CN120017455B - A symbol detection method for OTSM systems in high-speed mobile environments - Google Patents

A symbol detection method for OTSM systems in high-speed mobile environments

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CN120017455B
CN120017455B CN202510150936.0A CN202510150936A CN120017455B CN 120017455 B CN120017455 B CN 120017455B CN 202510150936 A CN202510150936 A CN 202510150936A CN 120017455 B CN120017455 B CN 120017455B
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symbol
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CN120017455A (en
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李国军
舒俊霖
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03821—Inter-carrier interference cancellation [ICI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03878—Line equalisers; line build-out devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/0001—Arrangements for dividing the transmission path
    • H04L5/0014—Three-dimensional division
    • H04L5/0023—Time-frequency-space
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/03592—Adaptation methods
    • H04L2025/03598—Algorithms
    • H04L2025/03611—Iterative algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/03592—Adaptation methods
    • H04L2025/03598—Algorithms
    • H04L2025/03611—Iterative algorithms
    • H04L2025/03636—Algorithms using least mean square [LMS]

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Power Engineering (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)

Abstract

本发明属于通信技术领域，具体涉及一种高速移动环境下OTSM系统的符号检测方法；该方法包括：接收时域向量后，对每个时域块进行分级优化，得到每个时域块的符号向量；将每个时域块的符号向量判决后作为改进GS算法的初始值进行迭代检测，得到每个时域块的符号向量的估计值；将每个时域块的符号向量的估计值进行矩阵化，得到时延‑时间域信息符号；对时延‑时间域信息符号进行沃尔什‑哈达玛变换，得到时延‑序列域信息符号的估计值；本发明减少了计算量又提高了系统在高维数据中的处理效率，避免了固定步长可能带来的收敛缓慢和振荡问题，提高了系统稳定性。

This invention belongs to the field of communication technology, specifically relating to a symbol detection method for an OTSM system in a high-speed mobile environment. The method includes: receiving a time-domain vector, performing hierarchical optimization on each time-domain block to obtain a symbol vector for each time-domain block; using the decision of the symbol vector for each time-domain block as the initial value for an improved GS algorithm for iterative detection to obtain an estimated value of the symbol vector for each time-domain block; matrixing the estimated value of the symbol vector for each time-domain block to obtain a delay-time domain information symbol; and performing a Walsh-Hadamard transform on the delay-time domain information symbol to obtain an estimated value of the delay-sequence domain information symbol. This invention reduces computational load and improves the system's processing efficiency in high-dimensional data, avoids the slow convergence and oscillation problems that may arise from a fixed step size, and improves system stability.

Description

Symbol detection method of OTSM system in high-speed mobile environment
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a symbol detection method of OTSM systems in a high-speed mobile environment.
Background
In recent years, the rapid development of high-speed mobile communication systems makes the realization of efficient and robust signal processing algorithms in dynamic and high-speed environments an urgent issue to be addressed. The mobility of users introduces challenges for many communication systems, in particular 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 vehicle communications and high frequency communications bandwidths, the performance of the system may be significantly degraded.
Orthogonal time-frequency spatial modulation (OTSM) is a promising modulation scheme that can effectively address challenges in high-speed mobile environments. OTSM perform modulation and demodulation in the time-frequency joint domain, which makes it effective against the effects of time-selective fading and frequency-selective fading. However, despite the advantages of OTSM in terms of multipath and frequency selective fading, the signal recovery problem faced by the system still presents a great challenge, mainly in terms of complex interference patterns, high frequency selective fading and high dimensionality of the time-frequency grid.
Conventional gaussian-Seidel (GS) iterative algorithms are 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 generally suffers from slow convergence and poor stability when applied to high-speed mobile channels, especially in noisy and noisy environments. Furthermore, the step size in the conventional GS algorithm is fixed, which makes it difficult for the algorithm to adapt to the dynamic characteristics of the residual error over time, resulting in slow convergence speed and unstable system performance.
