RIS-assisted high-speed mobile communication channel estimation method

Abstract

The invention relates to an RIS-assisted channel estimation method in a high-speed mobile communication scene, and belongs to the field of wireless communication. The method comprises the following steps: establishing an RIS auxiliary wireless communication system channel estimation system model based on the OTFS modulation technology; the pilot frequency overhead is reduced through a compressed sensing technology by utilizing the sparsity of the gain of a millimeter wave channel in a Doppler time delay domain; the improved greedy reconstruction algorithm is utilized to quickly approach the real sparseness, so that the convergence speed of the recovery algorithm is greatly improved; and dividing adjacent RIS elements into one group, estimating a grouped channel matrix, and performing up-sampling on the matrix by using a Lagrange interpolation method to recover a complete channel matrix. The DV-STOMP algorithm is provided according to the sparsity of the OTFS millimeter wave transmission channel in the Doppler-time delay domain, and the convergence speed is increased under the condition that the target precision is ensured by using the Dice coefficient and the variable step size. According to the channel estimation scheme, the channel estimation precision of the RIS-assisted wireless communication system in a high-speed moving scene can be effectively improved.

Description

Technical Field

[0001] The invention belongs to the field of wireless communications and relates to an RIS-assisted wireless channel estimation method based on OTFS. Background Art

[0002] In recent years, mobile communication technology has developed rapidly. The 6G mobile communication system will contain a large number of nodes and require a mobility of up to 1000km / h. As a key technology for intelligent wireless communication environment, Reflecting Intelligent Surface (RIS) has become a potential solution to meet the challenges of future wireless communication networks.

[0003] RIS is essentially a system composed of metamaterials, each element of which can independently adjust the amplitude and phase of the reflected signal, thereby improving the wireless communication environment and improving the communication quality. In addition, placing the RIS between the base station and the user can provide an additional transmission path for the direct link affected by obstacles, further improving the communication quality. At present, RIS-assisted wireless communication systems have obvious advantages over traditional communication systems in many aspects, such as coverage enhancement, system capacity improvement, and enhanced physical layer security transmission. In order to achieve the above goals, it is necessary to regulate the reflection unit of RIS to a certain extent, such as beamforming, which puts forward requirements for the accuracy of channel state information (CSI). Due to the passive characteristics of RIS elements and their own lack of processing capabilities, it is currently difficult to directly obtain channel state information using traditional estimation methods.

[0004] Traditional channel estimation methods for RIS communication systems mainly focus on quasi-static channel scenarios, assuming that channel characteristics remain unchanged over a short period of time. Therefore, these methods can effectively perform channel estimation in quasi-static environments. However, with the emergence of high-speed mobile scenarios, such as high-speed rail and satellite communications, channel characteristics change rapidly and dynamically, making it impossible for traditional methods to capture these changes in a timely manner. This limitation may lead to inaccurate channel estimation, thereby affecting communication quality and system performance. Therefore, it is urgent to develop new channel estimation techniques to meet the needs of these highly dynamic environments.

[0005] At present, there have been extensive studies on RIS-assisted channel estimation schemes, such as deep learning (DL), adaptive channel estimation methods, and compressed sensing. DL shows significant advantages in extracting channel features. When this technology is applied to RIS channel estimation, it can significantly improve the accuracy of estimation. However, DL methods require a large number of data sets for time-consuming offline training and have relatively limited generalization capabilities, which restricts direct application in a wider range of scenarios. Adaptive channel estimation methods can be applied to different channel conditions and can be adaptively adjusted to optimize performance. Adaptive channel estimation methods have the advantages of strong real-time performance and high flexibility. They can dynamically adjust estimation parameters according to changes in channel states to improve accuracy and robustness. However, this method has the disadvantages of slow convergence speed and dependence on prior knowledge, and may show weak performance in high-noise environments. Compressed sensing technology has a unique ability to accurately restore signal or channel states with fewer observation samples and reduce pilot overhead. This makes it play an important role in channel estimation. However, one of the key points of using compressed sensing technology is the reconstruction of sparse channel matrices. At present, the advantages of greedy matching pursuit algorithms such as orthogonal matching pursuit (OMP) are easy to implement and simple ideas. However, the stability of OMP is weak because only one atom can be selected in each iteration, and this atom may sometimes be suboptimal. In the Compressed Sampling Matching Pursuit (CoSaMP) algorithm, the Compressed Sampling Matching Pursuit (CoSaMP) method is used to remove erroneous atoms from the support set. However, the actual sparsity K is unknown. Therefore, the Adaptive Matching Pursuit Algorithm (SAMP) proposes to approximate the true sparsity by iterative step size. To ensure accuracy, the initial step size is usually set small, but if it is much smaller than the actual sparsity, it will lead to a large number of iterations and increase the calculation time. In high-speed mobile scenarios, this time overhead does not meet the requirements of high-performance estimation.

