Multi-user communication enhancement method based on multi-metasurface network

Abstract

The invention relates to a multi-user communication enhancement method based on a multi-metasurface network. The method comprises the following steps: deploying the multi-metasurface network; performing three-dimensional beam scanning on the plurality of programmable metasurfaces to obtain an angle amplitude spectrum; position information of each user receiving end is obtained through estimation according to the angle amplitude spectrum; constructing a directed graph; constructing a gain weight matrix according to the distance between the nodes; on the basis of the gain weight matrix, a shortest path fast algorithm is adopted, and a programmable metasurface needing to participate when service is provided for each user receiving end is determined; and for each programmable metasurface, calculating the phase configuration of beamforming of each user receiving end providing service by the programmable metasurface, and synthesizing all the phase configurations to obtain the final phase configuration of the programmable metasurface. According to the method, the problem of low coverage efficiency of a single RIS system in an indoor short-distance scene due to a far-field boundary condition is solved, the signal coverage range and the communication quality are remarkably improved, and the method is particularly suitable for a high-density Internet of Things equipment scene.

Description

Technical Field

[0001] This application relates to the field of intelligent wireless communication, and specifically, to a multi-user communication enhancement method based on a multi-metasurface network. Background Art

[0002] The number of global Internet of Things (IoT) devices continues to grow and is expected to exceed 30 billion by 2025. In typical applications such as smart homes, smart agriculture, and industrial IoT, a large number of devices need to achieve high-density concurrent connections. However, the internal structure obstacles of buildings cause serious signal attenuation, and the antenna performance and computing power of low-cost IoT devices are limited, making stable wireless connections an urgent problem to be solved.

[0003] Existing solutions mainly include the following types: wireless relay technology, single-user reconfigurable intelligent surface (RIS) systems, and multi-RIS cooperation schemes. Among them, wireless relay devices reconstruct the link through signal amplification, but they require additional power supply and have poor protocol compatibility, making it difficult to meet the long-term deployment requirements. Single-RIS-based systems improve signal coverage by regulating the electromagnetic wave propagation environment, but are limited by far-field boundary conditions (usually hundreds of meters away) and cannot effectively act on indoor short-distance scenarios. When attempting to deploy a very large-scale RIS, the manufacturing and installation costs increase exponentially, and the multi-reflection paths introduce multiplicative fading effects, resulting in signal attenuation far exceeding the direct path. For multi-user scenarios, existing methods use algorithms such as particle swarm optimization to store configuration schemes offline. A single RIS needs to store 57 MB of configuration data, and the storage requirement surges to the order of 10,000 TB when the number of users increases to 3, presenting serious scalability problems.

[0004] In summary, there are three core defects in the existing technology: 1. Single-RIS systems are limited by physical scale and far-field conditions, resulting in low indoor coverage efficiency; 2. Large-scale RIS deployment is costly and introduces multiplicative signal attenuation; 3. Multi-user configuration schemes generate huge storage overheads and cannot meet real-time requirements. Summary of the Invention

[0005] To overcome at least one deficiency in the existing technology, this application provides a multi-user communication enhancement method based on a multi-metasurface network.

[0006] In a first aspect, a multi-user communication enhancement method based on a multi-metasurface network is provided, including:

[0007] Deploy a multi-metasurface network, where the multi-metasurface network includes multiple programmable metasurfaces, a transmitter, and multiple user receivers, and each user receiver is located within the field of view (FOV) of at least one programmable metasurface;

[0008] Multiple programmable metasurfaces perform three-dimensional beam scanning to obtain the angular amplitude spectrum; the position information of each user receiver is estimated based on the angular amplitude spectrum;

[0009] Based on the position information of the transmitter, the position information of multiple programmable metasurfaces, and the position information of each user receiver, the transmitter, multiple programmable metasurfaces, and multiple user receivers are used as nodes to construct a directed graph; a gain weight matrix is constructed based on the distances between the nodes; the shortest path fast algorithm is used based on the gain weight matrix to determine the programmable metasurfaces that need to participate when serving each user receiver;

[0010] For each programmable metasurface, calculate the phase configuration for beamforming of each user receiver served by the programmable metasurface, and synthesize all the phase configurations to obtain the final phase configuration of the programmable metasurface.

