Information processing method and device, equipment, storage medium and computer program product

Abstract

The invention provides an information processing method and device, equipment, a storage medium and a computer program product, and the information processing method comprises the steps: obtaining a signal arrival angle from first equipment to intelligent super-surface RIS equipment and a signal emergence angle from the RIS equipment to second equipment; the signal arrival angle comprises a first azimuth angle and a first pitch angle; the signal emergence angle comprises a second azimuth angle and a second pitch angle; determining a phase regulation matrix of the RIS equipment according to the signal arrival angle and the signal emergence angle; determining a time delay matrix and a Doppler matrix according to the phase regulation matrix; and determining the distance between the first device and the second device and the speed of the first device relative to the second device according to the time delay matrix and the Doppler matrix. The scheme can support and solve the problem of low processing precision of a scheme for processing a common-inductance integrated signal in the prior art.

Description

Technical Field

[0001] The present application relates to the technical field of communication perception integration, and in particular to an information processing method, device, equipment, storage medium and computer program product. Background Art

[0002] As the core research direction of future wireless communication systems, communication and perception integration can integrate sensing and communication functions, significantly improve the system's working efficiency, reduce hardware costs, and achieve efficient use of spectrum resources. However, the signal processing of the synaesthesia integrated system not only needs to ensure the quality of signal transmission, but also needs to obtain environmental information from the received signal. Among them, the mainstream frequency bands (such as millimeter waves and terahertz high-frequency bands) in the ISAC (communication and perception integration) system are highly sensitive to environmental conditions, and signal propagation is largely affected by the spatial characteristics of the system; in the perception process, the presence of obstacles in complex and changeable scenes leads to multipath effects and signal shielding during propagation, and will experience significant channel fading; the above two factors affect the resolution of the signal in time, space and angle, and ultimately present a decrease in perception accuracy. In this regard, the existing technology provides an optimization method, but the spatial design scheme for synaesthesia integrated signal processing based on intelligent metasurfaces is often limited to constructing a channel model in a two-dimensional plane to perform signal processing tasks, which leads to the neglect of signals in other dimensions and reduces the accuracy of related schemes.

[0003] As described above, the solutions for synaesthesia integrated signal processing in the prior art have problems such as low processing accuracy. Summary of the invention

[0004] The purpose of the present application is to provide an information processing method, device, equipment, storage medium and computer program product to solve the problem of low processing accuracy in the prior art solutions for synaesthesia integrated signal processing.

[0005] In order to solve the above technical problems, the present application provides an information processing method, including:

[0006] Acquire a signal arrival angle from the first device to the smart metasurface RIS device and a signal exit angle from the RIS device to the second device; the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal exit angle includes: a second azimuth angle and a second elevation angle;

[0007] Determining a phase control matrix of the RIS device according to the signal arrival angle and the signal departure angle;

[0008] Determining a delay matrix and a Doppler matrix according to the phase control matrix;

[0009] The distance between the first device and the second device and the speed of the first device relative to the second device are determined according to the delay matrix and the Doppler matrix.

[0010] Optionally, determining the phase control matrix of the RIS device according to the signal arrival angle and the signal departure angle includes:

[0011] Determining a response steering vector of the RIS device according to the signal arrival angle and the signal departure angle;

[0012] A phase control matrix of the RIS device is determined according to the response steering vector.

[0013] Optionally, determining a delay matrix and a Doppler matrix according to the phase control matrix includes:

[0014] Determining a channel information matrix according to the phase control matrix;

[0015] A delay matrix and a Doppler matrix are determined according to the channel information matrix.

[0016] Optionally, determining the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix and the Doppler matrix includes:

[0017] Obtain spectrum occupancy sequence;

[0018] Determining a channel matrix according to the spectrum occupancy sequence;

[0019] The compressed sensing (CS) algorithm is used to determine the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis.

[0020] Optionally, determining the distance between the first device and the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix, and the sparse basis includes:

[0021] Obtaining a range spectrum vector of the first device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis;

[0022] Determining a first peak index value according to the distance spectrum vector;

[0023] A distance between the first device and the second device is determined according to the first peak index value.

[0024] Optionally, determining the speed of the first device relative to the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix, and the sparse basis includes:

[0025] Obtaining a velocity spectrum vector of the first device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis;

[0026] Determine a second peak index value according to the velocity spectrum vector;

[0027] A speed of the first device relative to the second device is determined according to the second peak index value.

[0028] The present application also provides an information processing device, including:

[0029] A first acquisition module is used to acquire a signal arrival angle from the first device to the smart metasurface RIS device and a signal exit angle from the RIS device to the second device; the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal exit angle includes: a second azimuth angle and a second elevation angle;

[0030] A first determination module is used to determine the phase control matrix of the RIS device according to the signal arrival angle and the signal emission angle;

[0031] A second determination module is used to determine a delay matrix and a Doppler matrix according to the phase control matrix;

[0032] The third determination module is used to determine the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix and the Doppler matrix.

[0033] Optionally, determining the phase control matrix of the RIS device according to the signal arrival angle and the signal departure angle includes:

[0034] Determining a response steering vector of the RIS device according to the signal arrival angle and the signal departure angle;

[0035] A phase control matrix of the RIS device is determined according to the response steering vector.

[0036] Optionally, determining a delay matrix and a Doppler matrix according to the phase control matrix includes:

[0037] Determining a channel information matrix according to the phase control matrix;

[0038] A delay matrix and a Doppler matrix are determined according to the channel information matrix.

[0039] Optionally, determining the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix and the Doppler matrix includes:

[0040] Obtain spectrum occupancy sequence;

[0041] Determining a channel matrix according to the spectrum occupancy sequence;

[0042] The compressed sensing (CS) algorithm is used to determine the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis.

[0043] Optionally, determining the distance between the first device and the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix, and the sparse basis includes:

[0044] Obtaining a range spectrum vector of the first device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis;

[0045] Determining a first peak index value according to the distance spectrum vector;

[0046] A distance between the first device and the second device is determined according to the first peak index value.

[0047] Optionally, determining the speed of the first device relative to the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix, and the sparse basis includes:

[0048] Obtaining a velocity spectrum vector of the first device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis;

[0049] Determine a second peak index value according to the velocity spectrum vector;

[0050] A speed of the first device relative to the second device is determined according to the second peak index value.

[0051] The embodiment of the present application also provides an information processing device, including: a processor;

[0052] The processor is used to obtain a signal arrival angle from the first device to the smart metasurface RIS device and a signal exit angle from the RIS device to the second device; the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal exit angle includes: a second azimuth angle and a second elevation angle;

[0053] Determining a phase control matrix of the RIS device according to the signal arrival angle and the signal departure angle;

[0054] Determining a delay matrix and a Doppler matrix according to the phase control matrix;

[0055] The distance between the first device and the second device and the speed of the first device relative to the second device are determined according to the delay matrix and the Doppler matrix.

