by column to form an index matrix X;

[0123]

[0124] Among them, x ij is the element in the i-th row and j-th column of matrix X, representing the data of the j-th index under the i-th scheme.

[0125] Step 2: Obtain the subjective weight of the index based on the analytic hierarchy process. First, construct a hierarchical model. In this invention, the system communication performance is used as the decision-making layer, each specific module is used as the criterion layer, and the index parameters within the module are used as the scheme layer; secondly, establish a comparison matrix. According to the three-scale method of the following formula, multiple experts combine their prior knowledge to make pairwise comparisons of the selected evaluation indicators:

[0126]

[0127] Among them, the comparison matrix element a kj represents the importance degree of the k-th index relative to the j-th index. On this basis, calculate the importance ranking index r j :

[0128]

[0129] After constructing the judgment matrix, the optimal transfer matrix and the quasi-optimal consistent matrix are obtained based on this:

[0130]

[0131] Among them, j, k, and s are the jth, kth, and sth indicators respectively; r k is the same as r j in terms of calculation method. b is the comparison benchmark, which is obtained by comparing the most important indicator and the least important indicator in the same level, and its value range is 1-9. b kj , b ks , b js are the elements in the judgment matrix, c kj and d kj represent the elements in the optimal transfer matrix and the quasi-optimal consistent matrix respectively. Based on this, the index weight w j is calculated according to the following formula:

[0132]

[0133] Among them, w j represents the subjective weighting result of an expert for the jth indicator. The weighting results of each expert are combined by rows into a weight matrix W; finally, based on the cloud model theory, the multi-expert weight allocation is integrated to obtain the subjective weighting result w hj :

[0134]

[0135] Among them, w tj is the element in the tth row and jth column of matrix W, representing the weighting result of the tth expert for the jth evaluation indicator in the subjective weighting, and m is the number of experts.

[0136] Step 3: First, calculate the mean and standard deviation of the indicators according to the index parameters x ij of different waveform parameters, different channels, and different receiving algorithms in the evaluation index matrix X, and then obtain the coefficient of variation of the indicators;

[0137]

[0138] Among them, x ij is the data of the jth indicator under the ith scheme; υ j represents the coefficient of variation of the jth indicator, s j represents the standard deviation of the index parameters, represents the mean of the index parameters.

[0139] Finally, the Pearson correlation coefficient of formula (1) is used to quantify the conflict degree between indicators, and the objective weight result is obtained:

[0140]

[0141] Among them, u j represents the conflict degree between indicators, and w ej represents the objective weight result.

[0142] Step 4: Construct the combined weight based on game theory:

[0143]

[0144] Among them, β h and β e respectively represent the linear coefficients of the subjective weight and the objective weight

[0145] Step 5: Apply the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for evaluation. The closeness between different solutions and the ideal solution and the negative ideal solution can be calculated, and thus the ranking of the advantages and disadvantages of different solutions can be obtained according to the closeness. The specific steps are as follows:

[0146] 1) According to the evaluation index parameters and the combined weight result, the normalized data matrix Y is obtained;

[0147]

[0148] Among them, is the j-th element in the combined weight vector obtained in formula (33), representing the combined weight result of the j-th element. y i,j is the element in the i-th row and j-th column of matrix Y, representing the normalized data of the j-th indicator under the i-th solution.

[0149] 2) Determine the positive and negative ideal solutions. The calculation formulas for the positive and negative ideal solutions of benefit-type indicators and cost-type indicators are as follows:

[0150]

[0151] 3) Calculate the distance of each objective to the positive ideal solution and the negative ideal solution:

[0152]

[0153] Among them, L i + represents the distance of the solution to the positive ideal solution, and L i- represents the distance of the solution to the negative ideal solution.

[0154] 4) Calculate the relative closeness of each solution, that is, normalize the distance of each solution to the positive ideal solution and the distance to the negative ideal solution:

[0155]

[0156] The higher the degree of fit, the closer the solution is to the positive ideal solution, which means that the system performance is better. Based on this, the performance of multiple system solutions is evaluated and ranked.

[0157] Simulation results

[0158] The evaluation model of the present invention can quickly evaluate the advantages and disadvantages of several OTFS systems. Different solutions can be preset by changing the modules and parameter settings of the OTFS communication system, and the comprehensive analysis and evaluation method is used to evaluate the performance of these nine solutions; the specific module settings are shown in Table 2:

[0159] Table 2 Parameter settings of nine solutions

[0160]

[0161] The channel parameters use the EVA model, and the specific settings are shown in Table 3:

[0162] Table 3 Channel parameter settings

[0163]

[0164] Under such parameter settings, the bit error rate simulation results and the comprehensive analysis and evaluation results are obtained. Since the number of solutions is large, they are divided into two groups according to performance, namely the group with better performance parameters Figure 3 and the group with poorer performance parameters Figure 4 .

