Symbol detection method of OTSM system in high-speed mobile environment

Abstract

The invention belongs to the technical field of communication, and particularly relates to a symbol detection method of an OTSM system in a high-speed mobile environment. The method comprises the following steps: after receiving time domain vectors, performing hierarchical optimization on each time domain block to obtain a symbol vector of each time domain block; the symbol vector of each time domain block is judged to serve as an initial value of an improved GS algorithm for iterative detection, and an estimated value of the symbol vector of each time domain block is obtained; carrying out matrix processing on the estimated value of the symbol vector of each time domain block to obtain a time delay-time domain information symbol; carrying out Walsh-Hadamard transform on the time delay-time domain information symbol to obtain an estimated value of the time delay-sequence domain information symbol; according to the method, the calculated amount is reduced, the processing efficiency of the system in high-dimensional data is improved, the problems of slow convergence and oscillation possibly caused by a fixed step length are avoided, and the system stability is improved.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a symbol detection method for an OTSM system in a high-speed mobile environment. Background Art

[0002] In recent years, the rapid development of high-speed mobile communication systems has made it an urgent problem to implement efficient and robust signal processing algorithms in dynamic and high-speed environments. The mobility of users introduces many challenges to communication systems, especially Doppler shift, frequency selective fading and inter-symbol interference (ISI). These factors significantly affect the accuracy of signal estimation and equalization, especially in high-speed scenarios such as vehicular communication and high-frequency communication bandwidth, the performance of the system may be greatly reduced.

[0003] Orthogonal time-frequency-space modulation (OTSM) is a promising modulation scheme that can effectively cope with the challenges in high-speed mobile environments. OTSM performs modulation and demodulation in the joint time-frequency domain, which makes it effective in resisting the effects of time-selective fading and frequency-selective fading. However, despite the advantages of OTSM in dealing with multipath effects and frequency-selective fading, the signal recovery problem faced by the system is still a huge challenge, mainly manifested in complex interference patterns, high-frequency selective fading, and the high dimensionality of the time-frequency grid.

[0004] The traditional Gauss-Seidel (GS) iterative algorithm is widely used in signal estimation and equalization problems, especially in channel estimation in communication systems. Although the GS algorithm can provide accurate estimation results, it usually suffers from slow convergence and poor stability when applied to high-speed mobile channels, especially in environments with high noise and severe interference. In addition, the step size in the traditional GS algorithm is fixed, which makes it difficult for the algorithm to adapt to the dynamic characteristics of the residual error changing over time, resulting in slow convergence and unstable system performance.

[0005] In summary, there is an urgent need for a new symbol detection method for the OTSM system in a high-speed mobile environment to improve the system's processing efficiency in high-dimensional data and avoid the slow convergence and oscillation problems that may be caused by a fixed step size, thereby improving the system stability. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a symbol detection method for an OTSM system in a high-speed mobile environment, the method comprising:

[0007] S1: After receiving the time domain vector, each time domain block is optimized hierarchically to obtain the symbol vector of each time domain block;

[0008] S2: The symbol vector of each time domain block is determined and used as the initial value of the improved GS algorithm for iterative detection to obtain an estimated value of the symbol vector of each time domain block;

[0009] S3: Matrix the estimated value of the symbol vector of each time domain block to obtain the delay-time domain information symbol;

[0010] S4: Performing Walsh-Hadamard transform on the delay-time domain information symbol to obtain an estimated value of the delay-sequence domain information symbol.

[0011] Preferably, the process of performing hierarchical optimization on each time domain block includes:

[0012] Perform M-point FFT operation on the received time domain block to obtain the time-frequency block corresponding to the time domain block;

[0013] Perform MMSE equalization on each time-frequency block to obtain time-frequency domain estimation;

[0014] The time-frequency domain estimation performs an M-point IFFT operation to obtain the symbol vector of the time domain block.

[0015] Furthermore, the formula for performing MMSE equalization processing on the time-frequency block is:

[0016]

[0017] in, represents the signal estimate at the mth iteration in the nth time domain block, represents the complex conjugate of the channel frequency domain response matrix of the mth iteration in the nth time domain block, represents the frequency domain received signal of the mth iteration in the nth time domain block, represents the channel frequency domain response matrix of the mth iteration in the nth time domain block, Represents the AWGN noise variance of the time domain block.

