A symbol detection method of OTSM system in high-speed mobile environment

Abstract

The application 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 a time domain vector, each time domain block is hierarchically optimized to obtain a symbol vector of each time domain block; each symbol vector of each time domain block is judged and then taken as an initial value of an improved GS algorithm to perform iterative detection, so as to obtain an estimated value of each symbol vector of each time domain block; the estimated value of each symbol vector of each time domain block is matrixed to obtain a time delay-time domain information symbol; and the time delay-time domain information symbol is subjected to Walsh-Hadamard transformation to obtain an estimated value of a time delay-sequence domain information symbol; the application reduces the amount of calculation and improves the processing efficiency of the system in high-dimensional data, avoids the problems of slow convergence and oscillation possibly caused by a fixed step length, and improves the system stability.

Description

Technical Field

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

[0002] In recent years, the rapid development of high-speed mobile communication systems has made the implementation of efficient and robust signal processing algorithms in dynamic and high-speed environments a pressing issue. User mobility 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, particularly in high-speed scenarios such as vehicular communication and high-frequency communication bandwidth, where system performance can degrade dramatically.

[0003] Orthogonal Time-Frequency Spatial Modulation (OTSM) is a promising modulation scheme that can effectively address the challenges of high-speed mobile environments. OTSM performs modulation and demodulation in the joint time-frequency domain, which makes it effective against time-selective and frequency-selective fading. However, despite its advantages in handling multipath effects and frequency-selective fading, OTSM still faces significant challenges in signal recovery, mainly due to complex interference modes, 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 for communication systems. While the GS algorithm can provide accurate estimation results, it often suffers from slow convergence and poor stability when applied to high-speed mobile channels, particularly in environments with high noise and severe interference. Furthermore, the fixed step size in the traditional GS algorithm makes it difficult to adapt to the dynamic characteristics of residual error 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 OTSM systems in high-speed mobile environments to improve the system's processing efficiency in high-dimensional data and avoid the slow convergence and oscillation problems that may be caused by fixed step size, thereby improving system stability. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a symbol detection method for OTSM systems in high-speed mobile environments, the method comprising:

[0007] S1: After receiving the time-domain vector, perform hierarchical optimization on each time-domain block to obtain the symbol vector of each time-domain block;

[0008] S2: After the symbol vector of each time-domain block is determined, iterative detection is performed using the initial value of the improved GS algorithm to obtain the estimated value of the symbol vector of each time-domain block;

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

[0010] S4: Perform Walsh-Hadamard transform on the time-delay-time domain information symbols to obtain the estimated values ​​of the time-delay-sequence domain information symbols.

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

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

[0013] MMSE equalization is performed on each time-frequency block to obtain a time-frequency domain estimate;

[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 MMSE equalization processing of the time-frequency block is as follows:

[0016]

[0017] in, This represents the signal estimation in the m-th iteration of the n-th time-domain block. Let represent the complex conjugate of the channel frequency domain response matrix in the m-th iteration of the n-th time domain block. This represents the frequency domain received signal in the m-th iteration of the n-th time-domain block. This represents the channel frequency domain response matrix in the m-th iteration of the n-th time domain block. This represents the AWGN noise variance of the time-domain block.

[0018] Preferably, the process of obtaining an estimate of the symbol vector for each time-domain block includes:

[0019] S21: The symbol vector of each time-domain block is determined to obtain the time-domain information symbol; the time-domain information symbol is then subjected to matched filtering to obtain the time-domain input-output relationship;

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

[0021] S23: Adjust the step size during the iterative solution process;

[0022] S24: Perform hard decision on the least squares solution obtained through iterative solution to obtain the time delay-sequence domain information symbol in each iteration process;

[0023] S25: Relax and scale the time-delay-sequence domain information symbols to obtain an estimate of the symbol vector of the new time-domain block and use it as the initial value in the next iteration. Repeat steps S22 to S25 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, This represents the estimated value of the sign vector of the nth time-domain block in the i-th iteration. α represents the estimated value of the sign vector of the nth time-domain block in the (i-1)th iteration. (i) T represents the step size of the i-th iteration. n Let b represent the GS iteration matrix of the nth time-domain block. n This represents the correction term for the nth time-domain block.