[0006] In 6G communications, millimeter waves are widely used in high-speed mobile scenarios because of their significant advantages such as extremely wide bandwidth, low latency, and stability under various climatic conditions. However, due to the loss of free space propagation paths, millimeter waves mainly rely on line-of-sight propagation and non-line-of-sight propagation with a single reflection. This sparse channel characteristic leads to limited effective paths in the channel, which makes the channel estimation problem relatively simple. According to the sparse characteristics of millimeter wave channels, the sparseness of cascaded channel responses is utilized to introduce CS technology for channel estimation in RIS-assisted wireless communication systems to reduce pilot overhead. Compressed sensing technology has a unique ability to accurately restore the signal or channel state with fewer observation samples, which makes it play an important role in channel estimation.

[0007] Aiming at quickly estimating high Doppler frequency shift channels within a limited frame length. Hadani et al. proposed the Orthogonal Time Frequency Space (OTFS) modulation technology in 2017. OTFS technology is an innovative modulation method that performs modulation in the Delay-Doppler (DD) domain and converts the time-varying channel in the Time Frequency (TF) domain into the time-invariant channel in the DD domain. Through this conversion, the OTFS technology can obtain the full channel diversity gain in time and frequency, thereby significantly improving the performance of the communication system. Based on the above, the performance gain of OTFS in high-speed mobile scenarios is significantly higher than that of traditional OFDM modulation technology. This conclusion shows the effectiveness of OTFS technology in dealing with channel estimation problems in high-speed mobile scenarios.

[0008] Since the reflection units of RIS are passive and numerous, the complexity of channel estimation increases significantly. In addition, in a multi-antenna configuration, the scale of estimated parameters increases exponentially, further increasing the computational complexity and resource consumption. Therefore, it is particularly important to explore efficient and low-complexity channel estimation solutions in high-speed mobile and multi-antenna scenarios.

[0009] In response to the above problems, the present invention proposes an efficient channel estimation solution. In response to the challenges of wireless communication in high-speed mobile scenarios, the present invention utilizes the RIS-OTFS wireless communication system to effectively reduce the impact of high Doppler frequency shift on system performance by processing signals in the delay-Doppler (DD) domain. In order to further enhance the accuracy of channel estimation, the present invention proposes an innovative algorithm in the sparse channel matrix recovery stage - a variable-step target matching algorithm based on the Dice coefficient (Dice Variable-Step Target Orthogonal Matching Pursuit DV-STOMP). The DV-STOMP algorithm is an improvement on the traditional OMP algorithm. It introduces the Dice coefficient as a matching criterion to replace the traditional inner product criterion. This improvement enables the algorithm to more accurately identify the basis vectors most relevant to the signal. At the same time, the present invention introduces the target estimation sparsity K to obtain the initial sparsity estimate. Considering the uncertainty that sparsity may exist in practical applications, the present invention designs a variable step size mechanism to optimize the algorithm, thereby accelerating the convergence speed. In order to reduce the complexity of channel estimation and reduce the estimation parameters, the present invention groups the adjacent elements of RIS. This grouping strategy not only simplifies the channel estimation problem, but also reduces the computational burden. On this basis, in order to ensure the accuracy of channel estimation and make full use of the correlation between RIS element channels, the present invention adopts the Lagrange interpolation method. The grouped channel estimation matrix is ​​restored to a complete matrix form, which improves the performance of channel estimation. Summary of the invention

[0010] In view of the problems existing in the prior art, the present invention provides a channel estimation method in a high-speed mobile communication scenario, aiming to improve the efficiency of RIS-assisted wireless communication channel estimation.

[0011] First, a RIS-assisted wireless channel estimation method based on OTFS is provided, in which data information and pilot codes are encoded in a single Doppler-delay domain frame, and a guard interval is designed according to the maximum delay and the maximum Doppler shift; a low peak-to-average power ratio sequence is used as a pilot signal to achieve a smaller difference between the peak power and the average power of the signal, thereby improving the signal transmission efficiency and stability; for millimeter wave channel estimation, the channel state information matrix in the Doppler-delay domain based on OTFS is sparse, and the channel estimation problem is converted into a sparse channel information matrix recovery problem.

