Communication and imaging integrated method based on distributed intelligent metasurface cooperation

Abstract

The invention discloses a distributed intelligent metasurface cooperative communication and imaging integrated method, and belongs to the technical field of wireless communication and sensing. According to the method, a multi-antenna and multi-carrier frequency technology is adopted, and a plurality of reconfigurable intelligent metasurface RISs are utilized to cooperatively complete communication and imaging of a target region ROI. Firstly, an imaging target ROI is divided into a plurality of space grid units, and equivalent scattering coefficients of all the units form a vector to serve as a solving target. A plurality of RISs are installed at different positions of the ROI near field. Secondly, transmitting pilot signals for a plurality of times, and configuring different RIS phases; then, a receiving end obtains overall channel state information by using a channel estimation algorithm, and extracts channel components which finally reach a receiving antenna from a transmitting antenna through the ROI and the RIS in sequence; and finally, obtaining a sensing matrix, solving an equivalent scattering coefficient of the ROI unit by using a compressed sensing imaging algorithm, and completing target imaging.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication and perception technology, and in particular relates to a distributed intelligent super-surface collaborative communication and imaging integrated method. Background Art

[0002] With the development of mobile communication systems, higher frequency bands, wider bandwidths, and larger-scale antenna arrays make high-precision and high-resolution perception possible, so that communication and perception integration (ISAC) can be realized in the system, making communication and perception complement each other. Research on ISAC application scenarios and potential needs based on 5G-NR has been initially launched in the 3rd Generation Partnership Project (3GPP). In the future, the application scenarios of ISAC systems are likely to include ultra-high-precision positioning and tracking, imaging, map construction, human sensory enhancement, etc., which requires the ISAC system to break through the limitations of the current 5G system and continue to optimize to achieve comprehensive interawareness integration.

[0003] Imaging is an important part of perception and can provide richer environmental information than positioning. RIS can assist in the integration of communication and imaging by reconstructing the electromagnetic environment. RIS is composed of a large number of carefully designed electromagnetic units. By applying control signals to the adjustable elements on the electromagnetic units, various electromagnetic parameters of electromagnetic waves, such as amplitude, phase and polarization, can be controlled in a programmable manner to achieve spatial beam control. Compared with traditional wireless relays, RIS can achieve full-duplex mode transmission without introducing self-interference, and has the advantages of light volume, low cost and low energy consumption. RIS-assisted imaging has two main advantages. First, as an important part of the future communication system, RIS participates in communication and imaging at the same time, without the need for additional sensing equipment, to achieve the integration of communication and imaging; second, RIS can control the electromagnetic environment by changing its own phase to obtain more information about the imaging target.

[0004] At present, the research on RIS in the field of communication and imaging integration has just started, and there are few related works and reports. The design and research of imaging algorithms are still in the initial stage, and a mature solution has not yet been formed. There is no research on related technical verification. In existing research, the inherent resources of communication systems such as multiple antennas and multiple carrier frequencies have not been effectively utilized, and a single RIS has poor imaging capabilities for three-dimensional targets due to angle limitations. It is necessary to configure multiple RIS to more effectively obtain target information and achieve high-precision imaging. Summary of the invention

[0005] Purpose of the invention: In view of the problem of low accuracy of RIS-assisted imaging in current research, the present invention provides an integrated communication and imaging method for distributed intelligent metasurface collaboration, aiming to reasonably install multiple RIS for collaborative perception based on multi-antenna and multi-carrier resources in the communication system, complete high-precision imaging of the target during communication, and shorten the time required for imaging.

[0006] Technical solution: The present invention is a distributed intelligent metasurface collaborative communication and imaging integrated method, which adopts multi-antenna and multi-carrier technology and utilizes multiple reconfigurable intelligent metasurfaces (RIS, Reconfigurable Intelligent Surface) to collaborate to complete communication and imaging of the target area (ROI, Region of Interest). Its imaging function includes the following steps:

[0007] Step 1: Divide the imaging target ROI into several spatial grid units, and use the equivalent scattering coefficient of each unit to form a vector as the solution target. Install multiple RIS in the near field of ROI for collaboration, and the scattering coefficient of each RIS unit can be adjusted independently.

[0008] Step 2: The transmitting antenna transmits the pilot signal several times and configures different RIS phases.