[0011] In one embodiment, the element w of the gain weight matrix ij , 1 ≤ i ≤ k, 1 ≤ j ≤ k, where k is the number of nodes:

[0012]

[0013] where, G t is the gain of the transmitting node, G r is the gain of the receiving node, λ is the signal wavelength, d ij is the distance from node i to node j in the directed graph, and α is the path loss exponent.

[0014] In one embodiment, using the shortest path fast algorithm based on the gain weight matrix to determine the programmable metasurfaces that need to participate when serving each user receiver includes:

[0015] Construct a target function based on the gain weight matrix and solve it to obtain the optimal gain path and the weight value of the optimal gain path; the programmable metasurfaces included in the optimal gain path form the first metasurface set; the target function is:

[0016]

[0017] where, P m is all the feasible paths from the transmitter node to the node corresponding to user receiver m in the directed graph, P is a feasible path in P m ; w ij is the element of the gain weight matrix, and i, j are the nodes in P; is the weight value of the optimal gain path P max ;

[0018] According to Set the enhanced performance threshold, in P mSelect a feasible path with a weight value greater than the enhanced performance threshold from the [[]], and the programmable metasurfaces included in the selected feasible path form a second metasurface set;

[0019] Merge the first metasurface set and the second metasurface set to obtain the programmable metasurfaces that need to participate when serving the user receiver m.

[0020] In one embodiment, for each programmable metasurface, calculate the phase configuration for beamforming of each user receiver served by the programmable metasurface, and synthesize all the phase configurations to obtain the final phase configuration of the programmable metasurface, including:

[0021] For each programmable metasurface, calculate the phase configuration for beamforming of each user receiver served by the programmable metasurface using the following formula:

[0022]

[0023] where, is the phase configuration for beamforming of the programmable metasurface n for the user receiver t, and t is the t-th user receiver served by the programmable metasurface n, is the path phase offset of the incident electromagnetic wave, is the path phase offset of the outgoing electromagnetic wave;

[0024] Synthesize all the phase configurations to obtain the final phase configuration of the programmable metasurface using the following formula:

[0025]

[0026] where, is the final phase configuration of the programmable metasurface n, T is the number of user receivers served by the programmable metasurface n, and β t is the user service priority.

[0027] Compared with the prior art, the present application has the following beneficial effects:

[0028] 1. Through the collaborative networking of multiple metasurfaces, the present application overcomes the problem of low coverage efficiency caused by far-field boundary conditions in the indoor short-distance scenario of a single RIS system, significantly improves the signal coverage range and communication quality, and is particularly suitable for scenarios with high-density Internet of Things devices.

[0029] 2. The present application adopts the SPFA algorithm combined with the fast synthesis technology, greatly reduces the storage overhead and computational complexity in the multi-user scenario, avoids the problem of exponential growth of storage requirements caused by the increase in the number of users in the prior art, and significantly improves the scalability and real-time performance of the system.

[0030] 3. This application estimates the user's location quickly and accurately through three-dimensional beam scanning combined with a coarse-grained - fine-grained hybrid scanning strategy. Compared with traditional single-precision scanning methods, it significantly reduces the scanning time and computational complexity, and improves the system performance.

[0031] 4. This application realizes multi-user simultaneous service through a fast synthesis technology, and can dynamically adjust the phase configuration according to the user priority, improving the fairness of the system and the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings. The drawings, together with the following detailed description, are included in this specification and form a part of this specification. In the drawings:

[0033] Figure 1 A flowchart showing a multi-user communication enhancement method based on a multi-metasurface network is shown;

[0034] Figure 2 An experimental floor plan is shown;

[0035] Figure 3 A directed graph corresponding to the experimental floor plan is shown;

[0036] Figure 4 A graph showing the experimental results of the communication performance improvement of this application under different numbers of users is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] Hereinafter, exemplary embodiments of this application will be described in conjunction with the accompanying drawings. For clarity and conciseness, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions may be made in the process of developing any such actual embodiment in order to achieve the specific goals of the developer, and these decisions may vary with different embodiments.