[0012] Furthermore, the greedy algorithm of orthogonal matching is characterized by two parts: atomic correlation and step size optimization, including: using the Dice coefficient instead of the traditional similarity calculation; and then using the double threshold method to dynamically adjust the step size to ensure the final sparse matrix recovery accuracy.

[0013] Furthermore, a method for grouping RIS reflection units is characterized in that the number of channels to be estimated is reduced, the estimation efficiency is improved, and the complexity of channel estimation in high-speed mobile scenarios is reduced. The method comprises: firstly, by grouping adjacent reflection elements of RIS, each group containing N0 elements, the number of channels to be estimated is The complexity of channel estimation is reduced.

[0014] Furthermore, the method for improving the accuracy of the channel information matrix based on Lagrange interpolation is characterized in that a basis function is constructed according to the position between the reference point and the point to be interpolated. The method of piecewise interpolation and construction of low-order basis functions is adopted to reduce the complexity of interpolation and avoid the phenomenon that the interpolation result deviates significantly from the original data.

[0015] In a second aspect, a RIS-assisted wireless channel estimation device of OTFS is provided, comprising:

[0016] An acquisition unit, based on the wireless communication channel model of OTFS, acquires a received signal and a local reference signal;

[0017] An acquisition unit estimates a channel response in the Doppler-delay domain using an improved sparse channel matrix recovery algorithm based on a received pilot signal in the Doppler-delay domain;

[0018] A processing unit, performing up-sampling using the estimated packet channel state information to restore a complete channel state information matrix;

[0019] The processing unit recovers the transmitted signal using the channel state information.

[0020] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, the memory being coupled to the processor, the memory being used to store computer program code, the computer program code comprising computer instructions, the processor reading the computer instructions from the memory so that the electronic device performs the RIS-assisted wireless channel estimation method based on OTFS as described in the first aspect.

[0021] In a fourth aspect, the present application provides a computer storage medium comprising computer instructions. When the computer instructions are executed on a terminal, the terminal executes the RIS-assisted wireless channel estimation method based on OTFS as described in the first aspect.

[0022] In a fifth aspect, the present application provides a computer program product. When the computer program product is run on a computer, the computer executes the RIS-assisted wireless channel estimation method based on OTFS as described in the first aspect.

[0023] In addition, the present invention proposes an efficient channel estimation scheme. The present invention utilizes the RIS-OTFS wireless communication system to effectively reduce the impact of high Doppler frequency shift on system performance by processing signals in the delay-Doppler (DD) domain. In order to further enhance the accuracy of channel estimation, the present invention proposes an innovative algorithm in the sparse channel matrix recovery stage, namely a variable step size target matching algorithm (DV-STOMP) based on the Dice coefficient. The DV-STOMP algorithm is an improvement on the traditional OMP algorithm. It introduces the Dice coefficient as a matching criterion to replace the traditional inner product criterion. This improvement enables the algorithm to more accurately identify the basis vectors most relevant to the signal. At the same time, the present invention introduces the target estimation sparsity K to obtain the initial sparsity estimate. Considering the uncertainty that sparsity may exist in practical applications, the present invention designs a variable step size mechanism to optimize the algorithm, thereby accelerating the convergence speed. In order to reduce the complexity of channel estimation and reduce the estimation parameters, the present invention groups adjacent elements of RIS into one group. On this basis, the Lagrange interpolation method is used to restore the grouped channel estimation matrix to a complete matrix form, thereby improving the performance of channel estimation.

[0024] Other advantages, objectives and features of the present invention will be described in the following description, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0026] Figure 1 A communication scenario diagram after the application system model is constructed in the embodiment of the present application;

[0027] Figure 2 A flowchart of an efficient channel estimation method proposed in an embodiment of the present application.

[0028] Figure 3 A flow chart of a channel estimation method based on OTFS modulation proposed in an embodiment of the present application;

[0029] Figure 4 A signal detection device provided in an embodiment of the present application;

[0030] Figure 5 This is an electronic device according to an example of this application. DETAILED DESCRIPTION

[0031] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict. Among them, the drawings are only used for exemplary illustrations, and only schematic diagrams are shown, not physical drawings, and cannot be understood as limitations on the present invention; it is understandable to those skilled in the art that some well-known structures and their descriptions in the drawings may be omitted.