[0056] Optionally, determining the phase control matrix of the RIS device according to the signal arrival angle and the signal departure angle includes:

[0057] Determining a response steering vector of the RIS device according to the signal arrival angle and the signal departure angle;

[0058] A phase control matrix of the RIS device is determined according to the response steering vector.

[0059] Optionally, determining a delay matrix and a Doppler matrix according to the phase control matrix includes:

[0060] Determining a channel information matrix according to the phase control matrix;

[0061] A delay matrix and a Doppler matrix are determined according to the channel information matrix.

[0062] Optionally, determining the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix and the Doppler matrix includes:

[0063] Obtain spectrum occupancy sequence;

[0064] Determining a channel matrix according to the spectrum occupancy sequence;

[0065] The compressed sensing (CS) algorithm is used to determine the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis.

[0066] Optionally, determining the distance between the first device and the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix, and the sparse basis includes:

[0067] Obtaining a range spectrum vector of the first device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis;

[0068] Determining a first peak index value according to the distance spectrum vector;

[0069] A distance between the first device and the second device is determined according to the first peak index value.

[0070] Optionally, determining the speed of the first device relative to the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix, and the sparse basis includes:

[0071] Obtaining a velocity spectrum vector of the first device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis;

[0072] Determine a second peak index value according to the velocity spectrum vector;

[0073] A speed of the first device relative to the second device is determined according to the second peak index value.

[0074] An embodiment of the present application also provides an information processing device, including a memory, a processor, and a program stored in the memory and executable on the processor; when the processor executes the program, the above-mentioned information processing method is implemented.

[0075] An embodiment of the present application also provides a readable storage medium on which a program is stored, and when the program is executed by a processor, the steps in the above-mentioned information processing method are implemented.

[0076] An embodiment of the present application also provides a computer program product, including computer instructions, which implement the steps of the above-mentioned information processing method when executed by a processor.

[0077] The beneficial effects of the above technical solution of the present application are as follows:

[0078] In the above scheme, the information processing method obtains the signal arrival angle from the first device to the smart metasurface RIS device and the signal emission angle from the RIS device to the second device; the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal emission angle includes: a second azimuth angle and a second elevation angle; according to the signal arrival angle and the signal emission angle, the phase control matrix of the RIS device is determined; according to the phase control matrix, the delay matrix and the Doppler matrix are determined; according to the delay matrix and the Doppler matrix, the distance between the first device and the second device and the speed of the first device relative to the second device are determined; it can support the implementation of enhancing the viewing angle coverage of RIS by introducing a vertical dimension in space, realizing a three-dimensional space solution for synaesthesia integrated signal processing based on RIS, increasing the dimensional consideration of the signal, and improving the processing accuracy of the solution, which well solves the problem of low processing accuracy in the prior art for synaesthesia integrated signal processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a flowchart of the information processing method according to an embodiment of the present application;

[0080] Figure 2 This is a schematic diagram of the RIS structure of an embodiment of the present application;

[0081] Figure 3 This is a schematic diagram of the incident angle of the RIS surface according to an embodiment of the present application;

[0082] Figure 4 A schematic diagram of the cascade channels of the RIS according to an embodiment of the present application;

[0083] Figure 5 This is a schematic diagram of the structure of an information processing device according to an embodiment of the present application;

[0084] Figure 6 This is a schematic diagram of the structure of an information processing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0085] In order to make the technical problems, technical solutions and advantages to be solved by the present application clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0086] The following first introduces the relevant contents of this plan.

[0087] Most of the current solutions to improve resolution degradation start from two perspectives: waveform design and algorithm processing. Different waveform designs are used to optimize modulation to achieve resolution improvement, and different radar signal processing algorithms optimize system index parameters. Specifically, they are introduced as follows:

[0088] (1) Current spatial design scheme for synaesthesia integrated signal processing based on smart metasurfaces;

[0089] The analysis of the spatial dimension of the ISAC signal processing solution based on RIS is currently mainly focused on the perspective of the two-dimensional plane, focusing on the geometric relationship between the spatial position of the transceiver and the RIS and the simple description of its correlation. Among them, when discussing the signal processing based on RIS, the signal propagation path and plane angle relationship of the base station-RIS-target are analyzed from the two-dimensional plane. Although this two-dimensional analysis method can intuitively show the basic structure of the system layout and intuitively show the plane distance between the base station, RIS and the target, the signal propagation path in the vertical direction is ignored.

[0090] Specifically, the spatial design scheme of synaesthesia integrated signal processing based on intelligent metasurfaces is often limited to constructing channel models in a two-dimensional plane to perform signal processing tasks. However, although the channel modeling based on a two-dimensional plane can capture the azimuth information of the signal, it also limits the ability of RIS (intelligent metasurface) to dynamically change the angle of arrival (AOA) and the angle of departure (AOD), and ignores the impact of the vertical dimension on signal propagation. Especially in complex and changing scenes, the undulating terrain and the occlusion caused by obstacles directly cause the channel to suffer different degrees of attenuation in the vertical dimension.

[0091] (2) Current synaesthesia integrated signal processing algorithm based on smart metasurface;

[0092] Reducing noise interference The NLOS path signal of the ISAC system is weak and highly dependent on the environment. Especially when the LOS path is blocked by obstacles, the channel fading problem will be aggravated, resulting in a significant increase in the signal coverage blind area and a decrease in the spatial resolution and time resolution of the system. At the same time, the signal strength and direction received by the antenna array are also affected, resulting in a decrease in the beamforming performance, which ultimately manifests as a decrease in the angular resolution of the system. The above factors work together to reduce the perception accuracy of the system. In response to this, the current signal processing algorithms based on intelligent metasurfaces have been optimized. Among them, most of the signal processing algorithms that improve resolution focus on RIS phase control and RIS structure, and adjust the phase by correcting the unit structure of RIS itself to achieve signal processing; however, this method requires the design of the structure of RIS itself, without changing it at the channel level, so the sparsity of the signal in the NLOS path is often ignored.

[0093] Based on the above, the present application provides an information processing method to solve the problem of low processing accuracy in the existing technology for synaesthesia integrated signal processing, such as Figure 1 As shown, including:

[0094] Step 11: Obtain a signal arrival angle from a first device (such as a terminal UE) to a smart metasurface RIS device and a signal emission angle from the RIS device to a second device (such as a base station); the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal emission angle includes: a second azimuth angle and a second elevation angle;

[0095] Step 12: Determine a phase control matrix of the RIS device according to the signal arrival angle and the signal exit angle;

[0096] Step 13: Determine a delay matrix and a Doppler matrix according to the phase control matrix;

[0097] Step 14: Determine the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix and the Doppler matrix.