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

[0028]

[0029] Where, α (i) Let r represent the step size of the i-th iteration. (i-1) Let r represent the time-domain block of the (i-1)th iteration. (i) Let z represent the time-domain block of the i-th iteration. n R represents the signal received in the nth time-domain block. n Represents the matched filter matrix. This represents the estimated value of the symbol vector of the nth time-domain block in the i-th iteration.

[0030] Furthermore, the process of relaxing and scaling the time delay-sequence domain information symbols can be represented as follows:

[0031]

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

[0033] The beneficial effects of this invention are as follows: By introducing a block-level optimization strategy and a dynamic step-size adjustment mechanism based on the residual norm, this invention significantly improves the convergence speed and stability of the system. Specifically, block-level optimization divides the signal space into multiple small blocks and performs local optimization on each block, thus reducing computational load and improving the system's processing efficiency in high-dimensional data. Furthermore, the dynamic step-size adjustment mechanism automatically adjusts the step size according to the residual size of each iteration, avoiding the slow convergence and oscillation problems that may arise from a fixed step size, thereby improving the system's stability.

[0034] Simulation experiments under various signal-to-noise ratio (SNR) and motion conditions demonstrate that the proposed algorithm significantly improves convergence speed and bit error rate (BER) performance compared to the traditional GS method. By dynamically adjusting the step size and introducing block-level optimization, this invention enables efficient and stable signal estimation in high-speed moving environments. Attached Figure Description

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

[0036] Figure 2 This is a flowchart of the symbol detection method for an OTSM system in a high-speed mobile environment according to the present invention;

[0037] Figure 3 A comparison chart of the error rate performance of the algorithm of this invention and the traditional GS algorithm under the OTSM system;

[0038] Figure 4 A comparison chart of the bit error rate performance of the OTSM system under different detection algorithms;

[0039] Figure 5 This is a graph showing the bit error rate performance of the algorithm of this invention when the user moves at different speeds in an OTSM system.

[0040] Figure 6 This is a comparison chart of the bit error rate performance of the algorithm of this invention and the traditional GS algorithm under different speeds and signal-to-noise ratios. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] This invention applies to OTSM systems in high-speed mobile environments. The invention uses the following matrix / vector representation to represent the OTSM system. Let... These are the information symbols for transmission and reception, respectively. For OTSM signal frame transmission, the total frame duration is set to T. f =NT, bandwidth is B=MΔf, where Δf=1 / T, the signal is sampled strictly according to the pulse-shaped waveform, and N is selected as a power of 2. Figure 1 The transmission model of the OTSM system is demonstrated.

[0043] At the sending end, information symbols Divided into m symbol vectors Each symbol vector x m All symbolic vectors are neatly arranged in the m-th row of the matrix. The column index and row index are used to represent the delay index and sequence index of the delayed sequence grid, respectively.

[0044] X = [x0, x1, ..., x M-1 ] T (1)

[0045] Then X last l max The row is set as the zero vector, where l max This represents the discrete channel delay spread index. For example... Figure 1 As shown, zero-padding (ZP) along the delay domain helps avoid inter-block interference caused by 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 It contains time-delay samples, which are vectorized to obtain time-delay domain symbol vectors. Finally, it is 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 into the received time-domain vector following the reverse steps compared to the transmitting end. Let... This is the time-domain vector after RF front-end processing and ADC sampling. The received information symbols y are arranged column-wise into a matrix. Each column corresponds to a received symbol vector.

[0050]

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

[0052]

[0053] The baseband equivalent channel model of interest in this invention includes P propagation paths, each with a different gain v. i Delay offset τ i and Doppler frequency shift v i Let the maximum delay spread of the channel be τ. max The maximum Doppler extension 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, it is assumed that an extended vehicle channel model (EVA) is used. Under this setting, the delay-Doppler spread length α = 10 and β = 3 are 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 represented as a sparse representation:

[0055]

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

[0057]

[0058] Starting with 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, With a mean of 0 and a variance of Gaussian white noise, It is a time-domain discrete channel matrix.

[0063]

[0064] in,

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

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

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

[0068] It is worth noting that, due to the use of the Walsh-Hadamard extension, the components of all N time-domain blocks have an equal distribution with the information symbols in each delay sequence domain. This extension method enables a balanced distribution of components within each time-domain block in the frequency domain, resulting in a uniform power distribution for the signal corresponding to each time-domain block, which helps improve the overall system performance.