[0018] Preferably, the process of obtaining the estimated value of the symbol vector of each time domain block includes:

[0019] S21: After the symbol vector of each time domain block is determined, the time domain information symbol is obtained; for the time domain information symbol, a time domain input-output relationship is obtained through matched filtering operation;

[0020] S22: using the improved GS method to iteratively solve the least square solution corresponding to the time domain input-output relationship, and obtain an estimated value of the symbol vector of the time domain block of the current iteration;

[0021] S23: adjust the step size in the iterative solution process;

[0022] S24: performing hard decision on the iterative least square solution to obtain a delay-sequence domain information symbol in each iteration process;

[0023] S25: The delay-sequence domain information symbol is relaxed and scaled to obtain an estimated value of the symbol vector of a new time domain block and use it as the initial value in the next iteration process, and steps S22 to S25 are repeated until the iteration is completed.

[0024] Furthermore, the process of iteratively solving the least squares solution corresponding to the time domain input-output relationship using the GS method can be expressed as:

[0025]

[0026] in, represents the estimated value of the symbol vector of the nth time-domain block in the i-th iteration, represents the estimated value of the symbol vector of the nth time domain block in the i-1th iteration, α (i) represents the step size of the i-th iteration, T n represents the GS iteration matrix of the nth time domain block, b n Represents the correction term of the nth time domain block.

[0027] Furthermore, the formula for adjusting the step size in the iterative solution process is:

[0028]

[0029] Among them, α (i) represents the step size of the i-th iteration, r (i-1) represents the time domain block of the i-1th iteration, r (i) represents the time domain block of the i-th iteration, z n represents the signal received by the nth time domain block, R n represents the matched filter matrix, represents the estimate of the symbol vector of the nth time-domain block in the i-th iteration.

[0030] Furthermore, the process of relaxing and scaling the delay-sequence domain information symbol is expressed as:

[0031]

[0032] in, represents the estimated value of the symbol vector of the nth time domain block in the i+1th iteration, δ represents the relaxation parameter, represents the estimated value of the symbol vector of the nth time domain block in the i-th iteration, X (i) represents the delay-sequence domain information symbol of the i-th iteration, W N represents the N-point Walsh–Hadamard transform.

[0033] The beneficial effects of the present invention are as follows: the present invention significantly improves the convergence speed and stability of the system by introducing a block-level optimization strategy and a dynamic step-size adjustment mechanism based on the residual norm. Specifically, the block-level optimization divides the signal space into multiple small blocks and performs local optimization on each small block, which not only reduces the amount of calculation but also improves the processing efficiency of the system in high-dimensional data. In addition, the dynamic step-size adjustment mechanism can automatically adjust the step size according to the residual size of each iteration, avoiding the slow convergence and oscillation problems that may be caused by a fixed step size, and improving the stability of the system.

[0034] Through simulation experiments under various signal-to-noise ratios (SNR) and motion conditions, the results show that the proposed algorithm has significantly improved convergence speed and bit error rate (BER) performance compared with the traditional GS method. By dynamically adjusting the step size and introducing block-level optimization, the present invention can efficiently and stably perform signal estimation in a high-speed mobile environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a diagram of the OTSM system transceiver model in the present invention;

[0036] Figure 2 It is a flow chart of the symbol detection method of the OTSM system in a high-speed mobile environment of the present invention;

[0037] Figure 3 This is a comparison chart of the bit error performance between the algorithm of the present invention and the traditional GS algorithm in the OTSM system;

[0038] Figure 4 This is a comparison chart of the error performance of the OTSM system under different detection algorithms;

[0039] Figure 5 It is a bit error performance diagram of the algorithm of the present invention when the user moves at different speeds in the OTSM system;

[0040] Figure 6 This is a comparison chart of the bit error performance of the algorithm of the present invention and the traditional GS algorithm at different speeds and signal-to-noise ratios. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The present invention is applied to the OTSM system in a high-speed mobile environment. The present invention uses the following matrix / vector to represent the OTSM system. For the transmission of OTSM signal frames, the total frame duration is set to T f =NT, the bandwidth is B=MΔf, where Δf=1 / T, the signal is sampled strictly according to the pulse shaping waveform, and N is selected as a power of 2. Figure 1 The transmission model of the OTSM system is shown.

[0043] At the sending end, the information symbol is divided into m symbol vectors Each symbolic vector x m are placed in the mth row of the matrix, and all symbol vectors are neatly arranged into the matrix The column index and the row index are used to represent the delay index and the sequence index of the delay sequence grid, respectively.