[0098] The information processing method provided in the embodiment of the present application obtains the signal arrival angle from the first device to the smart metasurface RIS device and the signal emission angle from the RIS device to the second device; the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal emission angle includes: a second azimuth angle and a second elevation angle; according to the signal arrival angle and the signal emission angle, the phase control matrix of the RIS device is determined; according to the phase control matrix, the delay matrix and the Doppler matrix are determined; according to the delay matrix and the Doppler matrix, the distance between the first device and the second device and the speed of the first device relative to the second device are determined; it can support the implementation of enhancing the viewing angle coverage of RIS by introducing a vertical dimension in space, realizing a three-dimensional space solution for synaesthesia integrated signal processing based on RIS, increasing the dimensional consideration of the signal, and improving the processing accuracy of the solution, which well solves the problem of low processing accuracy of the solution for synaesthesia integrated signal processing in the prior art.

[0099] Wherein, determining the phase control matrix of the RIS device according to the signal arrival angle and the signal exit angle includes: determining the response steering vector of the RIS device according to the signal arrival angle and the signal exit angle; and determining the phase control matrix of the RIS device according to the response steering vector. In this way, the phase control matrix can be accurately obtained.

[0100] In the embodiment of the present application, the determining of the delay matrix and the Doppler matrix according to the phase control matrix includes: determining the channel information matrix according to the phase control matrix; and determining the delay matrix and the Doppler matrix according to the channel information matrix. In this way, the delay matrix and the Doppler matrix can be accurately obtained.

[0101] Wherein, the distance between the first device and the second device and the speed of the first device relative to the second device are determined according to the delay matrix and the Doppler matrix, including: obtaining a spectrum occupancy sequence; determining a channel matrix according to the spectrum occupancy sequence; using a compressed sensing CS algorithm, according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis, to determine the distance between the first device and the second device and the speed of the first device relative to the second device. In this way, the distance and speed can be accurately obtained, which supports solving the problem of low perception accuracy caused by signal sparsity. Wherein, the elements in the spectrum occupancy sequence can be marked as a first value (such as 0) when a subcarrier is unavailable in a specific symbol period; conversely, if it is available, it can be marked as a second value (such as 1), but it is not limited to this.

[0102] In an embodiment of the present application, the distance between the first device and the second device is determined according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis, including: obtaining the distance spectrum vector of the first device (for example, as a first sparse signal) according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix (such as column vector) and sparse basis; determining a first peak index value according to the distance spectrum vector; determining the distance between the first device and the second device according to the first peak index value. In this way, the distance can be accurately obtained. Among them, "obtaining the distance spectrum vector of the first device according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis" may include: using the fast iterative threshold shrinkage algorithm FISTA, according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis, (iterative recovery) to obtain the distance spectrum vector of the first device, but is not limited to this.

[0103] Wherein, determining the speed of the first device relative to the second device according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis includes: obtaining the speed spectrum vector of the first device (for example, as a second sparse signal) according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix (such as row vector) and sparse basis; determining the second peak index value according to the speed spectrum vector; and determining the speed of the first device relative to the second device according to the second peak index value. In this way, the speed can be accurately obtained. Wherein, "obtaining the speed spectrum vector of the first device according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis" may include: using FISTA to obtain the speed spectrum vector of the first device according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis (iterative recovery), but is not limited thereto.

[0104] The information processing method provided in the embodiment of the present application is illustrated below by taking an example of a target (i.e., a perception target, such as a terminal UE) as the first device and a base station as the second device.

[0105] In response to the above technical problems, the embodiments of the present application provide an information processing method, which can be specifically implemented as a synaesthesia integrated signal processing solution design based on an intelligent super surface. Specifically, in response to the problems of spatial characteristics, obstructed LOS (line-of-sight link) paths, and limited NLOS (non-line-of-sight link) paths, the embodiments of the present application propose a three-dimensional space design scheme for synaesthesia integrated signal processing based on an intelligent super surface, which not only establishes a channel model based on RIS on the basis of a three-dimensional space structure, but also designs a super-resolution synaesthesia integrated signal processing algorithm scheme based on an intelligent super surface to obtain channel information from sparse signals; ultimately, it alleviates the blind spot coverage caused by obstructions in the environment and achieves high-precision perception. The channel model adopts a cascade channel model to realize the cascade channel of base station-RIS (i.e., base station to RIS) and RIS-target (i.e., RIS to target). The following is a specific example introduction to the contents involved in this scheme.

[0106] The design of the integrated signal processing scheme based on the intelligent metasurface proposed in the embodiment of the present application may include a RIS structure model module and a signal processing model module, wherein the RIS structure model module can implement the channel model, and the signal processing model module can implement the signal processing (algorithm). The signal processing algorithm can be based on compressed sensing technology to convert the sparse signal matrix propagating in the channel into a compact signal matrix (see the following formula (9)), and finally measure the distance and speed of the target. The two model modules involved in the scheme are introduced as follows:

[0107] 1. Signal processing model (module);

[0108] 1. Waveform design;

[0109] Due to the collaborative sensing and communication functions of the ISAC system, the signal needs to meet the coexistence and waveform multiplexing mechanism. As the mainstream signal of ISAC, OFDM has attracted much attention for its advantages of anti-multipath interference and high spectrum efficiency.

[0110] In this scheme, the base station transmit signal x(t) can be expressed in the form of OFDM signal expression: OFDM , as shown in formula (1):

[0111]

[0112] Among them, M sym Represents the total number of symbols of the OFDM signal, m represents the symbol index of the OFDM signal, N C Indicates the total number of subcarrier numbers available in a symbol in spectrum sensing mode, n indicates the subcarrier number index available in a symbol in spectrum sensing mode, and f n represents the frequency of the nth subcarrier, f n=nΔf, Δf represents the subcarrier spacing, j represents the imaginary unit, t represents the time variable, T sym Indicates the duration of an OFDM symbol, d m,n Represents the modulation symbol on the nth subcarrier of the mth OFDM symbol.

[0113] In addition, in the 2D-FFT (two-dimensional fast Fourier transform) algorithm, the distance and speed information comes from the influence of delay and Doppler effect on the signal. Assuming that the distance between the target and the base station is R, the signal will have a delay during the round-trip propagation process; the relative motion between the target and the transceiver causes the signal to be affected by the Doppler effect during the reception process, which will cause frequency offset; Based on this, the received signal y(t) OFDM It can be expressed as the following formula (2):

[0114]

[0115] Among them, α m,n represents the channel gain of the nth subcarrier of the mth OFDM symbol, τ Represents the time delay, and τ=2R / c, c represents the speed of light. Further, according to the time domain and frequency domain offsets caused by the time delay and Doppler on the OFDM symbol information matrix, this scheme can express formula (2) in the form of the following formula (3) to separate the time delay and Doppler (Doppler corresponds to the first part of the summation term in the following formula (3)):

[0116]

[0117] Among them, f D represents the Doppler frequency shift, and f D =2v0f c / c, v0 represents the relative speed between the target and the transmitter and receiver, f c Indicates the frequency of the carrier (of the OFDM signal).