[0069] In static (or very low Doppler spread) wireless channel environments, it is assumed that the time-domain channel matrix of each block is a cyclic matrix, which 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 sub-block consists of a corresponding channel. However, in mobile channel environments, due to the time-varying nature of the channel, Doppler spread introduces interference between the frequency domain samples of each block, causing the time-domain channel matrix to no longer be a cyclic matrix. To address the specific challenges in high-speed mobile environments, where the Doppler effect and time-frequency interference are particularly prominent, this invention proposes a symbol detection method for OTSM systems in high-speed mobile environments, such as... Figure 2 As shown (BDSGS is the algorithm of this invention), the method includes the following:

[0070] S1: After receiving the time-domain vector, perform hierarchical optimization on each time-domain block to obtain the symbol vector of each time-domain block.

[0071] A frame typically exhibits good performance if it contains multiple subcarriers and the cross-correlation function between two adjacent subcarriers exceeds a given threshold. Given that the duration of each time-domain block is relatively short compared to the entire frame, it can be assumed that the channel within each block is relatively constant, but the channels between blocks will differ. The advantage of this assumption is that signal detection can be performed using a single-tap MMSE equalizer within each block, and the estimates from each block can be combined using the Walsh-Hadamard transform (WHT).

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

[0073]

[0074] Then, MMSE equalization can be performed on each time-frequency block to obtain the time-frequency domain estimate:

[0075]

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

[0077]

[0078] Among them, F M This represents the transformation matrix used to perform an FFT operation on a time-domain signal. This represents the conjugate transpose of the transformation matrix used to perform FFT operations on a time-domain signal.

[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: After the symbol vector of each time-domain block is determined, iterative detection is performed using the initial value of the improved GS algorithm to obtain the estimated value of the symbol vector of each time-domain block.

[0082] Dividing the entire frame into short blocks assumes that the channel remains constant throughout the duration (TTT) of each block. However, under high Doppler spread conditions, this assumption can lead to performance degradation, especially when using high-order QAM symbols. To address this issue, this invention first employs a hierarchical optimization strategy for accurate signal recovery and demodulation. Next, this invention introduces an improved Gaussian Seidel (GS) algorithm combined with a dynamic step-size adjustment mechanism. This mechanism automatically adjusts the step size based on the residual magnitude in each iteration, thus avoiding the slow convergence and oscillation problems that can occur with traditional fixed step sizes.

[0083] S21: The symbol vector of each time-domain block is determined to obtain the time-domain information symbol; the time-domain information symbol is then processed by matched filtering to obtain the time-domain input-output relationship.

[0084] In the detector proposed in this invention, the improved GS iteration operation is performed on each block of the matched filter channel matrix. In equation (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, and obtain the estimated value of the symbol vector of the time-domain block in the current iteration.

[0088] The least-squares solution of the M-dimensional linear equation system in (16) is obtained iteratively using the GS method.

[0089]

[0090] Let D n and L n For a matched filter matrix R n The matrix consists of the diagonal and lower triangular elements. In each iteration, the GS iterative method solves for the estimated value as follows:

[0091]

[0092] in, This represents the estimated value of the sign vector of the nth time-domain block in the i-th iteration. α represents the estimated value of the sign vector of the nth time-domain block in the (i-1)th iteration. (i) T represents the step size of the i-th iteration. n Let b represent the GS iteration matrix of the nth time-domain block. n This 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 in the iterative solution process.

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

[0095]

[0096] Where, r (i-1) Let r represent the time-domain block of the (i-1)th iteration. (i) Let z represent the time-domain block of the i-th iteration. n This represents the signal received in the nth time-domain block.

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

[0098] The delayed sequence field information symbol in the i-th iteration is represented as:

[0099]

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

[0101] S25: Relax and scale the time-delay-sequence domain information symbols to obtain an estimate of the symbol vector of the new time-domain block and use it as the initial value in the next iteration. Repeat steps S22 to S25 until the iteration is completed.