[0044] X=[x0,x1,…,x M-1 ] T (1)

[0045] Then add X to the last l max The rows are set to zero vectors, where l max represents the discrete channel delay spread index. Figure 1 As shown, zero padding (ZP) along the delay domain helps to avoid inter-block interference due to channel delay spread. For each symbol vector x m Perform an N-point WHT transform to convert it to the delay time domain:

[0046]

[0047] matrix Contains the delayed time samples, which are vectorized to obtain the delayed time domain symbol vector It is finally sent to the channel, where each symbol vector represents a signal sample at a specific time delay.

[0048]

[0049] At the receiving end, after analog-to-digital conversion and sampling, the time domain signal r(t) is demodulated in the reverse order of the transmitting end to obtain the received time domain vector. is the time domain vector after RF front-end operation and ADC sampling. The received information symbol y is arranged in columns into a matrix Each column corresponds to a received symbol vector.

[0050]

[0051] right Perform N-point WHT changes to obtain information symbol Y in the DS domain, where:

[0052]

[0053] The baseband equivalent channel model of the present invention includes P propagation paths, each with a different gain v i , delay offset τ i and Doppler frequency shift v i . Assume that the maximum delay spread of the channel is τ max , the maximum Doppler spread is v max The corresponding Doppler spread length and channel delay spread length are α = [v max NT] and β = [τ max MΔf]. In an OTSM system with a carrier frequency of 4 GHz and a subcarrier spacing of 15 kHz, the extended vehicular channel model (EVA) is assumed. In this setting, the delay-Doppler spread length is α=10, β=3, which is significantly smaller than the number of sampling points NM of the system.

[0054] Since the number of channel coefficients P in the delay-Doppler domain is usually finite, the channel response can be presented as a sparse representation:

[0055]

[0056] The corresponding continuous time-varying channel impulse response function can be expressed as

[0057]

[0058] Starting from the received time domain signal r(t), the continuous time domain input-output relationship can be expressed as

[0059]

[0060] Therefore, the input-output relationship in the time domain can be expressed as

[0061] r=H·s+w (9)

[0062] in, The mean is 0 and the variance is Gaussian white noise, is the discrete channel matrix in the time domain.

[0063]

[0064] in,

[0065] like Figure 1As shown in Figure 2, due to the introduction of zero padding (ZP) in the time domain, inter-block interference in the time domain is effectively avoided. Therefore, in the time domain, the input-output relationship in equation (9) is separated and can be processed independently, which can be specifically expressed as

[0066] r n =H n ·s n +w n ,n=0,...,N-1(11)

[0067] in, And H n is the nth time-domain block channel matrix.

[0068] It is worth noting that due to the Walsh-Hadamard expansion, the components of all N time domain blocks have equal distribution with the information symbols in each delay sequence domain. Through this expansion method, the components in the time domain block can be evenly distributed in the frequency domain, so that the signal corresponding to each time domain block has the same power distribution, which helps to improve the overall performance of the system.

[0069] In a static (or very low Doppler spread) wireless channel environment, the time domain channel matrix of each block is assumed to be a circulant matrix, so that it can be diagonalized in the frequency domain. For a specific subcarrier and reference signal sequence, the entire frame can be transmitted through multiple channels, and each subblock consists of corresponding channels. However, in a mobile channel environment, due to the time-varying nature of the channel, the Doppler spread will introduce interference between the frequency domain samples of each block, causing the time domain channel matrix to no longer be a circulant matrix. In order to cope with the specific challenges in high-speed mobile environments, where the Doppler effect and time-frequency interference problems are particularly prominent, the present invention proposes a symbol detection method for an OTSM system in a high-speed mobile environment, such as Figure 2 As shown (BDSGS is the algorithm of the present invention), the method includes the following contents:

[0070] S1: After receiving the time domain vector, each time domain block is hierarchically optimized to obtain the symbol vector of each time domain block.

[0071] If a frame contains multiple subcarriers and the cross-correlation function value between two adjacent subcarriers exceeds a given threshold, the frame usually shows good performance. Considering that the duration of each time domain block is short relative to the entire frame, it can be assumed that the channel within each block is relatively unchanged, but the channel between blocks will be different. The advantage of this assumption is that a single-tap MMSE equalizer can be used for signal detection in each block, and the estimated values ​​of each block can be combined through the Walsh-Hadamard transform (WHT).