[0038] Here, it should also be noted that in order to avoid obscuring this application with unnecessary details, only the device structures closely related to the solution of this application are shown in the drawings, and other details less related to this application are omitted.

[0039] It should be understood that this application is not limited to the described embodiments only due to the following description with reference to the drawings. In this document, where feasible, embodiments can be combined with each other, features can be replaced or borrowed between different embodiments, and one or more features can be omitted in one embodiment.

[0040] An embodiment of this application provides a multi-user communication enhancement method based on a multi-metasurface network. Figure 1The flowchart of the multi - user communication enhancement method based on a multi - metasurface network is shown. Refer to Figure 1 , the method mainly includes the following steps:

[0041] Step S1: Deploy a multi - metasurface network. The multi - metasurface network includes multiple programmable metasurfaces, a transmitter, and multiple user receivers. Each user receiver is located within the field of view (FOV) of at least one programmable metasurface.

[0042] The programmable metasurface is a transmissive / reflective hybrid reconfigurable intelligent surface. Its operating frequency band includes three ISM sub - bands: 2.400 - 2.4835 GHz, 5.150 - 5.350 GHz, and 5.725 - 5.850 GHz. Its units are arranged in a two - dimensional planar array with a spacing not exceeding 1 / 2 of the operating wavelength and are controlled by a microcontroller.

[0043] Step S2: Multiple programmable metasurfaces perform three - dimensional beam scanning to obtain the angular amplitude spectrum; the position information of each user receiver is estimated based on the angular amplitude spectrum.

[0044] For three - dimensional beam scanning, the azimuth scanning range is from 0° to 180°, with a 1° step, and the elevation scanning range is from - 60° to 60°, with a 1° step; coarse - grained - fine - grained hybrid scanning: In the coarse scanning stage, the azimuth steps by 10° and the elevation steps by 5°, and the single - beam dwell time ≤ 1 ms; in the fine scanning stage, it scans with a 1° step.

[0045] Step S3: According to the position information of the transmitter, the position information of multiple programmable metasurfaces, and the position information of each user receiver; taking the transmitter, multiple programmable metasurfaces, and multiple user receivers as nodes, construct a directed graph; here, when the user receiver node is within the line - of - sight path of the programmable metasurface, there is an edge between them, and in addition, when there is no communication between nodes, there is no edge between nodes.

[0046] Construct a gain weight matrix according to the distances between nodes; specifically, the gain weight matrix W can be expressed as:

[0047]

[0048] The element w of the gain weight matrix ij , 1 ≤ i ≤ k, 1 ≤ j ≤ k, where k is the number of nodes:

[0049]

[0050] Among them, G t is the transmitting node gain, G r is the receiving node gain, λ is the signal wavelength, d ijLet \(d_{ij}\) be the distance from node \(i\) to node \(j\) in the directed graph, and \(\alpha\) be the path loss exponent, which is 2 in free space, 4 to 6 in indoor blockage, and 5 to 20 in urban non-line-of-sight.

[0051] Based on the gain weight matrix, the Shortest Path Faster Algorithm (SPFA) is used to determine the programmable metasurfaces that need to participate when serving each user receiver.

[0052] Step S4: For each programmable metasurface, calculate the phase configuration for beamforming of each user receiver served by the programmable metasurface, and synthesize all the phase configurations to obtain the final phase configuration of the programmable metasurface.

[0053] In this embodiment, through the collaborative networking of multiple metasurfaces, the problem of low coverage efficiency caused by far-field boundary conditions in the indoor short-distance scenario of a single RIS system is overcome, and the signal coverage range and communication quality are significantly improved, which is especially suitable for scenarios with high-density Internet of Things devices.