[0032] like Figure 1 The figure shows a scenario to which the present invention is applied, which includes a base station, a single user and a RIS. The BS includes M BS The user is equipped with a single antenna and the RIS is fixed at a high place, and its reflection path can cover the user. Assume that the RIS is composed of N RIS =N1×N2 reflective elements form a uniform planar array, and both the antenna array and the RIS element use a uniform planar array (UPA). In millimeter wave communication, when the direct line of sight between the BS and the UE is blocked, the present invention uses RIS to construct a BS-RIS-UE virtual line-of-sight link. represents the channel from BS to RIS, represents the channel from RIS to UE. RIS guides the signal around obstacles by adjusting the phase of its surface elements, thereby expanding the application range and reliability of millimeter wave communication. The present invention assumes that the channel impulse response G between the bth antenna of the BS and the rth reflection unit of the RIS is b，r It can be expressed as:

[0033]

[0034] Where L g represents the number of paths between the bth antenna and the rth reflection unit of the RIS. i , τ i denote the transmission coefficient and delay of the ith path from BS to RIS. Similarly, h r It can be expressed as:

[0035]

[0036] Where L h represents the number of paths from the rth element of RIS to the UE, α j , τ jThey represent the j-th path gain and delay from RIS to UE, represents the Doppler frequency shift, where v represents the UE moving speed and λ represents the signal wavelength. They respectively represent the azimuth and elevation angles of the signal reaching the UE.

[0037] set up represents the RIS reflection coefficient, where β n represents the amplitude coefficient, θ n Represents the reflection phase coefficient. The user's time domain received signal can be expressed as:

[0038]

[0039] Where x(t) represents the time domain representation of the signal sent by the BS, is the time domain representation of the noise to which the user is subjected. The present invention uses H to represent the cascaded channel matrix of the user. Because the scattering environment around the BS and RIS is limited, there are only a few non-zero elements in the cascade channel H, showing sparsity. After the pilot transmission, assuming that the transmission symbol x(t) = 1, the measurement matrix can be obtained:

[0040] Y=HΘ+Z, (4)

[0041] here, represents the perception matrix, Z = [z(t)1, z(t)2, ..., z(t) M ] T Based on (4), the CS algorithm can be used to estimate the cascade channel.

[0042] Figure 2 As one embodiment of the present application, a flow chart of a RIS-assisted wireless communication channel estimation method based on OTFS is provided, and the method is:

[0043] S201: Based on the cascade system model, the estimated cascade channels are grouped to reduce the number of estimated channels. It should also be noted that the present invention sets parameter ρ (0<ρ<1) as the grouping density, N0 represents the number of elements in the group, and N represents the total number of RIS elements. It can be defined as:

[0044]

[0045] S202: Based on the RIS-assisted wireless channel estimation model in the high-speed mobile scenario, a signal vector transmitted through the cascade channel is obtained and a compressive sampling is performed on the signal vector to obtain a signal Y.

[0046] S203: Estimate the channel information matrix H using Y and the perception matrix through an improved orthogonal matching algorithm.

[0047] It should be noted that since RIS is usually fixed at a high place, there are fewer scatterers around, and the user area in the high-speed rail scenario is relatively fixed, according to the characteristics of the millimeter wave channel, it can be seen that in the RIS-assisted wireless communication system, most of the energy is concentrated on only a few paths, which means that the channel response in the Doppler-delay domain has significant energy only on a few Doppler-delay grid points, and the constructed parameter channel is sparse in the Doppler-delay domain, which can be estimated using the CS channel estimation method. Since the OTFS channel response has the characteristics of local sparsity and uneven distribution, the sparse channel matrix recovery methods such as basis pursuit and gradient descent are not applicable. The present invention uses a greedy algorithm to recover the sparse channel matrix. The present invention proposes an enhanced STOMP algorithm, namely a variable step size target matching algorithm based on the Dice coefficient. In DV-STOMP, the Dice coefficient is used instead of the traditional inner product criterion. The present invention uses a standardized threshold when setting the threshold, and does not need to be determined by the size of the inner product, thereby simplifying the algorithm. In order to better achieve adaptive sparsity, the present invention sets a variable step size so that the algorithm can converge quickly while controlling the accuracy loss within a smaller range, thereby increasing the applicability of the algorithm.