[0072] Perform M-point FFT operation on the received time domain block to obtain the time-frequency block corresponding to the time domain block:

[0073]

[0074] Then, each time-frequency block can be subjected to MMSE equalization to obtain a time-frequency domain estimate:

[0075]

[0076] Where m=0,...,M-1, n=0,...,N-1, For the AWGN noise variance of each time domain block, the frequency domain channel coefficient can be expressed by the following formula:

[0077]

[0078] Among them, F M Represents the transformation matrix for FFT operation on time domain signal, Represents the conjugate transpose of the transform matrix for performing FFT operations on time domain signals.

[0079] The time-frequency domain estimation performs an M-point IFFT operation to obtain the symbol vector of the time domain block.

[0080]

[0081] S2: The symbol vector of each time domain block is judged and used as the initial value of the improved GS algorithm for iterative detection to obtain the estimated value of the symbol vector of each time domain block.

[0082] Dividing the entire frame into short blocks can assume that the channel remains unchanged within the duration TTT of each block. However, this assumption may lead to performance degradation under high Doppler spread conditions, especially when using high-order QAM symbols. In order to solve this problem, the present invention first uses a hierarchical optimization strategy to accurately recover and demodulate the signal. Then, the present invention introduces an improved Gauss-Seidel (GS) algorithm and combines it with a dynamic step size adjustment mechanism. This mechanism can automatically adjust the step size according to the residual size in each iteration, thereby avoiding the slow convergence speed and oscillation problems that may be caused by the traditional fixed step size.

[0083] S21: After the symbol vector of each time domain block is determined, a time domain information symbol is obtained; for the time domain information symbol, a time domain input-output relationship is obtained through matched filtering operation.

[0084] In the detector proposed by the present invention, the improved GS iterative operation is performed on each block of the matched filter channel matrix. In formula (9), the matrix input-output relationship after the matched filter operation can be expressed as:

[0085]

[0086] in and

[0087] S22: Use the improved GS method to iteratively solve the least squares solution corresponding to the time domain input-output relationship to obtain an estimated value of the symbol vector of the time domain block of the current iteration.

[0088] Use the GS method to iteratively find the least squares solution of the M-dimensional linear equation system in (16)

[0089]

[0090] Let D n and L n is the matched filter matrix R n The matrix of diagonal elements and lower triangular elements of . In each iteration, the process of solving the estimated value by the GS iterative method can be expressed as:

[0091]

[0092] in, represents the estimated value of the symbol vector of the nth time-domain block in the i-th iteration, represents the estimated value of the symbol vector of the nth time domain block in the i-1th iteration, α (i) represents the step size of the i-th iteration, T n represents the GS iteration matrix of the nth time domain block, b n Represents the correction term used to update the signal estimate during the current iteration of the nth time domain block.

[0093] S23: Adjust the step size during the iterative solution process.

[0094] Adjust the step size during the iterative solution process based on the residual norm:

[0095]

[0096] Among them, r (i-1) represents the time domain block of the i-1th iteration, r (i) represents the time domain block of the i-th iteration, z n Represents the signal received by the nth time domain block.

[0097] S24: Perform hard decision on the iterative least square solution to obtain a delay-sequence domain information symbol in each iteration process.

[0098] The delayed sequence domain information in the i-th iteration is expressed as:

[0099]

[0100] in And D(.) denotes the decision function, which replaces all elements of the input with the nearest QAM symbol (measured in terms of Euclidean distance).

[0101] S25: The delay-sequence domain information symbol is relaxed and scaled to obtain an estimated value of the symbol vector of a new time domain block and use it as the initial value in the next iteration process, and steps S22 to S25 are repeated until the iteration is completed.

[0102]

[0103] Where δ is a relaxation parameter used to improve the detector convergence for higher modulation schemes such as 64-QAM. The introduction of the relaxation parameter helps to adjust the update step size of the estimate in each iteration, thereby improving the detection performance of high-order QAM symbols, especially in high-noise or multipath propagation environments. By properly selecting δ, convergence can be accelerated and errors can be reduced, making the detector more stable and efficient under high-order modulation.

[0104] S3: Matrix the estimated value of the symbol vector of each time domain block to obtain the delay-time domain information symbol.

[0105] The symbol vector estimation value of each time domain block is matrixed, and the symbol vector matrixing can obtain the delay-time domain information symbol:

[0106]

[0107] S4: Performing Walsh-Hadamard transform on the delay-time domain information symbol to obtain an estimated value of the delay-sequence domain information symbol.