[0054] In one embodiment, in step S3, based on the gain weight matrix, the Shortest Path Faster Algorithm is used to determine the programmable metasurfaces that need to participate when serving each user receiver, including:

[0055] First, construct an objective function based on the gain weight matrix and solve it to obtain the optimal gain path and the weight value of the optimal gain path; the programmable metasurfaces included in the optimal gain path form the first metasurface set; the objective function is:

[0056]

[0057] where \(P\) m is all the feasible paths from the sending end node to the node corresponding to user receiver \(m\) in the directed graph, and \(P\) is a feasible path in \(P\) m ; \(w\) ij is an element of the gain weight matrix, and \(i, j\) are nodes in \(P\); is the weight value of the optimal gain path \(P\) max ;

[0058] Then, in order to consider the potential gain of sub-optimal paths, record all the paths and their weight values from the sending end node to the node corresponding to user receiver \(m\) during the algorithm operation, and set the enhanced performance threshold according to . Specifically, set as the enhanced performance threshold, \(\theta = 3\), and \(\sigma\) is the fluctuation parameter. In \(P\) mSelect the feasible paths with weight values greater than the enhanced performance threshold from the paths, and the programmable metasurfaces included in the selected feasible paths form the second metasurface set; the selected feasible paths have effective communication enhancement for the user.

[0059] Then, merge the first metasurface set and the second metasurface set to obtain the programmable metasurfaces that need to participate when serving the user receiver m.

[0060] In one embodiment, in step S4, for each programmable metasurface, calculate the phase configuration for beamforming of each user receiver served by the programmable metasurface, and synthesize all the phase configurations to obtain the final phase configuration of the programmable metasurface, including:

[0061] First, for each programmable metasurface, calculate the phase configuration for beamforming of each user receiver served by the programmable metasurface, using the following formula:

[0062]

[0063] Where, is the phase configuration for beamforming of the programmable metasurface n for the user receiver t, and t is the t-th user receiver served by the programmable metasurface n, is the path phase offset of the incident electromagnetic wave, is the path phase offset of the outgoing electromagnetic wave;

[0064] Then, synthesize all the phase configurations to obtain the final phase configuration of the programmable metasurface, using the following formula:

[0065]

[0066] Where, is the final phase configuration of the programmable metasurface n, T is the number of user receivers served by the programmable metasurface n, and β t is the user service priority.

[0067] To further verify the effectiveness of the method of the present application, the following experimental analysis was carried out.

[0068] Figure 2 Shows the experimental floor plan, see Figure 2, in a typical home environment with a size of 15m × 10m, a network consisting of 2 programmable metasurfaces is deployed. The network includes a transmitter, 2 programmable metasurfaces, and 3 user receivers to form a communication system, where the network deployment needs to ensure that at least one metasurface covers the position of any user receiver. Among them, both the transmitter and the user receivers are USRP N210 software-defined radio devices equipped with UBX-40 daughter boards, the sampling rate is set to 1MHz, and the transmission signal frequency is 5.2GHz; the programmable metasurface is a reflective metasurface operating on dual frequency bands of 2.4GHz and 5GHz, and the total area of a single metasurface is 0.35×0.35m 2 , which is composed of 16×16 superatom units with an atomic spacing of 19.5mm; the state of the microcontroller control unit of model STM32H743IIT6 is adopted. Figure 3 The directed graph corresponding to the experimental floor plan is shown.

[0069] The number of users is adjusted from less to more to compare the improvement of the communication performance of the receiver under different numbers of users. The specific control test conditions and control schemes are as follows:

[0070] The number of users in the experimental scenario is gradually increased from 2 to 6 to compare the improvement of the communication performance of the receiver under different numbers of users. Figure 4 The experimental result graph of the communication performance improvement of this application under different numbers of users is shown. According to Figure 4 , it can be observed that when the number of users is between 2 and 6, the communication performance of the receiver can still be improved by more than 10dB on average. Through the above experiments, the feasibility of the method of this application is verified.

[0071] In summary, this application has the following technical effects:

[0072] 1. Through the collaborative networking of multiple metasurfaces, this application overcomes the problem of low coverage efficiency caused by far-field boundary conditions in the indoor short-distance scenario of a single RIS system, significantly improves the signal coverage range and communication quality, and is especially suitable for scenarios with high-density Internet of Things devices.