[0118] In addition, in the OFDM classic architecture, the channel state is obtained by sending known pilot signals and detecting the changes of these signals during channel transmission; however, in the ISAC system, since obstacles hinder signal propagation, the acquisition of channel information is more dynamic to cope with the sparsity of the received signal. In this scheme, it is assumed that OFDM technology can dynamically perceive the spectrum. Based on this, when the signal is sparse, sequence annotation A can be introduced. m (which may correspond to the above-mentioned spectrum occupancy sequence) to characterize the signal fading situation; wherein, represents a real number set, M represents the total number of symbols of the OFDM signal (i.e., M sym), m represents the mth OFDM symbol; if a subcarrier is unavailable in a specific symbol period, the corresponding a can be marked n,m =0 (n is greater than or equal to 0, and less than or equal to NC-1 ); otherwise, if available, it can be marked as 1. At the receiving end, the spectrum occupancy sequence A m It can be used to demodulate user data symbols (such as reconstructing and recovering sparse signals) and construct a channel information matrix to clarify the information lost during signal propagation due to obstacle shielding and fading.

[0119] 2. Algorithm model;

[0120] The traditional 2D-FFT algorithm has the problems of poor noise resistance and low Fourier sidelobe. The 2D-FFT algorithm based on compressed sensing technology can effectively solve this problem. Specifically, the CS (compressed sensing) process can be divided into three main steps: sparse representation (see the following formulas (4)-(6)), measurement process (see the following formulas (7)-(9)), and signal recovery (see the following implementation using FISTA). For a sparse signal, the signal can be linearly mapped into a low-dimensional vector (such as the following y) by applying the measurement matrix. m,oc ); then solve the sparse optimal problem (see the relevant content of FISTA iteration below), so that the original signal can be restored with high accuracy under the condition of a small amount of under-sampled information. The following is an example of the 2D-FFT algorithm based on compressed sensing technology:

[0121] (1) Distance estimation algorithm;

[0122] Since the NLOS path signal propagation is limited, this scheme sets some positions in the channel matrix to zero values. Here, the zero value of the matrix row vector corresponds to the spectrum occupancy sequence A m The zero value in (which may correspond to the determination of the channel matrix according to the spectrum occupancy sequence) can support avoiding the situation where the calculation cannot be performed due to the denominator of the corresponding element of the modulated signal at the transmitting end being zero. Specifically, the channel matrix D (which may be a known quantity) can be shown in the following formula (4):

[0123]

[0124] Here D represents the set matrix of channel elements d obtained by dividing the received signal and the modulated signal at the transmitting end, d(n,m) represents the channel coefficient of the nth subcarrier of the mth OFDM symbol (in formula (4), k represents the kth subcarrier, n is greater than or equal to 0 and less than or equal to N c , m is greater than or equal to 0 and less than or equal to M sym), in which the signal is severely attenuated, the channel coefficient can be 0. Since the linear displacement of the distance is only along the frequency axis, D can be expressed as a column vector as shown in the following formula (5): Among them, y m The mth column of the set matrix representing the channel element d is the channel coefficient.

[0125]

[0126] in,

[0127] The OFDM symbol channel matrix (i.e., y OFDM , corresponding to y(t) in the above formula (3) OFDM , which can be a known quantity) and the spectrum occupancy sequence A m Perform Hadamard product operation and get y m It represents the OFDM matrix required by this scheme, and represents the OFDM signal existing when the channel is occupied, as the original matrix.

[0128] Next, the target’s range spectrum vector P can be obtained according to the following formula (7): m (For the overall received signal):

[0129] Ψy OFDM =P m (7)

[0130] Where, Ψ represents a sparse basis (which can be a known quantity), is a discrete inverse Fourier matrix; represents a set of complex numbers; because the OFDM vector (i.e. y OFDM ) needs to be operated through IDFT (inverse discrete Fourier transform) to obtain the distance spectrum vector P m , so we can combine formula (6) and formula (7) to get the following formula (8):

[0131]

[0132] because With A m Perform Hadamard product operation, so A m Expanded to N C ×N C In order to obtain the peak index value of the target's distance spectrum (vector), the compressed sensing technology needs to use the screening matrix S ( represents a complex number set, N occupied Indicates the number of subcarriers occupied by the channel, which can reflect the sparsity) and determine the occupied subcarrier position; specifically, y m,oc =Sym and Formula (8) is expressed as the following formula (9):

[0133]

[0134] Among them, y m,oc Represents y m The matrix after screening. In this scheme, the sparsity of the signal can be used to estimate the distance and speed of the target through compressed sensing technology; in many radar systems, especially in high-resolution radar systems, the target appears as a sparsely distributed peak on the range-velocity profile; this is because although there may be a large number of potential range-velocity cells, only a few cells actually contain the reflected target signal. Among them, the cell refers to the cell of the range-velocity profile, and each cell corresponds to a specific combination of distance and speed.

[0135] The above formula (9) is obtained based on the principle of compressed sensing technology. The sparse signal P m (i.e., the range spectrum vector) shows the sparsity of the range-velocity profile, which can be subsequently obtained by y m,oc Reconstruct the recovery matrix to obtain the sparse signal P m Among them, the perception matrix The distance structure of the original signal is maintained, which can better ensure the accuracy of signal reconstruction.

[0136] However, in the actual scene, there is noise, and the problem becomes to reconstruct the sparse signal from the noise measurement value; for this, this solution adopts the fast iterative shrinkage-thresholding algorithm (FISTA) to solve it. Among them, FISTA is an optimization algorithm that uses an iterative method to solve the problem of sparse signal recovery. It is particularly suitable for large-scale problems because it has a faster convergence speed than the traditional iterative threshold shrinkage algorithm (ISTA). Based on this, in the embodiment of the present application, the distance spectrum vector P can be reconstructed by the FISTA algorithm. m Make an estimate.

[0137] Among them, CS uses FISTA fast iteration to recover the distance spectrum vector P according to formula (9) m(which may correspond to obtaining the distance spectrum vector of the first device according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis), and then obtaining the peak index value k0 (which may correspond to determining the first peak index value according to the distance spectrum vector), and further obtaining the distance R of the target by using the following formula (10) (which may correspond to determining the distance between the first device and the second device according to the first peak index value):

[0138]

[0139] (2) Velocity estimation algorithm;

[0140] From the 2D-FFT algorithm, it can be determined that the target distance estimation and target speed estimation algorithms can be performed separately and ideally do not affect each other, as shown in the following formulas (11) and (12):

[0141]

[0142] Among them, D R (n) represents the frequency domain vector of the target distance dimension, D V (l) represents the frequency domain vector of the target velocity dimension; l represents the index of the OFDM symbol after compressed sensing.