In view of the foregoing, a new symbol detection method for OTSM systems in a high-speed mobile environment is needed to improve the processing efficiency of the systems in high-dimensional data, avoid the problems of slow convergence and oscillation caused by fixed step sizes, and improve the system stability.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a symbol detection method of OTSM systems in a high-speed mobile environment, which comprises the following steps:
s1, after receiving time domain vectors, carrying out hierarchical optimization on each time domain block to obtain symbol vectors of each time domain block;
S2, performing iterative detection by taking the symbol vector of each time domain block as an initial value of an improved GS algorithm after judgment, and obtaining an estimated value of the symbol vector of each time domain block;
s3, matrixing the estimated value of the symbol vector of each time domain block to obtain a time delay-time domain information symbol;
s4, carrying out Walsh-Hadamard transformation on the time delay-time domain information symbol to obtain an estimated value of the time delay-sequence domain information symbol.
Preferably, the step of performing hierarchical optimization on each time domain block includes:
Performing M-point FFT operation on the received time domain block to obtain a time-frequency block corresponding to the time domain block;
Performing MMSE equalization processing on each time-frequency block to obtain time-frequency domain estimation;
and performing IFFT operation of M points on the time-frequency domain estimation to obtain a symbol vector of the time domain block.
Further, the formula for performing MMSE equalization processing on the time-frequency block is as follows:
wherein, the Representing the signal estimate for the mth iteration in the nth time domain block,Representing the complex conjugate of the channel frequency domain response matrix for the mth iteration in the nth time domain block,Representing the frequency domain received signal for the mth iteration in the nth time domain block,A channel frequency domain response matrix representing the mth iteration in the nth time domain block,Representing the AWGN noise variance of the time domain block.
Preferably, the process of obtaining the estimated value of the symbol vector of each time domain block includes:
s21, obtaining a time domain information symbol after symbol vector judgment of each time domain block;
s22, iteratively solving a least square solution corresponding to the time domain input-output relation by using an improved GS method to obtain an estimated value of a symbol vector of a time domain block of the current iteration;
s23, adjusting the step length in the iterative solving process;
S24, performing hard decision on the least square solution of the iterative solution to obtain a time delay-sequence domain information symbol in each iterative process;
S25, performing relaxation and scaling on the time delay-sequence domain information symbol to obtain an estimated value of a symbol vector of a new time domain block, taking the estimated value as an initial value in a next iteration process, and repeating the steps S22-S25 until the iteration is completed.
Further, 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 follows:
wherein, the Representing an estimate of the symbol vector of the nth time domain block in the ith iteration,Representing the estimated value of the symbol vector of the nth time domain block in the i-1 th iteration, alpha (i) represents the step length of the ith iteration, T n represents the GS iteration matrix of the nth time domain block, and b n represents the correction term of the nth time domain block.
Further, the formula for adjusting the step length in the iterative solving process is as follows:
Where a (i) denotes the step size of the ith iteration, R (i-1) denotes the time domain block of the i-1 th iteration, R (i) denotes the time domain block of the i-th iteration, z n denotes the signal received by the n-th time domain block, R n denotes the matched filter matrix, Representing an estimate of the symbol vector of the nth time domain block in the ith iteration.
Further, the process of performing relaxation and scaling on the delay-sequence domain information symbol is expressed as:
wherein, the Representing an estimate of the symbol vector of the nth time domain block in the i+1th iteration, delta representing the relaxation parameter,Representing an estimated value of a symbol vector of an nth time domain block in an ith iteration, X (i) represents a delay-sequence domain information symbol of the ith iteration, and W N represents an N-point walsh-hadamard transform.