[0009] Step 3: After receiving the pilot signal, the receiving antenna performs multipath channel estimation and extracts the channel component (TX-ROI-RIS-RX, Transmitter-Region of Interest-Reconfigurable Intelligent Surface-Receiver) that passes through the ROI and RIS from the transmitting antenna and finally reaches the receiving antenna as the measurement of the transmitted pilot signal at the receiving antenna.

[0010] Step 4: Based on the known physical parameters such as the device position and RIS phase, the sensing matrix is ​​obtained, and the equivalent scattering coefficient of the ROI unit is solved using the compressed sensing imaging algorithm to complete the target imaging.

[0011] Furthermore, the receiving antenna is configured with a multi-antenna array, which can be a linear array or a planar array. The distributed RIS system uses multiple RIS installed at different locations near the ROI, and each RIS can have different sizes, number of units, and orientations to capture multi-dimensional information of the ROI from different directions.

[0012] Furthermore, in step 2, the transmitting antenna adopts the standard frame structure of the fifth generation mobile communication system (5G, 5th Generation Mobile Communication Technology) new air interface (NR, New Radio) or other communication systems, and the transmitted multi-carrier signal adopts an orthogonal frequency division multiplexing (OFDM, Orthogonal Frequency Division Multiplexing) waveform. The pilot signal can be selected at different symbol times and carrier frequency positions according to needs.

[0013] Furthermore, in step 2, when transmitting the pilot signal, each symbol time is configured with a different RIS phase, which can be obtained by any one of random generation, simulated annealing algorithm, genetic algorithm, hybrid genetic simulated annealing algorithm and other phase optimization algorithms.

[0014] Furthermore, in step 3, the receiving antenna distinguishes channels passing through different RIS based on the angle of arrival (AOA) to complete channel estimation. The algorithm for estimating AOA is any one of the multiple signal classification (MUISC), estimating signal parameters via rotational invariance techniques (ESPRIT), orthogonal matching pursuit (OMP), and You Only Look Once (YOLO) algorithms based on deep learning.

[0015] Furthermore, step 4 is specifically as follows: divide the ROI into N spatial grid cells, each of which has its equivalent scattering coefficient x n , all scattering coefficients form a column vector x of length N, and the spatial vector of the nth ROI unit position is recorded as The number of transmitting antennas is N T , the i-th root is placed in the space vector The number of receiving antennas is N R , the jth root is placed in the space vector The distributed RIS system uses T-block RIS for collaboration, and the t-th RIS contains M t units, the spatial vector of the position of the mth unit of the tth RIS block is recorded as

[0016] The transmitting antenna transmits K pilot signals in total. After receiving the pilot signal, the receiving end extracts the overall channel state information through the channel estimation algorithm, and extracts the channel (TX-ROI-RIS) from the transmitting antenna through the ROI and the tth RIS to the receiving antenna. t -RX path). For TX-ROI-RIS t -RX propagation path, the signal sent by the kth transmitting antenna from the i-th transmitting antenna passes through the nth ROI unit and the mth RIS unit of the t-th RIS block, and finally reaches the j-th receiving antenna. The channel experienced can be expressed as:

[0017]

[0018] Among them, g is a constant determined by the transmit and receive gain of the antenna, and λ k represents the wavelength of the carrier frequency of the kth pilot signal, x n represents the scattering coefficient of the nth ROI unit, It is the phase configuration of the mth unit of the tth RIS when the kth pilot signal is transmitted.

[0019] The i-th transmitting antenna sends a signal for the kth time, and after the t-th RIS, all N R The N obtained on the receiving antenna R The measurement is written as 1*N R The vector y i,k,t ,satisfy

[0020] y i,k,t =A i,k,t ×

[0021] Among them, A i,k,t It is the channel matrix calculated according to the physical parameter configuration.

[0022] All the y obtained by sending signals from all transmitting antennas multiple times through each RIS i,k,t Written as vector y, the equation to be solved is expressed as

[0023] y=A×x

[0024] in, It is a vector composed of all the measurements obtained by each signal sent by each transmitting antenna through each RIS. The A of each RIS is the signal sent by each transmitting antenna. i,k,t The perception matrix.

[0025] Finally, based on the theory of compressed sensing, the sparse vector x is solved to obtain the equivalent scattering coefficient of the ROI unit, and the imaging of the target is completed. The algorithm for solving x is any one of the OMP, subspace pursuit (SP) and compressed sampling matching pursuit (CoSaMP) algorithms.