[0143] Considering that: because the delay effect is along the frequency axis, and the Doppler effect is a linear time shift on the same subcarrier, the channel information matrix D div (i.e. D in the above formula (4)) Take its row vector to get the row vector matrix (Similar to the above formula (9), the compressed matrix is ​​obtained, that is, converted into a compact matrix), the row vector includes channel information and noise, y Nc Indicates the total number of subcarriers in the non-sparse part. In addition, the traditional 2D-FFT algorithm cannot eliminate the noise of the row vector, so the Fourier sidelobe is seriously deteriorated. Therefore, this scheme gives the following formula (13) to achieve denoising using the improved 2D-FFT algorithm and reduce the peak sidelobe ratio.

[0144]

[0145] Where z represents the index in the discrete velocity domain, z = 0, ..., M sym -1; Formula (13) for D v (m) was subjected to a discrete Fourier transform (to achieve M sym The transformation of the time domain signal into the velocity domain) can obtain the matrix v(z) containing the velocity information (velocity spectrum vector v0). In formula (13), except v0, other parameters can be known. In addition, because D v(m) If there is Gaussian white noise w(m), the transformed v(z) is still interfered by Gaussian white noise, which can be shown in the following formula (14). The peak sidelobe ratio can be used to intuitively feel the interference of noise on the signal:

[0146]

[0147] In the above formula (14), Q is the Fourier sidelobe height, MAX is the maximum peak amplitude of v(z), and the mean value of w(m) after Fourier transformation is It can be seen that the greater the noise, the smaller the sidelobe ratio (i.e., the ratio obtained on the left side of formula (14)). When noise exists, a model formula as shown in formula (15) can be established to limit it:

[0148] min||P n ||1st ||ψP n -y n ||2≤w n (15)

[0149] The original signal in formula (15) is y n (corresponding to the above D v (m)),||P n ||1 represents the sparse signal (of speed) P n The l1 norm of P n As sparse as possible, that is, most elements are zero or close to zero; w n is an error tolerance, indicating that a certain error is allowed between the restored signal and the observed signal. Under certain constraints, the l1 norm minimization problem of the signal and the l0 norm minimization problem can be considered equivalent. This equivalence allows the original non-convex optimization problem to be transformed into a convex optimization problem for solution. Formula (15) means that if ||ψP n -y n ||2≤w n , then we can execute min||P n ||1, that is, the optimal solution, ensures that the reconstruction result is more in line with sparsity; it can also be understood as achieving equivalent conditions, the signal is sparse enough, satisfies mutual incoherence (OFDM characteristics), and the noise error has an upper limit w n . Among them, || ||2 is the representation of the l2 norm.

[0150] In this solution, the FISTA algorithm can be used to estimate the speed according to formula (13) to obtain a speed spectrum vector V (which can correspond to the speed spectrum vector of the first device obtained according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis); then, according to the speed spectrum vector V, the peak index is determined (which can correspond to the second peak index value determined according to the speed spectrum vector); according to the peak index, the relative speed between the target and the transceiver can be obtained (which can correspond to the speed of the first device relative to the second device determined according to the second peak index value). When the transceiver is stationary, the target speed V can be as shown in the following formula (16):

[0151]

[0152] Where z represents the index in the discrete velocity domain, and its value range is z=0,…,M sym -1. It is noted here that the meanings of symbols not otherwise explained in this scheme can be referred to the meanings explained for the same symbols, and the repeated parts will not be repeated.

[0153] 2. ISAC three-dimensional channel model based on RIS (corresponding to the above RIS structural model module);

[0154] 1. RIS phase control model;

[0155] like Figure 2 and Figure 3 As shown, RIS mainly includes a plane surface and an intelligent controller; wherein the intelligent controller includes a large number of small controllable unit elements, the switch state of the PIN diode embedded in the element corresponds to the phase 0 and 180°, and each element can independently adjust the phase and amplitude of the incident electromagnetic wave to achieve signal amplitude and phase compensation. In three-dimensional space, after RIS adjusts the phase of the incident signal, it can obtain the azimuth and elevation angle of the outgoing signal, and determine the specific direction of signal propagation in the two-dimensional plane and vertical dimension (which can correspond to the above-mentioned acquisition of the signal arrival angle from the first device to the intelligent metasurface RIS device and the signal exit angle from the RIS device to the second device; the signal arrival angle includes: the first azimuth angle and the first elevation angle; the signal exit angle includes: the second azimuth angle and the second elevation angle).

[0156] Among them, the incident and emission angles of the RIS surface signal can be analyzed first, such as Figure 2 As shown in (RIS structure diagram), RIS is equipped with a uniform planar array, and an xy coordinate system is established on the RIS surface; N y and N x Respectively represent the number of (array) units in the vertical and horizontal directions, and the total number of RIS elements is N = N x ×N y , where the distance between the horizontal coordinates of the elements is dx , the distance between the vertical coordinates of the components is d y , adjacent RIS elements are spaced d apart in the x and y directions x and d y ; Assuming that the RIS components are closely spaced, the nth x +(n y -1)×N x The position of the components (component numbering is from left to right) can be roughly expressed as (d x ×(n x -1),d y ×(n y -1)),n x ∈N x ,n y ∈N y .

[0157] In the embodiment of the present application, the angle of arrival (AoA) from the user (UE; i.e., the target) to the RIS and the angle of departure (By RIS) to the base station (BS) are set to θ and like Figure 3 As shown in (Schematic diagram of incident angle on RIS surface), the angle includes azimuth angle (located in the plane where the x-axis and z-axis are located; corresponding to the mark az) and elevation angle (located in the plane where the x-axis and y-axis are located; corresponding to the mark el). Specifically, the incident angle θ = (θ el ,θ az ), the exit angle Based on this, the two response steering vectors of RIS can be expressed as the following formulas (17) and (18):

[0158]

[0159]

[0160] Among them, d x represents the distance between the horizontal coordinates of the RIS elements, d y represents the distance between the ordinates of the RIS elements, λ is the carrier wavelength (of the OFDM signal), and the reflection coefficient matrix of the RIS is expressed as S(γ)=diag(γ1,γ2,…,γ N ),and γ n1 is the phase shift of the n1th RIS element, n1∈{1,2,…,N}, represents a complex number set. The RIS phase control matrix G can be obtained according to the following formula (19): M is the above M sym ):

[0161]

[0162] Where n and m correspond to the nth subcarrier and mth OFDM symbol of SC-OFDM (OFDMin sparse channels) in the system channel model (i.e., ISAC system channel model based on RIS), [G] n,m Refers to its corresponding phase control matrix.

