Intelligent reflector deployment system based on three-dimensional vision

Abstract

The present application relates to the field of intelligent reflecting surface deployment, and particularly relates to an intelligent reflecting surface deployment system based on three-dimensional vision. The scheme comprises the following steps: a three-dimensional reconstructor performs three-dimensional visual scanning and reconstruction on an indoor scene where a transmitter and a receiver are located after receiving an instruction signal, and transmits a three-dimensional scene model obtained to a signal strength simulator; the signal strength simulator receives the three-dimensional scene model of the indoor environment and an instruction signal of a coordinator, generates communication signal strength distribution data under the three-dimensional scene model through a communication signal strength simulation algorithm, and transmits the communication signal strength distribution data to a deployment optimizer; the deployment optimizer receives the communication signal strength distribution data and the instruction signal of the coordinator, determines an optimal deployment position and a number of intelligent reflecting surfaces through an intelligent reflecting surface deployment optimization algorithm, and transmits the optimal deployment position and the number of intelligent reflecting surfaces to a controller; and the controller deploys the corresponding number of intelligent reflecting surfaces to the corresponding positions. The present application is suitable for indoor intelligent reflecting surface deployment.

Description

Technical Field

[0001] The present invention relates to the field of intelligent reflective surface deployment, and in particular to an intelligent reflective surface deployment system based on three-dimensional vision. Background Art

[0002] In wireless communication systems, obstructions can significantly attenuate wireless signal strength, severely limiting signal coverage. To address this issue, a common approach is to increase the density of base station deployments. However, this approach significantly increases network operating costs and system energy consumption. In recent years, smart reflective surface technology has demonstrated great potential as an efficient and viable alternative to address these issues.

[0003] While numerous recent studies have demonstrated the enormous potential of intelligent reflective surfaces (IRSs) to improve wireless communication system performance, most existing solutions suffer from a critical flaw: theoretical analysis and system design often assume that IRSs are deployed in locations that optimize communication performance, without delving into how this "optimal location" is determined in real-world environments. While this assumption facilitates algorithm validation and system modeling, it overlooks the numerous challenges inherent in actual deployment.

[0004] First, wireless propagation in real-world environments is influenced by factors such as building structure, wall material, and the placement and material of objects in the environment, resulting in a high degree of uncertainty and spatial heterogeneity in communication channels. In this context, simply placing smart reflective surfaces in the "theoretically optimal" location is not practical. Second, there is currently a lack of systematic deployment strategies and optimization algorithms that can automatically recommend or search for suitable locations for smart reflective surfaces based on actual scenario information. This significantly limits the implementation and large-scale application of smart reflective surface technology in real-world networks.

[0005] Furthermore, while some research has considered the joint optimization of smart reflective surface deployment, these studies typically only address idealized environments and are difficult to scale to in complex, dynamic, and multi-user environments. In more challenging scenarios, such as those involving multiple rooms, multiple wall obstructions, and mobile scenarios with uncertain or frequently changing user locations, efficient and cost-effective deployment and dynamic adjustment of smart reflective surfaces remains an urgent challenge. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a smart reflective surface deployment system based on three-dimensional vision, which realizes flexible optimization of the deployment position and number of smart reflective surfaces based on the indoor scenes where the transmitter and receiver are located.

[0007] The present invention adopts the following technical solutions to achieve the above-mentioned purpose. The present invention provides an intelligent reflective surface deployment system based on three-dimensional vision, comprising:

[0008] transmitter, receiver, coordinator, 3D reconstructor, signal strength simulator, deployment optimizer, and controller;

[0009] The coordinator sends an indication signal to the 3D reconstructor. Upon receiving the indication signal, the 3D reconstructor first performs a 3D visual scan and reconstruction of the indoor scene where the transmitter and receiver are located, and transmits the obtained 3D scene model to the signal strength simulator. At the same time, it sends a feedback signal to the coordinator, indicating that the 3D visual scan and reconstruction has been completed.

[0010] After receiving the feedback signal from the 3D reconstructor, the coordinator sends an indication signal to the signal strength simulator. After receiving the 3D scene model of the indoor environment and the indication signal from the coordinator, the signal strength simulator generates communication signal strength distribution data under the 3D scene model using a communication signal strength simulation algorithm. The communication signal strength distribution data is sent to the deployment optimizer and a feedback signal is sent to the coordinator at the same time, indicating that the communication signal strength simulation has been completed.