[0026] The present invention also discloses a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of the present invention.

[0027] The present invention also discloses a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the method of the present invention are implemented.

[0028] The present invention also discloses a computer program product, comprising a computer program / instruction, which implements the steps of the method of the present invention when executed by a processor.

[0029] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: the present invention utilizes the ability of RIS to regulate the electromagnetic environment, and obtains information of various dimensions of the target from different directions through distributed RIS-assisted imaging, solving the problem of weak resolution of three-dimensional targets by single RIS imaging. At the same time, multiple RIS can achieve better imaging effects in half or even less time than single RIS, effectively reducing the time cost required for imaging. The present invention also utilizes multi-antenna and multi-carrier technology to obtain richer information about the target, designs a reasonable pilot configuration scheme, further improves imaging accuracy, and realizes integrated communication and imaging. Compared with the prior art, it has the advantages of high flexibility, high speed, and high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of an integrated communication and imaging method for distributed intelligent metasurface collaboration.

[0031] Figure 2 It is a scene diagram of a distributed intelligent metasurface collaborative communication and imaging integrated method in an embodiment of the present invention.

[0032] Figure 3 It is a schematic diagram of multiple optional pilot configuration schemes for a distributed intelligent metasurface collaborative communication and imaging integrated method in an embodiment of the present invention.

[0033] Figure 4It is a curve of the normalized mean square error (NMSE) of the imaging results of a distributed intelligent metasurface collaborative communication imaging integrated method and a single RIS imaging in an embodiment of the present invention with respect to the number of pilot signal transmissions K. DETAILED DESCRIPTION

[0034] The present invention is specifically explained below in conjunction with the accompanying drawings and an embodiment of a distributed intelligent super-surface collaborative communication and imaging integrated method:

[0035] The present invention discloses a distributed intelligent metasurface collaborative communication and imaging integrated method, which adopts multi-antenna and multi-carrier technology and utilizes multiple RIS to collaborate to complete communication and imaging of ROI. The imaging process is as follows: Figure 1 shown.

[0036] Scenes such as Figure 2 As shown, ROI is a rectangular area whose faces are parallel to the coordinate plane. Multiple transmitting and receiving antennas are placed in linear arrays, with a total of N T The i-th transmitting antenna is placed at the space vector Position; Total N R The jth receiving antenna is placed in the space vector In this example, N T and N R Take 2 for both.

[0037] The ROI is divided into 8×8×8 spatial grid cells, each of which has its equivalent scattering coefficient x n , all scattering coefficients form a column vector x of length 512, and the spatial vector of the nth ROI unit position is recorded as

[0038] Two RISs are placed in the near field of the ROI, called RIS1 and RIS2. RIS1 is placed directly behind the ROI (negative direction of the x-axis) and parallel to the yOz plane; RIS2 is placed directly above the ROI (positive direction of the z-axis) and parallel to the xOy plane. RIS1 and RIS2 each have 50*50 RIS units, and their scattering coefficients can be adjusted independently. The spatial vector of the position of the mth unit of the tth RIS is recorded as

[0039] The transmitting antenna transmits K pilot signals in total, such as Figure 3 As shown, the following pilot configuration scheme is adopted: the multi-carrier signal sent by the transmitting antenna adopts OFDM waveform. In this example, a total of 5 carrier frequencies are used, with a center frequency of 3GHz and a carrier frequency interval of 300kHz. The pilot signal can be selected at different symbol times and carrier frequency positions according to needs. For example, all carrier frequencies at a specific time (such as Figure 3 (b)); or one or more carrier frequencies for part of the time, and the time and carrier frequency can be fixed (such as Figure 3 (a) and Figure 3 (d)) or randomly select each time (such as Figure 3 (c)).

[0040] After receiving the pilot signal, the receiver extracts the overall channel state information through the channel estimation algorithm, and extracts the channel (TX-ROI-RIS) from the transmitting antenna through the ROI and the tth RIS to the receiving antenna. t -RX path), and obtain the measurement y. And calculate the perception matrix A based on the known physical parameters such as device position and RIS phase.