[0163] The above formulas (17) and (18) may correspond to the above determination of the response steering vector of the RIS device according to the signal arrival angle and the signal departure angle; the above formula (19) may correspond to the above determination of the phase control matrix of the RIS device according to the response steering vector.

[0164] 2. ISAC system channel model based on RIS;

[0165] This scheme assumes a RIS-assisted 3D ISAC system, including a single-antenna base station, a RIS, and a single-antenna user's single-input single-output (SISO) system. In the original sensing process, the signal is transmitted in the line-of-sight link between the base station and the user. When the line-of-sight link is blocked, the signal is transmitted from the base station, reflected by the RIS, and reaches the target. The target reflected echo is then reflected by the RIS and finally received by the base station.

[0166] This solution only considers the NLOS link after RIS is added. The channel consists of four cascaded channels: BS-RIS, RIS-UE, UE-RIS and RIS-BS. Because the path loss, delay and Doppler shift from base station to user and from user to base station are basically the same, only uplink communication is considered as an example. Figure 4 (Cascade channel of RIS), channel H from UE to RIS ur and H from RIS to BS rb Channel cascading.

[0167] Consider a SC-OFDM transmission system, assuming that the pilot signal is 1 and the number of SC-OFDM symbols is M sym , the number of subcarriers in each SC-OFDM symbol is N C In the uplink channel, each base station receives the signal from the RIS reflection path NLOS and receives the signal (M is the above M sym ) can be expressed as the following formula (20):

[0168]

[0169] Here h m,n For the m The channel gain of the nth subcarrier of the SC-OFDM symbol. In this scheme, the channel matrix D is obtained by element-by-element division of the matrix of the transmitting and receiving ends. div ; Integrated cascade channel gain g ur (channel gain between user and RIS) and g r b (channel gain between RIS and base station), and combined with: Formula (17) and Formula (18), the departure angle AoD between the base station and RIS and the arrival angle AoA between RIS and the user are respectively expressed as and θ, the time delay is expressed as τ = (R ur +R rb ) / c(c represents the speed of light, R ur Represents the distance between the user and the RIS, R rb represents the distance between RIS and the base station) and the Doppler frequency shift f D Represented as f D =v0f c / c(v0 is the relative speed between the target and the transmitter and receiver, f c represents the frequency of the carrier), and because the pilot can be set to 1, the row vector of 1×L all 1s is represented as 1 L , based on which the channel information matrix can be expressed as shown in the following formula (21) (corresponding to the above-mentioned determination of the channel information matrix according to the phase control matrix):

[0170]

[0171] Delay τ = τ ur +τ rb , the delay matrix D(τ) and the Doppler matrix D(f D ) can be expressed as shown in the following formulas (22) and (23) (corresponding to the above-mentioned determination of the delay matrix and the Doppler matrix according to the channel information matrix):

[0172]

[0173] Among them, the cascaded channels at both ends and the converted channels (M is the above M sym ), delay matrix (M is the above M sym ), Doppler matrix (M is the above M sym ); Substitute the delay matrix and Doppler matrix into the above formula (7) as P m Participate in the operation. The channel matrices of the two cascaded channels can be expressed as shown in the following formulas (24) and (25):

[0174]

[0175] Among them, H ur The channel matrix H corresponding to the channel between the user and RIS rb A channel matrix may correspond to channels between the RIS and the base station.

[0176] For the convenience of calculation, the delay matrix and Doppler matrix can be expressed as The form is as shown in the following formula (26):

[0177]

[0178] in, and It can be expressed as shown in the following formulas (27) and (28):

[0179]

[0180] In one signal observation cycle, the distance and speed changes of the target can be ignored, and the distance parameter R in the delay (including the distance R between the user and the RIS) ur and the distance R between RIS and the base station rb ) and the velocity parameter v0 in Doppler exist as constants, and the reflection phased matrix G contains the AOD and AOA information of RIS; therefore, the system distance and velocity information (i.e., the distance and relative velocity between the user and the base station) can be estimated by using the improved compressed sensing algorithm in the distance-velocity spectrum diagram based on G.

[0181] Specifically, during the CS processing, the FISTA algorithm can be used to quickly iterate and restore the range spectrum P m , to obtain the peak index value k0 and the distance from the target to RIS, as shown in the following formula (29):

[0182]

[0183] Among them, R ur Represents the distance between the user and the RIS, R rb represents the distance between RIS and the base station; Formula (29) is the specific operation of Formula (10) applied to the RIS scenario;

[0184] Similarly, the velocity estimation algorithm is also based on the CS reconstructed signal, and the Doppler frequency shift in the uplink channel is the overall f' D =v0f c / c, based on which the speed v0 from the target to the RIS can be obtained, as shown in the following formula (30):

[0185]

[0186] Where i0 represents the index value, i0 is an integer and i0∈[0,M sym ]; i0 can be 1; Formula (30) is the specific operation of Formula (16) applied to the RIS scenario. It is noted here that the operation symbols involved in the above formulas in this solution It represents the Hadamard product operation. The operator T in the upper right corner of the formula involved represents matrix transposition.

[0187] Based on the above, the solution provided by the embodiment of the present application involves the following contents:

[0188] (1) A three-dimensional spatial design scheme for synaesthesia integrated signal processing based on smart metasurface is proposed. By introducing the vertical dimension in space, the visual coverage of RIS is enhanced. This scheme utilizes the ability of RIS to dynamically adjust amplitude and phase to integrate the richness of three-dimensional space into the channel model, thereby effectively improving the spatial freedom and flexibility of the system and improving the accuracy of signal perception.

[0189] (2) Regarding the synaesthesia integrated signal processing algorithm based on intelligent metasurface, the existing technologies mostly use the structural characteristics of RIS itself to change the phase to make it focus on the same channel direction. This scheme proposes a super-resolution synaesthesia integrated signal processing algorithm based on intelligent metasurface, which uses compressed sensing technology to process signals, extracts multipath information from sparse signals, and combines RIS to reconstruct sparse angle domains, which can effectively reduce LOS path obstruction and NLOS path limitation, and improve the overall perception accuracy through channel sparse reconstruction.

[0190] In summary, this solution has the following advantages:

[0191] 1) Compared with the existing improvement scheme for the limited spatial characteristics of the signal processing model based on the smart metasurface, the design scheme based on the three-dimensional spatial characteristics of the smart metasurface proposed in this scheme can not only effectively utilize the ability of RIS to dynamically adjust the amplitude and phase, but also improve the spatial freedom and enhance the perception accuracy;

[0192] 2) Compared with the existing synaesthesia integrated signal processing algorithm based on intelligent metasurface, the super-resolution synaesthesia integrated signal processing algorithm based on intelligent metasurface proposed in this scheme not only improves the signal strength, but also can effectively reduce the coverage blind area, solve the channel fading problem, improve the spatial and temporal angle resolution, and improve the perception accuracy.