[0073] 2. This application adopts the SPFA algorithm combined with fast synthesis technology, which greatly reduces the storage overhead and computational complexity in the multi-user scenario, avoids the problem of exponential growth of storage requirements caused by the increase in the number of users in the prior art, and significantly improves the scalability and real-time performance of the system.

[0074] 3. Through three-dimensional beam scanning combined with a coarse-grained - fine-grained hybrid scanning strategy, this application quickly and accurately estimates the user position. Compared with the traditional single-precision scanning method, it significantly reduces the scanning time and computational complexity and improves the system performance.

[0075] 4. This application realizes multi-user simultaneous service through a fast integration technology, and can dynamically adjust the phase configuration according to the user priority, improving the fairness of the system and the user experience.

[0076] As described above, these are only various implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

1. A multi-user communication enhancement method based on a multi-metasurface network, characterized in that Including: Deploying a multi-metasurface network, the multi-metasurface network includes a plurality of programmable metasurfaces, a transmitting end, and a plurality of user receiving ends, and each user receiving end is within the field of view (FOV) of at least one programmable metasurface; The plurality of programmable metasurfaces perform three-dimensional beam scanning to obtain an angular amplitude spectrum; Estimating the position information of each user receiving end according to the angular amplitude spectrum; According to the position information of the transmitting end, the position information of the plurality of programmable metasurfaces, and the position information of each user receiving end, taking the transmitting end, the plurality of programmable metasurfaces, and the plurality of user receiving ends as nodes to construct a directed graph; constructing a gain weight matrix according to the distance between the nodes; based on the gain weight matrix, using the shortest path fast algorithm to determine the programmable metasurfaces that need to participate when providing services for each user receiving end; For each programmable metasurface, calculating the phase configuration for beamforming of each user receiving end served by the programmable metasurface, and synthesizing all the phase configurations to obtain the final phase configuration of the programmable metasurface.

2. The method according to claim 1, wherein The element w of the gain weight matrix ij , where 1 ≤ i ≤ k, 1 ≤ j ≤ k, and k is the number of nodes: Among them, G t is the transmitting node gain, G r is the receiving node gain, λ is the signal wavelength, d ij is the distance from node i to node j in the directed graph, and α is the path loss exponent.

3. The method according to claim 1, wherein Wherein, Based on the gain weight matrix, using the shortest path fast algorithm to determine the programmable metasurfaces that need to participate when providing services for each user receiving end, including: Constructing an objective function based on the gain weight matrix and solving it to obtain the optimal gain path and the weight value of the optimal gain path; the programmable metasurfaces included in the optimal gain path form a first metasurface set; the objective function is: Among them, P m is all the feasible paths from the sending end node in the directed graph to the node corresponding to the user receiving end m, and P is a feasible path in P m ; w ij is an element of the gain weight matrix, and i, j are nodes in P; is the weight value of the optimal gain path P max ; According to Set an enhanced performance threshold, and select a feasible path with a weight value greater than the enhanced performance threshold in P m The programmable metasurfaces included in the selected feasible paths form a second metasurface set; Combining the first metasurface set and the second metasurface set to obtain the programmable metasurfaces that need to participate when providing services for user receiving end m.

4. The method according to claim 1, wherein Wherein, For each programmable metasurface, calculating the phase configuration for beamforming of each user receiving end served by the programmable metasurface, and synthesizing all the phase configurations to obtain the final phase configuration of the programmable metasurface, including: For each programmable metasurface, calculating the phase configuration for beamforming of each user receiving end served by the programmable metasurface, using the following formula: Among them, is the phase configuration for the programmable metasurface n to perform beamforming on the user receiver t, where t is the t-th user receiver served by the programmable metasurface n, is the path phase offset of the incident electromagnetic wave, is the path phase offset of the outgoing electromagnetic wave; Synthesizing all the phase configurations to obtain the final phase configuration of the programmable metasurface, using the following formula: Among them, is the final phase configuration of the programmable metasurface n, T is the number of user receivers served by the programmable metasurface n, and β t is the user service priority.

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