[0102]

[0103] Here, δ is a relaxation parameter used to improve the convergence of detectors for higher modulation schemes such as 64-QAM. The introduction of the relaxation parameter helps adjust the update step size of the estimate in each iteration, thereby improving the detection performance of higher-order QAM symbols, especially in high-noise or multipath propagation environments. By appropriately selecting δ, convergence can be accelerated and errors reduced, making the detector more stable and efficient under higher-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 estimates for each time-domain block are matrixed, and the matrixing of the symbol vectors yields the delay-time domain information symbols:

[0106]

[0107] S4: Perform Walsh-Hadamard transform on the time-delay-time domain information symbols to obtain the estimated values ​​of the time-delay-sequence domain information symbols.

[0108] An estimate of the transmitted delay-sequence domain information symbols can be obtained by performing an N-point WHT on the delay-time domain information symbols:

[0109]

[0110] Evaluation of the present invention:

[0111] The Gauss-Seidel algorithm based on block-level optimization and dynamic step size adjustment designed in this invention is compared with the traditional Gauss-Seidel iterative detection algorithm through simulation. Unless otherwise specified, all simulations use the parameters listed in Table 1. According to 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 (1 max=4), approximately 4μs, therefore the maximum delay tap number seen by the receiver is L=4. The channel delay model is the standard EVA model, and each data point in the BER graph is transmitted through 10... 5 Statistical analysis of the frame signals is performed. The Doppler frequency shift of the channel is given by Jakes' formula v. i =v max cos(θ i Generated by ) v max For the maximum moving speed and θ i It is evenly distributed on [-π, π].

[0112] Table 1 Simulation Parameters

[0113]

[0114] according to Figure 3 The results show that, under high signal-to-noise ratio (SNR) conditions and with different equalization algorithms, the proposed algorithm significantly improves bit error rate (BER) performance compared to the traditional GS algorithm. This is mainly because block-level optimization improves computational efficiency and reduces reliance on global information by dividing the signal into smaller blocks and processing each block locally. Dynamic step-size adjustment flexibly adjusts the step size based on the residual size of each iteration, avoiding the slow convergence or oscillation problems caused by a fixed step size. This allows the algorithm to converge more stably under high-speed movement and multipath effects, significantly improving BER performance and reducing computational complexity. It exhibits higher convergence speed and superior performance, especially in environments with Doppler spread and varying time delays. Figure 3 The results show that the proposed algorithm achieves better performance under high signal-to-noise ratio conditions, which indicates that the algorithm has a good performance advantage in OTSM systems.

[0115] according to Figure 4 The comparison results show that, under high signal-to-noise ratio conditions and different modulation schemes, the proposed algorithm remains applicable to various modulation schemes and still offers significant improvements in bit error rate performance. In summary... Figure 4 The results show that the proposed algorithm can achieve better performance under different modulation methods and high signal-to-noise ratio conditions, which indicates that the algorithm has good applicability in OTSM systems.

[0116] Figure 5The figure illustrates the bit error rate (BER) performance of the proposed algorithm in an OTSM system under different speed and signal-to-noise ratio conditions. As can be seen from the figure, the proposed algorithm exhibits good BER performance under high-speed movement, and within a certain range, the BER performance improves with increasing speed. This indicates that the proposed algorithm demonstrates strong adaptability and robustness in dealing with the channel characteristics of high-speed moving environments. Surprisingly, the BER decreases with increasing Doppler shift, a novel finding for traditional techniques relying on static channel modulation. In fact, a larger Doppler shift is beneficial for performance improvement when modulating in the time-delay-sequence domain. 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 signal paths corresponding to different Doppler shifts, thereby mitigating the impact of inter-Doppler interference and improving BER performance. Simultaneously, the increased Doppler shift leads to an expansion of the signal spectrum in the frequency domain, thus improving the utilization efficiency of spectrum resources. This allows the system to better utilize orthogonal resources, improving system capacity and overall performance.

[0117] To more intuitively evaluate the performance under different user mobility rates and signal-to-noise ratios, 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, the proposed algorithm exhibits a significant performance advantage over the traditional GS algorithm as speed and signal-to-noise ratio increase. This indicates that the proposed algorithm can better meet the needs of future wireless mobile communication systems.