[0193] It should be noted that this solution involves the field of integrated communication and perception, and specifically involves the design of an integrated signal processing solution for synaesthesia based on intelligent metasurfaces. Among them, Integrated Sensing and Communication (ISAC) refers to the collaborative transmission of communication perception, which enhances the possibility of coexistence of communication systems and electronic devices in the same frequency band. Signal processing refers to the analysis and processing of received signals at the receiving node to obtain relevant information of the detected target. The Reconfigurable Intelligence Surface (RIS) is an array of reflective elements that can adjust the amplitude and phase of the incident signal in real time in an electromagnetic environment.

[0194] This solution also involves the field of orthogonal frequency division multiplexing (OFDM) synaesthesia integration of spatial correlation under single input single output (SISO) antenna system. When the line-of-sight (LOS) link is blocked in the environment, the RIS can be used to dynamically adjust the amplitude phase based on the comprehensive consideration of the complex three-dimensional spatial layout between the transmitter, the receiver and the RIS, optimize the signal propagation in the complex environment, and achieve high-precision ranging and speed measurement of the target for the transmitted signal.

[0195] In summary, to address the problem of channel environment deterioration, this solution provides a solution based on intelligent metasurface to improve the system perception performance, taking into account factors such as spatial characteristics, line-of-sight links, and non-line-of-sight links.

[0196] The present application also provides an information processing device, such as Figure 5 As shown, including:

[0197] A first acquisition module 51 is used to acquire a signal arrival angle from the first device to the smart metasurface RIS device and a signal exit angle from the RIS device to the second device; the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal exit angle includes: a second azimuth angle and a second elevation angle;

[0198] A first determination module 52, configured to determine a phase control matrix of the RIS device according to the signal arrival angle and the signal emission angle;

[0199] A second determination module 53, used to determine a delay matrix and a Doppler matrix according to the phase control matrix;

[0200] The third determination module 54 is used to determine the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix and the Doppler matrix.

[0201] The information processing device provided in the embodiment of the present application obtains the signal arrival angle from the first device to the smart metasurface RIS device and the signal emission angle from the RIS device to the second device; the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal emission angle includes: a second azimuth angle and a second elevation angle; according to the signal arrival angle and the signal emission angle, the phase control matrix of the RIS device is determined; according to the phase control matrix, the delay matrix and the Doppler matrix are determined; according to the delay matrix and the Doppler matrix, the distance between the first device and the second device and the speed of the first device relative to the second device are determined; it can support the implementation of enhancing the viewing angle coverage of RIS by introducing a vertical dimension in space, realizing a three-dimensional space solution for synaesthesia integrated signal processing based on RIS, increasing the dimensional consideration of the signal, and improving the processing accuracy of the solution, which well solves the problem of low processing accuracy of the solution for synaesthesia integrated signal processing in the prior art.

[0202] Wherein, determining the phase control matrix of the RIS device according to the signal arrival angle and the signal exit angle includes: determining a response steering vector of the RIS device according to the signal arrival angle and the signal exit angle; and determining the phase control matrix of the RIS device according to the response steering vector.

[0203] In an embodiment of the present application, determining the delay matrix and the Doppler matrix according to the phase control matrix includes: determining the channel information matrix according to the phase control matrix; determining the delay matrix and the Doppler matrix according to the channel information matrix.

[0204] Among them, determining the distance between the first device and the second device and the speed of the first device relative to the second device based on the delay matrix and the Doppler matrix includes: obtaining a spectrum occupancy sequence; determining a channel matrix based on the spectrum occupancy sequence; using a compressed sensing CS algorithm to determine the distance between the first device and the second device and the speed of the first device relative to the second device based on the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis.

[0205] In an embodiment of the present application, determining the distance between the first device and the second device based on the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis includes: obtaining a distance spectrum vector of the first device based on the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis; determining a first peak index value based on the distance spectrum vector; determining the distance between the first device and the second device based on the first peak index value.

[0206] In an embodiment of the present application, the speed of the first device relative to the second device is determined according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis, including: obtaining a speed spectrum vector of the first device according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis; determining a second peak index value according to the speed spectrum vector; and determining the speed of the first device relative to the second device according to the second peak index value.

[0207] Among them, the implementation embodiments of the above-mentioned information processing method are all applicable to the embodiments of the information processing device and can achieve the same technical effects.

[0208] The present application also provides an information processing device, such as Figure 6 As shown, it includes: a processor 61;

[0209] The processor 61 is used to obtain a signal arrival angle from the first device to the smart metasurface RIS device and a signal exit angle from the RIS device to the second device; the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal exit angle includes: a second azimuth angle and a second elevation angle;

[0210] Determining a phase control matrix of the RIS device according to the signal arrival angle and the signal departure angle;

[0211] Determining a delay matrix and a Doppler matrix according to the phase control matrix;

[0212] The distance between the first device and the second device and the speed of the first device relative to the second device are determined according to the delay matrix and the Doppler matrix.

[0213] The information processing device provided in the embodiment of the present application obtains the signal arrival angle from the first device to the smart metasurface RIS device and the signal emission angle from the RIS device to the second device; the signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal emission angle includes: a second azimuth angle and a second elevation angle; according to the signal arrival angle and the signal emission angle, the phase control matrix of the RIS device is determined; according to the phase control matrix, the delay matrix and the Doppler matrix are determined; according to the delay matrix and the Doppler matrix, the distance between the first device and the second device and the speed of the first device relative to the second device are determined; it can support the implementation of enhancing the viewing angle coverage of RIS by introducing a vertical dimension in space, realizing a three-dimensional space solution for synaesthesia integrated signal processing based on RIS, increasing the dimensional consideration of the signal, and improving the processing accuracy of the solution, which well solves the problem of low processing accuracy of the solution for synaesthesia integrated signal processing in the prior art.

[0214] In an embodiment of the present application, determining the phase control matrix of the RIS device according to the signal arrival angle and the signal exit angle includes: determining a response steering vector of the RIS device according to the signal arrival angle and the signal exit angle; and determining the phase control matrix of the RIS device according to the response steering vector.

[0215] The step of determining a delay matrix and a Doppler matrix according to the phase control matrix comprises: determining a channel information matrix according to the phase control matrix; and determining a delay matrix and a Doppler matrix according to the channel information matrix.