The method has the beneficial effects that the convergence speed and the stability of the system are obviously improved by introducing a block-level optimization strategy and a dynamic step length adjustment mechanism based on residual norms. Specifically, the block-level optimization performs local optimization on each small block by dividing the signal space into a plurality of small blocks, so that the calculation amount is reduced, and the processing efficiency of the system in high-dimensional data is improved. In addition, the dynamic step length adjustment mechanism can automatically adjust the step length according to the residual error size of each iteration, so that the problems of slow convergence and oscillation possibly caused by fixed step length are avoided, and the stability of the system is improved.
Through simulation experiments under various signal-to-noise ratios (SNR) and motion conditions, the result shows that the proposed algorithm has remarkable improvement on convergence speed and Bit Error Rate (BER) performance compared with the traditional GS method. By dynamically adjusting the step length and introducing block level optimization, the method can efficiently and stably perform signal estimation in a high-speed mobile environment.
Drawings
FIG. 1 is a diagram of a OTSM system transceiver model in the present invention;
FIG. 2 is a flow chart of a symbol detection method of OTSM system in a high-speed mobile environment according to the present invention;
FIG. 3 is a graph showing the comparison of error code performance of the algorithm of the present invention and the conventional GS algorithm in OTSM systems;
FIG. 4 is a graph showing the comparison of error performance of OTSM systems under different detection algorithms;
FIG. 5 is a chart of error performance of the algorithm of the present invention when a user moves at different speeds under OTSM system;
fig. 6 is a graph comparing error performance of the algorithm of the present invention with that of the conventional GS algorithm under different speeds and signal to noise ratios.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is applied to OTSM systems in high-speed mobile environments, and the invention adopts the following matrix/vector representation OTSM system. Order theRespectively transmitted and received information symbols. For the transmission of OTSM signal frames, the total frame duration is set to T f =nt, the bandwidth is set to b=mΔf, where Δf=1/T, the signal is sampled exactly in accordance with the pulse-shaping waveform, and N is chosen to be a power of 2. Fig. 1 shows a transmission model of OTSM system.
At the transmitting end, information symbolsIs divided into m symbol vectorsEach symbol vector x m is arranged at the m-th row of the matrix, and all symbol vectors are arranged neatly into a matrixThe column index and the row index are used to represent the delay index and the sequence index, respectively, of the delay sequence grid.
X=[x0,x1,…,xM-1]T (1)
The last l max rows of X are then set to zero vector, where l max represents the discrete channel delay spread index. As shown in fig. 1, zero Padding (ZP) along the delay domain helps to avoid inter-block interference due to channel delay spread. For each symbol vector x m, an N-point WHT transform is performed, which is converted into a delay time domain:
Matrix array Comprising delay time samples which are vectorised to obtain a delay time domain symbol vectorAnd ultimately to the channel, where each symbol vector represents a signal sample at a particular time delay.
At the receiving end, the time domain signal r (t) demodulates the received time domain vector in the reverse step of the transmitting end after analog-to-digital conversion and sampling. Order theIs a time domain vector after RF front-end operation and ADC sampling. Arranging the received information symbols y in a matrix by columnsWith each column corresponding to a received symbol vector.
For a pair ofPerforming an N-point WHT change to obtain an information symbol Y in the DS domain, wherein:
The baseband equivalent channel model of interest for the present invention includes P propagation paths, each path having a different gain v i, delay offset τ i, and doppler frequency offset v i. Let the maximum delay spread of the channel be τ max, the maximum doppler spread be v max, and the corresponding doppler spread length and channel delay spread length be α= [ v max NT ] and β= [ τ max mΔf ], respectively. In a OTSM system with a carrier frequency of 4GHz and a subcarrier spacing of 15kHz, the extended vehicle channel model (EVA) is assumed. In this setting, the delay-doppler spread length is α=10, and β=3 is significantly smaller than the sampling number NM of the system.