[0165] As Figure 3 and Figure 4 show, among the four solutions with relatively better system performance, the specific ranking under high signal-to-noise ratio conditions from best to worst is Solution 4, Solution 3, Solution 2, Solution 1. From the bit error rate simulation results, it can be seen that the bit error rate performance of Solution 4 is optimized faster as the signal-to-noise ratio increases, gradually exceeding other solutions to reach the best, and this change is also reflected in the image of the comprehensive analysis and evaluation results. This shows that the evaluation model can not only rank the performance of system solutions, but also reflect the performance change trend among different systems.

[0166] As Figure 4 shows, the bit error rate simulation and comprehensive analysis and evaluation results of the group with poorer performance parameters in the preset solutions are given; it can be found that from the two dimensions of bit error rate performance and evaluation results, the performance of the four solutions in this group is generally worse than that of the previous group. Specifically, the solutions from best to worst are Solution 8, Solution 7, Solution 6, Solution 5. This ranking of advantages and disadvantages is obtained by mutual verification of the bit error rate performance and evaluation results, and the degree of fit between the two is relatively high.

[0167] This evaluation model can not only rank the performance of different systems, but also explore the impact of changes in a single module on system performance, and then guide the selection of system parameters based on the evaluation results to achieve better transmission efficiency.

[0168] As Figure 5 shown, the comparison of the system transmission performance after controlling variables is given, and then the selection of system parameters is guided based on this. First, the comparison between Scheme 5 and Scheme 9 reflects the impact of changing waveform parameters on the system transmission efficiency. Secondly, the comparison of Schemes 1, 2, and 3 can reflect the advantages and disadvantages of the system transmission efficiency under different channel parameters. Finally, Schemes 1, 4, and 9 are the comparison images after changing the system receiving algorithm. According to the analysis of the result images, the high consistency between the bit error rate parameter and the evaluation model result verifies the rationality of the model evaluation. The comparison results of the three receiving algorithms also show that in the receiving algorithm module, the message passing algorithm has more advantages, while the zero-forcing algorithm cannot achieve better transmission effects.

[0169] This patent proposes the HGTIM evaluation model for communication waveforms in complex environments, and takes the OTFS system as an example to verify the rationality and accuracy of the model. The OTFS system under different waveform conditions, different channel conditions, and different receiving algorithms is evaluated by using a combined weighting method that combines subjective and objective factors and the evaluation method of the distance between the best and the worst solutions. The advantages and disadvantages of the transmission performance between systems with different index parameters are compared, and the optimal performance scheme is given at the same time. The simulation results verify that the evaluation results of the model are highly consistent with the analysis results of the bit error rate parameters. On the basis of demonstrating the rationality and accuracy of the model, it is possible to quickly and accurately compare the advantages and disadvantages of system performance without building a system simulation model, give the optimal scheme, and at the same time guide the selection of system parameters according to the evaluation results, so as to achieve better transmission effects.

[0170] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, which should all be regarded as belonging to the protection scope of the present invention.

Claims

1. A communication waveform performance evaluation method based on OTFS, characterized in that: The following steps are involved: Step 1: Obtain different waveform parameters, channel parameters and receiving algorithm parameters of the OTFS communication waveform, build a communication waveform performance evaluation index system, and introduce the transmission waveform index, channel index and receiving algorithm index into the communication waveform performance evaluation index system; Step 2: The weight values ​​of each evaluation index are mainly and objectively weighted by using the improved analytic hierarchy process and the improved coefficient of variation method. The weighted results of the two are combined through game theory to obtain the weighted communication waveform performance evaluation index system; Step 3: Use the distance between good and bad solutions to evaluate the communication waveform performance, input the communication waveform performance scheme to be evaluated, and the scheme includes waveform parameters, channel parameters, and receiving algorithm parameters; calculate the fit between different schemes and the ideal solution and the negative ideal solution through waveform parameters, channel parameters, receiving algorithm parameters and weighting results, and the scheme with the higher fit is the optimal scheme for communication waveform performance, and evaluate and rank different schemes.

2. The communication waveform performance evaluation method based on OTFS according to claim 1 is characterized in that: In step 1, the waveform index includes a modulation mode index D I and system time and frequency resource index MN I ; The modulation mode index D I To calculate the scores of signals with different modulation orders and different geometric characteristics, the system time-frequency resource index MN I To calculate and score the communication performance of different numbers of subcarriers and subsymbols; D I =(D b +D t )·Q Where Q is the number of modulation constellation points; D b is the box dimension; D t is the information dimension; Among them, N δ is the minimum number of grids covering the spectrum X(m); X is the signal sequence; δ is the minimum interval of the horizontal axis; m is the number of sampling points of the signal spectrum (m=1,2,...,N); Among them, P k is the frequency of the signal at each sampling point; Wherein, M is the number of subcarriers in the system; N is the number of subsymbols in the system.