[0048] The Dice coefficient is used to measure the effect of amplifying important components in the data. The Dice coefficient is defined as:

[0049]

[0050] This coefficient uses the arithmetic mean of the quadratic sum of each component in the vector, rather than the geometric mean on the denominator. The arithmetic mean can effectively highlight the important components of the vector. Therefore, the Dice coefficient criterion can select more suitable atomic matches. Under this criterion, the principle of selecting atoms is:

[0051] P = Max(Dice(A, r n-1 ), L), (7)

[0052] In order to speed up the convergence, the strategy of pre-estimating sparsity is adopted to get the pre-estimated sparsity k0 close to the actual sparsity k. In this strategy, the threshold is first multiplied by the normalized threshold and the parameter th to get a threshold, and then Dice(A, r n-1 ) is greater than or equal to the threshold, represented by:

[0053]

[0054] in, represents the threshold, and the length of C is used as the estimated sparsity k0.

[0055] When the present invention uses k0 to approach the actual sparsity k, if the difference with the step size is too large, errors usually occur. Therefore, the present invention adopts a variable step size in the iterative process, taking into account the residual energy During the iteration process, the threshold parameters η1 and η2 are added to make judgments:

[0056] η2||y||2≤||r new ||2≤η1||y||2,(9)

[0057] Among them, η1>η2, y is the measurement matrix, r new Represents the residual matrix in the current iteration. The present invention sets the initial step size to S0 and adjusts it according to the following rules

[0058]

[0059] The improved algorithm uses the Dice coefficient instead of the inner product criterion. After estimating the sparsity k0, two threshold parameters are introduced to adaptively adjust the step size.

[0060] S204: Up-sample the estimated channel matrix using Lagrange interpolation method to restore the complete information matrix H.

[0061] It should be noted that this paper groups adjacent RIS elements, each group contains N0 RIS elements, so the number of channels to be estimated is The estimation amount is greatly reduced. The basis of the Lagrange interpolation algorithm is to construct an interpolation basis function according to the position between the reference point and the point to be interpolated. The interpolation basis function is weighted superimposed at each reference point to obtain the final result. The interpolation basis function is expressed as:

[0062]

[0063] Among them, l m (x) represents the mth channel coefficient. Based on this function, the interpolation polynomial can be obtained as

[0064]

[0065] like Figure 3 As shown in FIG. 1 , the modulation and demodulation process of OTFS uses the inverse symplectic Fourier transform (ISFFT) at the transmitting end to transform the DD domain signal x[k, l] into the TF domain, which can be expressed as:

[0066]

[0067] Where x[k, l] represents the signal in the DD domain. After the Heisenberg transform, the TF domain signal x[m, n] is transformed to the time domain, expressed as

[0068]

[0069] in t∈[0,T] represents the pulse signal at which the base station sends the signal. T represents the sampling time, and Δf represents the subcarrier spacing.

[0070] Since BS-RIS-UE is a high Doppler frequency shift channel, it is assumed that the delay and Doppler spread corresponding to each path are τ, so the Doppler-delay domain cascade model can be expressed as:

[0071] h(τ,v)=h i，j δ(τ-τ i，j )δ(vv i，j ). (15)

[0072] In the Doppler-delay domain And M and N are large enough to ensure that they are greater than the delay and Doppler spread of all paths. ij and k ij Represents the channel gain coordinates.

[0073] Figure 4 The schematic diagram of the structure of a RIS-assisted wireless channel estimation method and device based on OTFS involved in the above embodiment is shown. The device comprises: an obtaining unit 601 and a setting unit 602.

[0074] An obtaining unit 601 obtains a received signal and a local reference signal according to a system model;

[0075] The processing unit 602 estimates the response of the channel in the Doppler-delay domain by using an improved sparse channel matrix recovery algorithm according to the received pilot signal in the Doppler-delay domain;

[0076] Processing unit 602, using the estimated grouped channel state information to up-sample and restore a complete channel state information matrix;

[0077] The processing unit 602 recovers the transmitted signal using the channel state information.

[0078] It should also be noted that, for the convenience and brevity of description, the specific working process of the RIS-assisted wireless channel estimation method and device based on OTFS described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0079] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 5As shown, the device includes a processor 701 and a memory 702, the memory 702 is coupled to the processor 701, the memory is used to store computer program code, the computer program code includes computer instructions, when the processor reads the computer instructions from the memory, so that the electronic device executes a RIS-assisted wireless channel estimation method based on OTFS proposed in the embodiment.