[0216] In an embodiment of the present application, determining the distance between the first device and the second device and the speed of the first device relative to the second device based on the delay matrix and the Doppler matrix includes: acquiring a spectrum occupancy sequence; determining a channel matrix based on the spectrum occupancy sequence; and using a compressed sensing CS algorithm to determine the distance between the first device and the second device and the speed of the first device relative to the second device based on the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and a sparse basis.

[0217] Among them, determining the distance between the first device and the second device based on the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis includes: obtaining the distance spectrum vector of the first device based on the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis; determining a first peak index value based on the distance spectrum vector; determining the distance between the first device and the second device based on the first peak index value.

[0218] In an embodiment of the present application, the speed of the first device relative to the second device is determined according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis, including: obtaining a speed spectrum vector of the first device according to the delay matrix, Doppler matrix, spectrum occupancy sequence, channel matrix and sparse basis; determining a second peak index value according to the speed spectrum vector; and determining the speed of the first device relative to the second device according to the second peak index value.

[0219] Among them, the implementation embodiments of the above-mentioned information processing method are all applicable to the embodiments of the information processing device and can achieve the same technical effects.

[0220] An embodiment of the present application also provides an information processing device, including a memory, a processor, and a program stored in the memory and executable on the processor; when the processor executes the program, the above-mentioned information processing method is implemented.

[0221] Among them, the implementation embodiments of the above-mentioned information processing method are all applicable to the embodiments of the information processing device and can achieve the same technical effects.

[0222] An embodiment of the present application also provides a readable storage medium on which a program is stored, and when the program is executed by a processor, the steps in the above-mentioned information processing method are implemented.

[0223] Among them, the implementation embodiments of the above-mentioned information processing method are all applicable to the embodiments of the readable storage medium and can achieve the same technical effects.

[0224] The embodiment of the present application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the various processes of the method embodiment of the above-mentioned information processing method are implemented, and the same technical effect can be achieved. To avoid repetition, they are not repeated here.

[0225] It should be noted that many functional components described in this specification are referred to as modules in order to more particularly emphasize the independence of their implementation methods.

[0226] In the embodiment of the present application, module can be implemented with software so that it can be executed by various types of processors. For example, an executable code module of an identification can include one or more physical or logical blocks of computer instructions, for example, it can be constructed as an object, process or function. Nevertheless, the executable code of the identified module does not need to be physically located together, but can include different instructions stored in different positions, and when these instructions are logically combined together, it constitutes a module and realizes the specified purpose of the module.

[0227] In fact, executable code module can be a single instruction or many instructions, and can even be distributed on a plurality of different code segments, distributed among different programs, and distributed across a plurality of memory devices. Similarly, operating data can be identified in the module, and can be implemented and organized in the data structure of any appropriate type according to any appropriate form. The operating data can be collected as a single data set, or can be distributed in different locations (including on different storage devices), and can only be present on a system or network as an electronic signal at least in part.

[0228] When a module can be implemented by software, considering the level of existing hardware technology, a person skilled in the art can build a corresponding hardware circuit to implement the corresponding function of the module that can be implemented by software without considering the cost. The hardware circuit includes a conventional very large scale integration (VLSI) circuit or gate array and existing semiconductors such as logic chips, transistors, or other discrete components. The module can also be implemented by a programmable hardware device, such as a field programmable gate array, a programmable array logic, a programmable logic device, etc.

[0229] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary personnel in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An information processing method, characterized in that: include: Acquire a signal arrival angle from the first device to the smart metasurface RIS device and a signal exit angle from the RIS device to the second device; The signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal exit angle includes: a second azimuth angle and a second elevation angle; Determining a phase control matrix of the RIS device according to the signal arrival angle and the signal departure angle; Determining a delay matrix and a Doppler matrix according to the phase control matrix; The distance between the first device and the second device and the speed of the first device relative to the second device are determined according to the delay matrix and the Doppler matrix.

2. The information processing method according to claim 1, characterized in that: Determining the phase control matrix of the RIS device according to the signal arrival angle and the signal exit angle includes: Determining a response steering vector of the RIS device according to the signal arrival angle and the signal departure angle; A phase control matrix of the RIS device is determined according to the response steering vector.

3. The information processing method according to claim 1, characterized in that: The step of determining a delay matrix and a Doppler matrix according to the phase control matrix includes: Determining a channel information matrix according to the phase control matrix; A delay matrix and a Doppler matrix are determined according to the channel information matrix.

4. The information processing method according to claim 1, characterized in that: The determining, according to the delay matrix and the Doppler matrix, a distance between the first device and the second device and a speed of the first device relative to the second device, comprises: Obtain spectrum occupancy sequence; Determining a channel matrix according to the spectrum occupancy sequence; The compressed sensing (CS) algorithm is used to determine the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis.

5. The information processing method according to claim 4, characterized in that: The determining, according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis, the distance between the first device and the second device comprises: Obtaining a range spectrum vector of the first device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis; Determining a first peak index value according to the distance spectrum vector; A distance between the first device and the second device is determined according to the first peak index value.

6. The information processing method according to claim 4, characterized in that: Determining a speed of the first device relative to the second device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix, and the sparse basis includes: Obtaining a velocity spectrum vector of the first device according to the delay matrix, the Doppler matrix, the spectrum occupancy sequence, the channel matrix and the sparse basis; Determine a second peak index value according to the velocity spectrum vector; A speed of the first device relative to the second device is determined according to the second peak index value.

7. An information processing device, characterized in that: include: A first acquisition module is used to acquire a signal arrival angle from the first device to the smart metasurface RIS device and a signal exit angle from the RIS device to the second device; The signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal exit angle includes: a second azimuth angle and a second elevation angle; A first determination module is used to determine the phase control matrix of the RIS device according to the signal arrival angle and the signal emission angle; A second determination module is used to determine a delay matrix and a Doppler matrix according to the phase control matrix; The third determination module is used to determine the distance between the first device and the second device and the speed of the first device relative to the second device according to the delay matrix and the Doppler matrix.

8. An information processing device, characterized in that: include: processor; The processor is used to obtain a signal arrival angle from the first device to the smart metasurface RIS device and a signal exit angle from the RIS device to the second device; The signal arrival angle includes: a first azimuth angle and a first elevation angle; the signal exit angle includes: a second azimuth angle and a second elevation angle; Determining a phase control matrix of the RIS device according to the signal arrival angle and the signal departure angle; Determining a delay matrix and a Doppler matrix according to the phase control matrix; The distance between the first device and the second device and the speed of the first device relative to the second device are determined according to the delay matrix and the Doppler matrix.

9. An information processing device, comprising a memory, a processor, and a program stored in the memory and executable on the processor; characterized in that: When the processor executes the program, the information processing method according to any one of claims 1 to 6 is implemented.

10. A readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the information processing method according to any one of claims 1 to 6 are implemented.

11. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the information processing method according to any one of claims 1 to 6.

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