3. The communication waveform performance evaluation method based on OTFS according to claim 1 is characterized in that: In step 1, the channel index h I For comprehensive calculation scores under different delay Doppler, different multipath numbers and different power attenuation conditions of each path: Among them, τ is the delay of each path; fd is the Doppler frequency shift; P is the power attenuation of each path; taps is the number of multipaths; l is the lth channel.

4. The communication waveform performance evaluation method based on OTFS according to claim 1 is characterized in that: In step 1, the receiving algorithm indicator B I To calculate and score the communication performance of different receiving algorithms under different signal-to-noise ratios; Among them, min(BER) is the optimal bit error rate result of multiple receiving algorithms under high signal-to-noise ratio conditions; max(BER) is the worst bit error rate result of multiple receiving algorithms under high signal-to-noise ratio conditions; BER f It is the bit error rate performance of a certain receiving algorithm under specific signal-to-noise ratio conditions.

5. The communication waveform performance evaluation method based on OTFS according to claim 1 is characterized in that: In step 2, the weights of the evaluation indicators are calculated according to the improved analytic hierarchy process. First, a judgment matrix is ​​established based on the importance of the relevant elements in the criterion layer and the scheme layer. The element b in the judgment matrix is ij : Where b is the comparison benchmark, b∈[1,9]; r k and r j They represent the importance ranking index of two different indicators, and the calculation method is to sum them by column; r max and r min are the maximum and minimum values ​​of the calculated importance ranking index respectively; Among them, the comparison matrix element a ij Indicates the importance of the i-th indicator relative to the j-th indicator; Then, based on the judgment matrix, the optimal transfer matrix and the quasi-optimal consistent matrix are obtained; Among them, the element c in the optimal transfer matrix kj and the element d in the quasi-optimal consistent matrix kj ; n is the number of evaluation indicators; Finally, the weight of each indicator is obtained: Among them, w j is the subjective weighting result of an expert on the j-th indicator.

6. The communication waveform performance evaluation method based on OTFS according to claim 5 is characterized in that: Based on the cloud model theory, the weight distribution of multiple experts is integrated to obtain the subjective weighting result of each indicator w hj : Among them, w tj is the weighting result of the t-th expert on the j-th evaluation indicator in the subjective weighting; m is the number of experts.

7. The communication waveform performance evaluation method based on OTFS according to claim 1 is characterized in that: In step 2, the improved coefficient of variation method firstly calculates the index x in the evaluation index system. ij Calculate the mean of each indicator parameter and standard deviation s j , get the coefficient of variation of each index parameter υ j : Among them, x ij is the data of the jth indicator under the i-th scheme; j is the coefficient of variation of the jth indicator; then, the degree of conflict between indicators is quantified according to the Pearson correlation coefficient u j After that, we get the objective weighting results of each indicator w ej : Among them, r kj is the correlation coefficient between the kth interference evaluation index and the jth interference evaluation index.

8. The communication waveform performance evaluation method based on OTFS according to claim 1 is characterized in that: In step 2, the combination weight is constructed based on game theory in, is the combined weighting result of the jth element in the combined weight vector; β h and β e are the linear coefficients of subjective weight and objective weight respectively.

9. The communication waveform performance evaluation method based on OTFS according to claim 1 is characterized in that: The step 3 specifically includes the following steps: Step 1: According to the waveform parameters, channel parameters, receiving algorithm parameters and combined weighting results, the normalized data matrix Y is obtained; Among them, y ij is the weighted value of the jth evaluation indicator under the i-th scheme; Step 2: Determine the positive and negative ideal solutions. The calculation of the positive and negative ideal solutions for benefit-based indicators and cost-based indicators is as follows: Among them, J + and J - Respectively represent whether the evaluation index is a benefit-based index or a cost-based index; Y i + and Y i - are the optimal performance parameters and the worst performance parameters of the i-th evaluation indicator respectively; Step 3: Calculate the distance from each target to the positive ideal solution and the negative ideal solution: Among them, L i + Represents the distance between the solution and the ideal solution, L i - Represents the distance between the solution and the negative ideal solution; y ij Represents the weighted value of the jth evaluation indicator under the i-th solution. Step 4: Calculate the relative fit of system solutions with different parameters, normalize the distance of each solution to the positive ideal solution and the distance to the negative ideal solution to obtain the fit, and sort the fit from high to low.

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