Since the number of channel coefficients in the delay-doppler domain, P, is typically limited, the channel response can be presented as a sparse representation:
the corresponding continuous time-varying channel impulse response function can be expressed as
Starting from the received time domain signal r (t), the successive time domain input-output relationship can be expressed as
Thus, the input-output relationship in the time domain can be expressed as
r=H·s+w (9)
Wherein, the Mean value 0, varianceIs a white gaussian noise of (a) and (b),Is a time domain discrete channel matrix.
Wherein, the
As shown in fig. 1, due to the introduction of time-domain Zero Padding (ZP), inter-block interference on the time-domain blocks is effectively avoided. Thus, in the time domain, the input-output relationship in equation (9) is partitioned and can be processed independently, specifically expressed as
rn＝Hn·sn+wn,n=0,...,N-1(11)
Wherein, the And H n is the nth time domain block channel matrix.
Notably, due to the Walsh-Hadamard spreading, the components of all N time domain blocks have equal distribution with the information symbols of each delay sequence domain. By the expansion mode, each component in the time domain block can be distributed in a balanced mode on the frequency domain, so that signals corresponding to each time domain block have the same power distribution, and the overall performance of the system is improved.
In a static (or very low doppler spread) wireless channel environment, the time domain channel matrix of each block is assumed to be a cyclic matrix so that it can be diagonalized in the frequency domain. For a particular subcarrier and reference signal sequence, the entire frame may be transmitted over multiple channels, and each sub-block may be composed of corresponding channels. However, in a mobile channel environment, doppler expansion can introduce interference between the frequency domain samples of each block due to the time-varying nature of the channel, resulting in the time domain channel matrix not being a cyclic matrix. In order to cope with specific challenges in a high-speed mobile environment, where the doppler effect and the time-frequency interference are particularly prominent, the present invention proposes a symbol detection method of OTSM systems in a high-speed mobile environment, as shown in fig. 2 (BDSGS is an algorithm of the present invention), where the method includes the following steps:
and S1, after receiving the time domain vectors, carrying out hierarchical optimization on each time domain block to obtain the symbol vector of each time domain block.
A frame typically exhibits good performance if it contains a plurality of sub-carriers and the cross-correlation function value between two adjacent sub-carriers exceeds a given threshold. 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 constant, but the channel will vary from block to block. The advantage of this assumption is that a single tap MMSE equalizer can be used for signal detection in each block and the estimates for the individual blocks combined by Walsh-Hadamard transform (WHT).
Performing M-point FFT operation on the received time domain block to obtain a time-frequency block corresponding to the time domain block:
then, each time-frequency block may be MMSE equalized to obtain a time-frequency domain estimate:
Where m=0,..m-1, n=0,..n-1, For AWGN noise variance of each time domain block, the frequency domain channel coefficients can be expressed by the following formula:
Wherein F M denotes a transform matrix for performing FFT operation on the time domain signal, Represents the conjugate transpose of the transform matrix for performing FFT operations on the time domain signal.
And performing IFFT operation of M points on the time-frequency domain estimation to obtain a symbol vector of the time domain block.
S2, carrying out iterative detection on the symbol vector of each time domain block as an initial value of an improved GS algorithm after judging, and obtaining an estimated value of the symbol vector of each time domain block.
Dividing the entire frame into short blocks may assume that the channel remains unchanged for the duration TTT of each block. However, under high doppler spread conditions, this assumption may lead to performance degradation, especially when higher order QAM symbols are used. In order to solve the problem, the invention firstly uses a hierarchical optimization strategy to accurately recover and demodulate signals. Next, the present invention introduces an improved Gauss Seidel (GS) algorithm in combination with a dynamic step size adjustment mechanism. The mechanism can automatically adjust the step length according to the residual error in each iteration, thereby avoiding the problems of slow convergence speed and oscillation possibly caused by the traditional fixed step length.
S21, obtaining time domain information symbols after symbol vector judgment of each time domain block, and obtaining time domain input-output relation to the time domain information symbols through matched filtering operation.