[0080] An embodiment of the present application also provides a computer-readable storage medium, which may include a computer program or instruction. When the computer program or instruction is executed on a computer, the computer executes the RIS-assisted wireless channel estimation method based on OTFS described in the above embodiment.

[0081] An embodiment of the present application provides a computer program product, including a computer program or instructions. When the computer program or instructions are executed on a computer, the computer executes the RIS-assisted wireless channel estimation method based on OTFS described in the above embodiment.

[0082] The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, the above embodiments may appear in whole or in part in the form of a computer program product, which includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part.

[0083] It should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are only exemplary. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A RIS-assisted wireless channel estimation method based on OTFS, characterized in that Improving the estimation accuracy includes the following steps: S1: Based on the cascade system model, the estimated cascade channels are grouped to reduce the total number of estimated channels; S2: Based on the RIS-assisted wireless channel estimation model in the high-speed mobile scenario, the signal vector transmitted through the cascade channel is obtained, and the signal Y is obtained by compressing and sampling it; S3: Using Y and the sensing matrix, the channel information matrix H is estimated through an improved orthogonal matching algorithm; S4: Use Lagrange interpolation method to upsample the estimated channel matrix and restore it to the complete information matrix H.

2. According to claim 1, the RIS-assisted wireless channel estimation method based on OTFS, wherein the information encoded in the Doppler-delay domain, the pilot design and the channel information estimated in the Doppler-delay domain, etc., include: Encode the data information and pilot code in a single Doppler-delay domain frame, and design a guard interval based on the maximum delay and the maximum Doppler shift; Using low peak-to-average power ratio sequences as pilot signals reduces the gap between the peak power and average power of the signal, thus improving signal transmission efficiency and stability; For millimeter wave channel estimation, the channel state information matrix in the Doppler-delay domain is sparse, and the channel estimation problem can be transformed into a sparse channel information matrix recovery problem.

3. According to claim 1, a greedy algorithm based on orthogonal matching, characterized in that: The two parts of element correlation and step size optimization include: Using the Dice coefficient instead of the traditional similarity calculation can better reflect the characteristics of the core components of the data; The step size is set according to the initialization threshold method to speed up the convergence speed; then the double threshold method is used to dynamically adjust the step size to ensure the final sparse matrix recovery accuracy.

4. A method for grouping RIS reflection units according to claim 1, characterized in that: The number of channels to be estimated is reduced to improve estimation efficiency and reduce the complexity of channel estimation in high-speed mobile scenarios. The method includes: By grouping adjacent reflection elements of RIS, each group contains N0 elements, the number of channels that need to be estimated is The complexity of channel estimation is reduced.

5. According to claim 1, the method for improving the accuracy of the channel information matrix based on Lagrange interpolation is characterized in that: Constructing a basis function according to the position between the reference point and the point to be interpolated, the method further comprises: The method of piecewise interpolation and construction of low-order basis functions is used to reduce the complexity of interpolation and avoid the phenomenon that the interpolation results deviate significantly from the original data.

6. According to claim 1, the NMSE is calculated by using the complete channel information matrix H estimated by upsampling to restore the transmitted data information.

7. A RIS-assisted wireless channel estimation device based on OTFS, characterized in that: include: An acquisition unit obtains a received signal and a local reference signal according to a system model; A processing unit estimates a channel response in the Doppler-delay domain using an improved sparse channel matrix recovery algorithm based on a received pilot signal in the Doppler-delay domain; A processing unit, using the estimated packet channel state information, performs upsampling to restore a complete channel state information matrix; The processing unit recovers the transmitted signal using the channel state information.

8. An electronic device, characterized in that: include: A processor and a memory, the memory being coupled to the processor, the memory being used to store computer program codes, the computer program codes comprising computer instructions, the processor reading the computer instructions from the memory so that the electronic device executes a RIS-assisted wireless channel estimation method based on OTFS as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The method comprises computer instructions, and when the computer instructions are executed on a terminal, the terminal is enabled to execute a RIS-assisted wireless channel estimation method based on OTFS as claimed in any one of claims 1 to 6.

10. A computer program product, characterized in that When the computer program product is run on a computer, the computer is enabled to execute the RIS-assisted wireless channel estimation method based on OTFS according to any one of claims 1 to 6.

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