[0041] The calculation process of measurement y and perception matrix A is as follows:

[0042] For TX-ROI-RIS t -RX propagation path, the signal sent by the kth transmitting antenna from the i-th transmitting antenna passes through the nth ROI unit and the mth RIS unit of the t-th RIS block, and finally reaches the j-th receiving antenna. The channel experienced can be expressed as:

[0043]

[0044] Among them, g is a constant determined by the transmit and receive gain of the antenna, and λ k represents the wavelength of the carrier frequency of the kth pilot signal, x n represents the scattering coefficient of the nth ROI unit, It is the phase configuration of the mth unit of the tth RIS when the kth pilot signal is transmitted.

[0045] The i-th transmitting antenna transmits the k-th pilot signal, which is reflected by the t-th RIS and received by the j-th receiving antenna. Through channel estimation, the channel measurement of the TX-ROI-RIS_t-RX path can be obtained as follows:

[0046]

[0047] in, It is the vector composed of all channels from each unit of the t-th RIS to the j-th receiving antenna through which the k-th signal transmitted from the i-th transmitting antenna is transmitted; The phase configuration of each unit of the t-th RIS block when transmitting the k-th pilot signal is a diagonal matrix with diagonal elements; It is the matrix composed of all channels of the k-th signal sent from each unit of ROI to each unit of t-th RIS; It is a diagonal matrix with all channels of the k-th signal sent by the i-th transmitting antenna to each unit of the ROI as diagonal elements.

[0048] Furthermore, the i-th transmitting antenna sends a signal for the kth time, and after the t-th RIS, the measurements obtained on the two receiving antennas are written as a vector y with a length of 2 i,k,t , which can be expressed as

[0049]

[0050] Among them, h k,t =[h k,t,1 ,h k,t,2 ] is the matrix of all channels that transmit the k-th signal from each unit of the t-th RIS to each receiving antenna.

[0051] All the y obtained by sending signals from all transmitting antennas multiple times through each RIS i,k,t Written as vector y, the equation to be solved is expressed as:

[0052] y=A×x

[0053] in, is the total y obtained by each signal sent by each transmitting antenna passing through each RIS i,k,t The vector composed of It is the signal sent by each transmitting antenna passing through all the A of each RIS. i,k,t The matrix composed of.

[0054] According to the equation y=A×x, the vector x is solved by the compressed sensing algorithm to obtain the equivalent scattering coefficient of the ROI unit, thus completing the imaging of the target.

[0055] In order to evaluate the gain of the present invention on the perception accuracy, under different total numbers of pilot signals K, based on this example, only RIS1 and RIS2 are used for imaging as a control, and at the same time, the number of pilots of the distributed RIS collaboration (two RISs in this example) proposed by the present invention is set to Comparing the NMSE of the imaging result with the target true value, the simulation results are as follows Figure 4 According to the simulation results, no matter how the K value increases, single RIS imaging cannot achieve the expected imaging effect. However, when the K value of distributed RIS collaborative imaging reaches a certain level, the NMSE of the imaging result is less than -20dB, and the accuracy is relatively high.

[0056] The simulation results confirm that the integrated communication and imaging method of distributed intelligent metasurface collaboration provided by the present invention can effectively solve the problem of weak three-dimensional target resolution capability of single RIS imaging, and can achieve better imaging effects in only half the time of single RIS, thus reducing the time cost required for imaging and having the advantages of high flexibility, high speed and high precision.

[0057] The above, in conjunction with the accompanying drawings, provides a detailed introduction to the communication and imaging integration method of a distributed intelligent supersurface collaboration provided by the present invention, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are illustrative and not restrictive. The embodiments are only for illustrating the technical ideas of the present invention and are not intended to limit the scope of protection of the present invention. Under the enlightenment of the present invention, those of ordinary skill in the art will make changes in the specific implementation methods and scope of application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed intelligent metasurface collaborative communication and imaging integrated method, characterized in that: Using multi-antenna and multi-carrier technology, multiple reconfigurable intelligent metasurfaces (RIS) are used to collaborate to complete communication and imaging of the target area ROI. The imaging function includes the following steps: Step 1: Divide the imaging target ROI into several spatial grid units, use the equivalent scattering coefficient of each unit as a vector as the solution target, install multiple RIS in the near field of ROI for collaboration, and independently adjust the scattering coefficient of each unit of RIS; Step 2: The transmitting antenna transmits several pilot signals and configures different RIS phases; Step 3: After receiving the pilot signal, the receiving antenna performs multipath channel estimation and extracts the channel component that passes through the ROI and RIS from the transmitting antenna and finally reaches the receiving antenna as the measurement of the pilot signal transmitted at the receiving antenna. Step 4: Based on the known physical parameters such as the device position and RIS phase, the sensing matrix is ​​obtained, and the equivalent scattering coefficient of the ROI unit is solved using the compressed sensing imaging algorithm to complete the target imaging.