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 expression (9), the matrix input-output relationship after the matched filtering operation can be expressed as:
Wherein the method comprises the steps of And is also provided with
And S22, iteratively solving a least square solution corresponding to the time domain input-output relation by using an improved GS method to obtain an estimated value of the symbol vector of the time domain block of the current iteration.
Iterative solution of least squares of the M-dimensional linear system of equations in (16) using the GS method
Let D n and L n be matrices containing diagonal elements and lower triangular elements of the matched filter matrix R n. In each iteration, the process of solving the estimated value by the GS iteration method can be expressed as follows:
wherein, the Representing an estimate of the symbol vector of the nth time domain block in the ith iteration,Representing the estimated value of the symbol vector of the nth time domain block in the i-1 th iteration, alpha (i) represents the step length of the ith iteration, T n represents the GS iteration matrix of the nth time domain block, and b n represents the correction term used for updating the signal estimation in the current iteration process of the nth time domain block.
S23, adjusting the step length in the iterative solving process.
Step length in the iterative solving process is adjusted based on residual norms:
Where r (i-1) represents the time domain block of the i-1 th iteration, r (i) represents the time domain block of the i-th iteration, and z n represents the signal received by the n-th time domain block.
And S24, performing hard decision on the least square solution of the iterative solution to obtain a time delay-sequence domain information symbol in each iterative process.
The delay sequence domain information in the ith iteration is symbolized as:
Wherein the method comprises the steps of And D (), represents a decision function that replaces all elements of the input (measured in euclidean distance) with the nearest QAM symbol.
S25, performing relaxation and scaling on the time delay-sequence domain information symbol to obtain an estimated value of a symbol vector of a new time domain block, taking the estimated value as an initial value in a next iteration process, and repeating the steps S22-S25 until the iteration is completed.
Where δ is a relaxation parameter for improving detector convergence for higher modulation schemes such as 64-QAM. The introduction of the relaxation parameters helps to adjust the update step size of the estimation value in each iteration, thereby improving the detection performance of the higher order QAM symbols, especially in high noise or multipath propagation environments. By proper selection of δ, convergence can be accelerated and errors reduced, making the detector more stable and efficient under higher order modulation.
And S3, matrixing the estimated value of the symbol vector of each time domain block to obtain the time delay-time domain information symbol.
The symbol vector estimation value of each time domain block is matrixed, and the symbol vector matrixing can obtain time delay-time domain information symbols:
s4, carrying out Walsh-Hadamard transformation on the time delay-time domain information symbol to obtain an estimated value of the time delay-sequence domain information symbol.
N points WHT is carried out on the time delay-time domain information symbols to obtain estimated values of the transmitted time delay-sequence domain information symbols:
the invention was evaluated:
The Gaussian Seidel algorithm based on block-level optimization and dynamic step length adjustment designed by the invention is subjected to simulation comparison with the traditional Gaussian Seidel iterative detection algorithm. All simulations employed the parameters listed in table 1 unless otherwise specified. As shown in table 1, OTSM frames of n=32 and m=32 were generated in the simulation, the subcarrier spacing Δf was set to 15kHz, and the carrier frequency was 4GHz. The maximum delay spread (in integer taps) is set to 4 (L max =4), approximately 4 μs, so the maximum delay tap number seen by the receiver is l=4. The channel delay model is a standard EVA model and each data point in the BER map is counted by sending a 10 5 frame signal. The Doppler shift of the channel is generated by Jakes equation v i＝vmaxcos(θi), v max is the maximum speed of movement and θ i is uniformly distributed over [ -pi, pi ].