[0108] Performing N-point WHT on the delay-time domain information symbol can obtain the estimated value of the transmitted delay-sequence domain information symbol:

[0109]

[0110] Evaluation of the present invention:

[0111] The Gauss-Seidel algorithm based on block-level optimization and dynamic step size adjustment designed by the present invention is simulated and compared with the traditional Gauss-Seidel iterative detection algorithm. Unless otherwise specified, all simulations use the parameters listed in Table 1. As shown in Table 1, OTSM frames with N=32 and M=32 are generated in the simulation, the subcarrier spacing Δf is set to 15kHz, and the carrier frequency is 4GHz. The maximum delay spread (in integer taps) is set to 4 (l max=4), which is about 4μs, so the maximum number of delay taps seen by the receiver is L = 4. The channel delay model is the standard EVA model, and each data point in the BER diagram is transmitted by 10 5 The Doppler frequency shift of the channel is calculated by Jakes formula v i =v max cos(θ i ) generated, v max is the maximum moving speed and θ i Uniformly distributed on [-π, π].

[0112] Table 1 Simulation parameters

[0113]

[0114] according to Figure 3 From the displayed results, we can see that under high signal-to-noise ratio conditions and different equalization algorithms, the proposed algorithm has a significant improvement in error performance compared to the traditional GS algorithm. This is mainly because block-level optimization improves computational efficiency and reduces dependence on global information by dividing the signal into smaller blocks and locally processing each block, while dynamic step size adjustment flexibly adjusts the step size according to the size of the residual of each iteration, avoiding slow convergence or oscillation problems caused by fixed step size. This enables the algorithm to converge more stably under high-speed movement and multipath effects, significantly improves the bit error rate (BER) performance, and reduces computational complexity, especially in environments with Doppler spread and delay changes, with higher convergence speed and better performance. Comprehensive Figure 3 The results show that the proposed algorithm can achieve better performance under high signal-to-noise ratio conditions, which shows that the algorithm has a good performance advantage in the OTSM system.

[0115] according to Figure 4 From the comparison results, we can see that under high signal-to-noise ratio conditions and different modulation modes, the proposed algorithm is still applicable to different modulation modes, and there is still a significant improvement in bit error performance. Figure 4 The results show that the proposed algorithm can achieve better performance under different modulation modes and high signal-to-noise ratio conditions, which shows that the algorithm has good applicability in OTSM system.

[0116] Figure 5The bit error rate (BER) performance of the proposed algorithm under different speeds and signal-to-noise ratios in the OTSM system is shown. As can be seen from the figure, the proposed algorithm shows good error performance under high-speed mobile conditions, and within a certain range, the error performance improves with the increase of speed. This shows that the proposed algorithm shows strong adaptability and robustness when dealing with channel characteristics in high-speed mobile environments. Surprisingly, as the Doppler shift increases, the bit error rate (BER) decreases. This phenomenon is a new discovery for traditional technologies that rely on static channel modulation. In fact, when modulating in the delay-sequence domain, a larger Doppler shift is beneficial to improving performance. This is because the Doppler shift caused by high-speed movement enhances the spectral spread of the signal, allowing the system to more effectively distinguish the signal paths corresponding to different Doppler shifts, thereby reducing the impact of inter-Doppler interference and improving the error performance. At the same time, the increase in Doppler shift leads to the expansion of the signal spectrum in the frequency domain, thereby improving the utilization efficiency of spectrum resources. This enables the system to better utilize orthogonal resources and improve the capacity and overall performance of the system.

[0117] In order to more intuitively evaluate the performance under different user mobility rates and signal-to-noise ratio conditions, we conducted a three-dimensional analysis of the GS algorithm and the algorithm proposed in this paper, such as Figure 6 As shown in the figure, as the speed and signal-to-noise ratio increase, the proposed algorithm shows obvious performance advantages over the traditional GS algorithm. This shows that the proposed algorithm can better meet the needs of future wireless mobile communication systems.

[0118] In summary, the present invention proposes a symbol detection method for an OTSM system in a high-speed mobile environment, which adopts an OTSM iterative algorithm based on block-level optimization and dynamic step size adjustment, taking into account the high Doppler frequency shift brought by the high-speed mobile environment and the inter-symbol interference caused by multipath transmission. The algorithm first uses block-level optimization detection in the time-frequency domain to accurately recover and demodulate the signal, suppress the interference between carriers and reduce the impact of Doppler frequency shift on the signal, and then uses the GS algorithm based on the dynamic step size adjustment mechanism of the residual norm, which can automatically adjust the step size according to the residual size of each iteration, avoiding the slow convergence and oscillation problems that may be caused by the fixed step size. The simulation experimental results show that the method proposed in the present invention significantly improves the error performance under different speeds and modulation modes, especially in a high-speed mobile environment. This innovative research is of great significance to improving the overall performance of the OTSM system, and provides a more reliable and efficient signal processing solution for wireless communication systems in practical applications. Especially in high-speed mobile scenarios, the excellent performance of the proposed algorithm provides strong support for applications in the fields of mobile communications, Internet of Vehicles, etc., and lays a solid foundation for achieving high-quality communication services.