2. The method for integrated communication and imaging of distributed intelligent metasurface collaboration according to claim 1, characterized in that: The receiving antenna is configured with a multi-antenna array, which can be a linear array or a planar array. The distributed RIS system uses multiple RIS installed at different positions in the ROI near field. Each RIS can have different sizes, number of units and orientations to capture multi-dimensional information of the ROI from different directions.

3. The method for integrated communication and imaging of distributed intelligent metasurface collaboration according to claim 1, characterized in that: In step 2, the transmitting antenna adopts the 5G-NR standard frame structure, and the transmitted multi-carrier signal adopts an orthogonal frequency division multiplexing OFDM waveform. The pilot signal is selected at different symbol times and carrier frequency positions according to requirements.

4. The method for integrated communication and imaging of distributed intelligent metasurface collaboration according to claim 1, characterized in that: In step 2, the transmitting antenna transmits pilot signals several times. When transmitting the pilot signals, a different RIS phase is configured for each symbol time, which can be obtained by any one of random generation, simulated annealing algorithm, genetic algorithm, and hybrid simulated annealing phase optimization algorithm.

5. The method for integrated communication and imaging of distributed intelligent metasurface collaboration according to claim 1, characterized in that: In step 3, the receiving antenna distinguishes channels passing through different RIS based on the arrival angle AOA and completes channel estimation. The algorithm for estimating AOA is any one of multiple signal classification, MUISC rotation invariant technology to estimate signal parameters ESPRIT, orthogonal matching pursuit OMP, and YOLO algorithm based on deep learning and only operates once.

6. The method for integrated communication and imaging of distributed intelligent metasurface collaboration according to claim 1, characterized in that: Step 4 is as follows: Divide the imaging target ROI into several spatial grid cells, each of which has its equivalent scattering coefficient x n , all scattering coefficients form a column vector x of length N, and the spatial vector of the nth ROI unit position is recorded as The number of transmitting antennas is N T , the i-th root is placed in the space vector The number of receiving antennas is N R , the jth root is placed in the space vector The distributed RIS system uses T-block RIS for collaboration, and the t-th RIS contains M t units, the spatial vector of the position of the mth unit of the tth RIS block is recorded as The transmitting antenna transmits K pilot signals in total. After receiving the pilot signal, the receiving end extracts the overall channel state information through the channel estimation algorithm, and extracts the channel TX-ROI-RIS that passes through the ROI and the tth RIS from the transmitting antenna and finally reaches the receiving antenna t -RX; for TX-ROI-RIS t -RX propagation path, the signal sent by the kth transmitting antenna from the i-th transmitting antenna passes through the nth ROI unit and the mth RIS unit of the t-th RIS block, and finally reaches the j-th receiving antenna. The channel experienced can be expressed as: Among them, g is a constant determined by the transmit and receive gain of the antenna, and λ k represents the wavelength of the carrier frequency of the kth pilot signal, x n represents the scattering coefficient of the nth ROI unit, is the phase configuration of the mth unit of the tth RIS when the kth pilot signal is transmitted; The i-th transmitting antenna sends a signal for the kth time, and after the t-th RIS, all N R The N obtained on the receiving antenna R The measurement is written as 1*N R The vector y i,k,t ,satisfy: and i,k,t =A i，k,t ×x Among them, A i,k,t It is the channel matrix calculated based on the physical parameter configuration; All the y obtained by sending signals from all transmitting antennas multiple times through each RIS i,k,t Written as vector y, the equation to be solved is expressed as: y=A×x in, It is a vector composed of all the measurements obtained by each signal sent by each transmitting antenna through each RIS. The A of each RIS is the signal sent by each transmitting antenna. i,k,t The perception matrix composed of Finally, based on the compressed sensing theory, the sparse vector x is solved to obtain the equivalent scattering coefficient of the ROI unit, thus completing the imaging of the target.

7. The method for integrated communication and imaging of distributed intelligent metasurface collaboration according to claim 6, characterized in that: Based on the compressed sensing theory, the sparse vector x is solved. The algorithm for solving x is any one of the OMP, subspace pursuit SP, and compressed sampling matching pursuit CoSaMP algorithms.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 1.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

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