Table 1 simulation parameters
From the results shown in fig. 3, it can be seen that the proposed algorithm has a significant improvement in error performance over the conventional GS algorithm under high signal-to-noise conditions and different equalization algorithms. This is mainly because the block level optimization improves the computational efficiency and reduces the dependence of global information by dividing the signal into smaller blocks and locally processing each block, while the dynamic step adjustment flexibly adjusts the step according to the residual size of each iteration, avoiding the slow convergence or oscillation problem caused by the fixed step. The algorithm can be converged more stably under the high-speed movement and multipath effect, the Bit Error Rate (BER) performance is obviously improved, the calculation complexity is reduced, and the algorithm has higher convergence speed and better performance especially under the environment of Doppler expansion and time delay variation. From the results of fig. 3, the proposed algorithm can obtain better performance under the condition of high signal-to-noise ratio, which indicates that the algorithm has better performance advantage in OTSM systems.
From the comparison result of fig. 4, it can be seen that under the condition of high signal-to-noise ratio and different modulation modes, the proposed algorithm is still applicable to different modulation modes, and there is still a significant improvement in error code performance. From the results of fig. 4, the proposed algorithm can obtain better performance under different modulation modes and high signal to noise ratio conditions, which indicates that the algorithm has better applicability in OTSM systems.
Fig. 5 shows the Bit Error Rate (BER) behavior of the proposed algorithm at various speeds and signal-to-noise ratios in OTSM systems. It can be seen from the figure that the proposed algorithm shows better error performance in high-speed moving state, and the error performance improves with the increase of speed within a certain range. This shows that the proposed algorithm exhibits a strong adaptability and robustness in coping with channel characteristics in a high-speed mobile environment. Surprisingly, the Bit Error Rate (BER) decreases instead as the doppler shift increases, a phenomenon that is a new finding for conventional techniques that rely on static channel modulation. In fact, a larger doppler shift is instead beneficial for improving performance when modulating in the delay-sequence domain. The Doppler frequency shift caused by high-speed movement enhances the spectrum expansibility of signals, so that the system can more effectively distinguish signal paths corresponding to different Doppler frequency shifts, thereby reducing the influence of interference among Doppler and improving the error code performance. Meanwhile, the increase of Doppler frequency shift leads to the expansion of the signal spectrum in the frequency domain, so that the utilization efficiency of spectrum resources is improved. This enables the system to better utilize orthogonal resources, improving the capacity and overall performance of the system.
To more intuitively evaluate performance under different user movement rates and signal-to-noise conditions, we performed a three-dimensional analysis of the GS algorithm and the algorithms presented herein, as shown in fig. 6. As can be seen from the figure, the proposed algorithm exhibits significant performance advantages over the conventional GS algorithm as the speed and signal-to-noise ratio increase. This shows that the proposed algorithm can better meet the demands of future wireless mobile communication systems.
In summary, the present invention proposes a symbol detection method of OTSM system in high-speed mobile environment, which adopts OTSM iterative algorithm based on block-level optimization and dynamic step adjustment, considers high doppler shift caused by high-speed mobile environment, and inter-symbol interference caused by multipath transmission, the algorithm firstly adopts block-level optimization detection in time-frequency domain to accurately recover and demodulate signals, suppresses interference between carriers and reduces influence of doppler shift on signals, then uses GS algorithm based on dynamic step adjustment mechanism of residual norm, can automatically adjust step according to residual magnitude of each iteration, and avoids convergence slowness and oscillation problems possibly caused by fixed step. Simulation experiment results show that the method provided by the invention obviously improves the error code performance under different speeds and modulation modes, and particularly shows outstanding performance in a high-speed mobile environment. The innovative research is of great significance in improving the overall performance of OTSM systems, and provides a more reliable and efficient signal processing solution for wireless communication systems in practical applications. Particularly in a high-speed moving scene, the excellent performance of the algorithm provides powerful support for applications in the fields of mobile communication, internet of vehicles and the like, and lays a solid foundation for realizing high-quality communication service.
While the foregoing is directed to embodiments, aspects and advantages of the present invention, other and further details of the invention may be had by the foregoing description, it will be understood that the foregoing embodiments are merely exemplary of the invention, and that any changes, substitutions, alterations, etc. which may be made herein without departing from the spirit and principles of the invention.