[0119] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation modes of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A symbol detection method for an OTSM system in a high-speed mobile environment, characterized in that: include: S1: After receiving the time domain vector, each time domain block is optimized hierarchically to obtain the symbol vector of each time domain block; S2: The symbol vector of each time domain block is determined and used as the initial value of the improved GS algorithm for iterative detection to obtain an estimated value of the symbol vector of each time domain block; S3: Matrix the estimated value of the symbol vector of each time domain block to obtain the delay-time domain information symbol; S4: Performing Walsh-Hadamard transform on the delay-time domain information symbol to obtain an estimated value of the delay-sequence domain information symbol.

2. The symbol detection method of the OTSM system in a high-speed mobile environment according to claim 1 is characterized in that: The process of hierarchical optimization for each time domain block includes: Perform M-point FFT operation on the received time domain block to obtain the time-frequency block corresponding to the time domain block; Perform MMSE equalization on each time-frequency block to obtain time-frequency domain estimation; The time-frequency domain estimation performs an M-point IFFT operation to obtain the symbol vector of the time domain block.

3. The symbol detection method of the OTSM system in a high-speed mobile environment according to claim 2 is characterized in that: The formula for MMSE equalization of time-frequency blocks is: in, represents the signal estimate at the mth iteration in the nth time domain block, represents the complex conjugate of the channel frequency domain response matrix of the mth iteration in the nth time domain block, represents the frequency domain received signal of the mth iteration in the nth time domain block, represents the channel frequency domain response matrix of the mth iteration in the nth time domain block, Represents the AWGN noise variance of the time domain block.

4. The symbol detection method of the OTSM system in a high-speed mobile environment according to claim 1, characterized in that: The process of obtaining an estimate of the symbol vector for each time domain block includes: S21: After the symbol vector of each time domain block is determined, the time domain information symbol is obtained; for the time domain information symbol, a time domain input-output relationship is obtained through matched filtering operation; S22: using the improved GS method to iteratively solve the least square solution corresponding to the time domain input-output relationship, and obtain an estimated value of the symbol vector of the time domain block of the current iteration; S23: adjust the step size in the iterative solution process; S24: performing hard decision on the iterative least square solution to obtain a delay-sequence domain information symbol in each iteration process; S25: The delay-sequence domain information symbol is relaxed and scaled to obtain an estimated value of the symbol vector of a new time domain block and use it as the initial value in the next iteration process, and steps S22 to S25 are repeated until the iteration is completed.

5. The symbol detection method of the OTSM system in a high-speed mobile environment according to claim 4 is characterized in that: The process of iteratively solving the least squares solution corresponding to the time domain input-output relationship using the GS method can be expressed as: in, represents the estimated value of the symbol vector of the nth time-domain block in the i-th iteration, represents the estimated value of the symbol vector of the nth time domain block in the i-1th iteration, α (i) represents the step size of the i-th iteration, T n represents the GS iteration matrix of the nth time domain block, b n Represents the correction term of the nth time domain block.

6. The symbol detection method of the OTSM system in a high-speed mobile environment according to claim 4 is characterized in that: The formula for adjusting the step size during the iterative solution process is: Among them, α (i) represents the step size of the i-th iteration, r (i-1) represents the time domain block of the i-1th iteration, r (i) represents the time domain block of the i-th iteration, z n represents the signal received by the nth time domain block, R n represents the matched filter matrix, represents the estimate of the symbol vector of the nth time-domain block in the i-th iteration.

7. The symbol detection method of the OTSM system in a high-speed mobile environment according to claim 4, characterized in that: The process of relaxing and scaling the delay-sequence domain information symbols is expressed as: in, represents the estimated value of the symbol vector of the nth time domain block in the i+1th iteration, δ represents the relaxation parameter, represents the estimated value of the symbol vector of the nth time domain block in the i-th iteration, X (i) represents the delay-sequence domain information symbol of the i-th iteration, W N represents the N-point Walsh–Hadamard transform.

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