[0118] In summary, this invention proposes a symbol detection method for OTSM systems in high-speed mobile environments. It employs an OTSM iterative algorithm based on block-level optimization and dynamic step-size adjustment, considering the high Doppler shift and inter-symbol interference caused by multipath transmission in high-speed mobile environments. The algorithm first uses block-level optimization detection in the time-frequency domain to accurately recover and demodulate the signal, suppressing inter-carrier interference and reducing the impact of Doppler shift. Then, it utilizes a GS algorithm based on a residual norm dynamic step-size adjustment mechanism, which automatically adjusts the step size according to the residual size in each iteration, avoiding the slow convergence and oscillation problems that may arise from fixed step sizes. Simulation results show that the proposed method significantly improves bit error rate performance under different speeds and modulation schemes, especially in high-speed mobile environments. This innovative research is of great significance for improving the overall performance of OTSM systems, providing 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 mobile communications, vehicle-to-everything (V2X) communication, and other fields, laying a solid foundation for achieving high-quality communication services.

[0119] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments 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 within 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, perform hierarchical optimization on each time-domain block to obtain the symbol vector of each time-domain block; S2: After the symbol vector of each time-domain block is determined, iterative detection is performed using the initial value of the improved GS algorithm to obtain the estimated value of the symbol vector of each time-domain block; The process of obtaining an estimate of the symbol vector for each time-domain block includes: S21: The symbol vector of each time-domain block is determined to obtain the time-domain information symbol; the time-domain information symbol is then subjected to matched filtering to obtain the time-domain input-output relationship; S22: Use the improved GS method to iteratively solve for the least squares solution corresponding to the time-domain input-output relationship, and obtain the estimated value of the sign vector of the time-domain block in the current iteration; the process of using the GS method to iteratively solve for the least squares solution corresponding to the time-domain input-output relationship can be expressed as: in, This represents the estimated value of the sign vector of the nth time-domain block in the i-th iteration. α represents the estimated value of the sign vector of the nth time-domain block in the (i-1)th iteration. (i) T represents the step size of the i-th iteration. n Let b represent the GS iteration matrix of the nth time-domain block. n This represents the correction term for the nth time-domain block; S23: Adjust the step size in the iterative solution process; the formula for adjusting the step size in the iterative solution process is: Where, r (i-1) Let r represent the time-domain block of the (i-1)th iteration. (i) Let z represent the time-domain block of the i-th iteration. n R represents the signal received in the nth time-domain block. n Represents the matched filter matrix; S24: Perform hard decision on the least squares solution obtained through iterative solution to obtain the time delay-sequence domain information symbol in each iteration process; S25: Relax and scale the time-delay-sequence domain information symbols to obtain the estimated value of the symbol vector of the new time-domain block and use it as the initial value in the next iteration process. Repeat steps S22 to S25 until the iteration is completed. S3: Matrix the estimated symbol vector of each time domain block to obtain the delay-time domain information symbol; S4: Perform Walsh-Hadamard transform on the time-delay-time domain information symbols to obtain the estimated values ​​of the time-delay-sequence domain information symbols.

2. The symbol detection method for an OTSM system in a high-speed mobile environment according to claim 1, characterized in that, The process of hierarchical optimization for each time-domain block includes: Perform an M-point FFT operation on the received time-domain block to obtain the time-frequency block corresponding to the time-domain block; MMSE equalization is performed on each time-frequency block to obtain a time-frequency domain estimate; 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 for an OTSM system in a high-speed mobile environment according to claim 2, characterized in that, The formula for MMSE equalization of time-frequency blocks is: in, This represents the signal estimation in the m-th iteration of the n-th time-domain block. Let represent the complex conjugate of the channel frequency domain response matrix in the m-th iteration of the n-th time domain block. This represents the frequency domain received signal in the m-th iteration of the n-th time-domain block. This represents the channel frequency domain response matrix in the m-th iteration of the n-th time domain block. This represents the AWGN noise variance of the time-domain block.

4. The symbol detection method for an OTSM system in a high-speed mobile environment according to claim 1, characterized in that, The process of relaxing and scaling the time-delay-sequence domain information symbols can be represented as follows: in, Let δ represent the estimated value of the sign vector of the nth time-domain block in the (i+1)th iteration, and let δ represent the relaxation parameter. X represents the estimated value of the sign vector of the nth time-domain block in the i-th iteration. (i) W represents the delay-sequence domain information symbol for the i-th iteration. N This represents the N-point Walsh-Hadamard transform.

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