Claims (4)

1. A symbol detection method for OTSM systems in a high-speed mobile environment, comprising:
s1, after receiving time domain vectors, carrying out hierarchical optimization on each time domain block to obtain symbol vectors of each time domain block;
S2, carrying out iterative detection on the symbol vector of each time domain block as an initial value of an improved GS algorithm after judging to obtain an estimated value of the symbol vector of each time domain block, wherein the process for obtaining the estimated value of the symbol vector of each time domain block comprises the following steps:
s21, obtaining a time domain information symbol after symbol vector judgment of each time domain block;
S22, iteratively solving a least square solution corresponding to the time domain input-output relation by using an improved GS method to obtain an estimated value of a symbol vector of a time domain block of the current iteration, wherein the process of iteratively solving the least square solution corresponding to the time domain input-output relation by using the GS method can be expressed as follows:
wherein, the Representing an estimate of the symbol vector of the nth time domain block in the ith iteration,Representing the estimated value of the symbol vector of the nth time domain block in the i-1 th iteration, alpha (i) representing the step length of the ith iteration, T n representing the GS iteration matrix of the nth time domain block, and b n representing the correction term of the nth time domain block;
s23, adjusting the step length in the iterative solving process, wherein the formula for adjusting the step length in the iterative solving process is as follows:
Where R (i-1) represents the time domain block of the i-1 th iteration, R (i) represents the time domain block of the i-1 th iteration, z n represents the signal received by the n-th time domain block, and R n represents the matched filter matrix;
S24, performing hard decision on the least square solution of the iterative solution to obtain a time delay-sequence domain information symbol in each iterative process;
s25, performing relaxation and scaling on the time delay-sequence domain information symbol to obtain an estimated value of a symbol vector of a new time domain block, taking the estimated value as an initial value in a next iteration process, and repeating the steps S22-S25 until the iteration is completed;
s3, matrixing the estimated value of the symbol vector of each time domain block to obtain a time delay-time domain information symbol;
s4, carrying out Walsh-Hadamard transformation on the time delay-time domain information symbol to obtain an estimated value of the time delay-sequence domain information symbol.
2. The symbol detection method for OTSM systems in a high-speed mobile environment according to claim 1, wherein the step of hierarchical optimization of each time domain block includes:
Performing M-point FFT operation on the received time domain block to obtain a time-frequency block corresponding to the time domain block;
Performing MMSE equalization processing on each time-frequency block to obtain time-frequency domain estimation;
and performing IFFT operation of M points on the time-frequency domain estimation to obtain a symbol vector of the time domain block.
3. The symbol detection method of OTSM systems in a high-speed mobile environment according to claim 2, wherein the formula for performing MMSE equalization processing on a time block is:
wherein, the Representing the signal estimate for the mth iteration in the nth time domain block,Representing the complex conjugate of the channel frequency domain response matrix for the mth iteration in the nth time domain block,Representing the frequency domain received signal for the mth iteration in the nth time domain block,A channel frequency domain response matrix representing the mth iteration in the nth time domain block,Representing the AWGN noise variance of the time domain block.
4. The symbol detection method of OTSM systems in a high-speed mobile environment according to claim 1, wherein the process of performing relaxation and scaling on the delay-sequence domain information symbol is expressed as:
wherein, the Representing an estimate of the symbol vector of the nth time domain block in the i+1th iteration, delta representing the relaxation parameter,Representing an estimated value of a symbol vector of an nth time domain block in an ith iteration, X (i) represents a delay-sequence domain information symbol of the ith iteration, and W N represents an N-point walsh-hadamard transform.
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CN115277335A (en) * 2022-07-21 2022-11-01 重庆邮电大学 Symbol detection method of orthogonal time sequence multiplexing system
CN115277334A (en) * 2022-07-21 2022-11-01 重庆邮电大学 MRC iterative equalization method of OTSM system under high-speed mobile environment

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