[0011] After receiving the feedback signal from the signal strength simulator, the coordinator sends an instruction signal to the deployment optimizer. After receiving the communication signal strength distribution data and the instruction signal from the coordinator, the deployment optimizer determines the optimal deployment location and number of smart reflective surfaces using the smart reflective surface deployment optimization algorithm, and sends the optimal deployment location and number of smart reflective surfaces to the controller.

[0012] The controller deploys the corresponding number of smart reflective surfaces to the corresponding positions. After the deployment is completed, it sends a feedback signal to the deployment optimizer. After receiving the feedback signal sent by the controller, the deployment optimizer sends a corresponding feedback signal to the coordinator, indicating that the smart reflective surfaces have been deployed to the corresponding positions.

[0013] Furthermore, generating the communication signal strength distribution data under the three-dimensional scene model by the communication signal strength simulation algorithm specifically includes:

[0014] Assume that the bottom surface of the 3D scene is parallel to plane, and in The projection area in the plane is , , then first place the three-dimensional scene in The projection area in the plane is divided into grids of equal size, and in each grid, randomly generate A fixed height and For each receiver location coordinate, the communication signal strength at that location is simulated by path tracing. After measuring the communication signal strength at each location, The average communication signal strength at each position is taken as the communication signal strength value of the grid, and all After obtaining the communication signal strength value of each grid, the communication signal strength distribution data under the corresponding three-dimensional scene model is obtained.

[0015] Furthermore, after receiving the communication signal strength distribution data and the instruction signal from the coordinator, the deployment optimizer determines the optimal deployment position of the smart reflective surface through the smart reflective surface deployment optimization algorithm, specifically including:

[0016] After receiving the communication signal strength distribution data and the instruction signal from the coordinator, the deployment optimizer first determines the signal blind area in the scene and makes For the corresponding grid The communication signal strength is , first determine a communication signal strength threshold , then compare the communication signal strength of all grids with the threshold to find Below threshold The grid is the signal blind area;

[0017] Determine the optimal deployment position of the intelligent reflective surface. The grid is the signal blind area, record the The coordinates of the center point of the grid are ,in , the average position of all grid center points is taken as the reference point of the blind area, and is the position coordinate of the reference point, where , , and note is the location coordinate of the transmitter, assuming that the reflective surface is deployed at , For any point on this line segment, the optimal placement of the smart reflective surface is determined based on the position coordinates of the transmitter and the signal blind spot reference point.

[0018] Furthermore, determining the optimal placement of the smart reflective surface based on the position coordinates of the transmitter and the signal blind zone reference point specifically includes:

[0019] First calculate the slope of the line connecting the transmitter and the blind zone reference point and the midpoint coordinates ,in , , , and then we get the equation of the line that passes through the midpoint of the line and is perpendicular to the line: , and finally substitute , you can get the y-axis coordinate at the optimal placement position: ,but It is determined to be the optimal deployment location for the smart reflective surface.

[0020] Furthermore, the number of smart reflective surfaces to be used is determined by the smart reflective surface deployment optimization algorithm, specifically including:

[0021] After determining the optimal deployment location for the smart reflector, deploy one smart reflector to the optimal location. Path tracing is then used to re-simulate the communication signal strength of all grid cells after the smart reflector is deployed and its beamforming is optimized.

[0022] Then, the communication signal strength of the signal blind area grid is compared with the previously determined threshold value. For comparison, if the communication signal strength of all signal blind spots is higher than the threshold, then deploy one smart reflective surface. Otherwise, gradually increase the number of smart reflective surfaces and repeat the path tracing, re-simulation, and comparison process until the communication signal strength of all blind spots is higher than the threshold. At this point, the number of deployed smart reflective surfaces is the number of smart reflective surfaces in use.

[0023] The beneficial effects of the present invention are:

[0024] The present invention performs three-dimensional visual scanning and reconstruction of the indoor scene where the transmitter and receiver are located, constructs a corresponding three-dimensional scene model, and uses path tracing technology to simulate and generate signal quality distribution. By analyzing the signal quality distribution, the system can accurately identify the location of signal coverage blind spots and then use a deployment optimization algorithm to simultaneously determine the optimal spatial position and quantity configuration of intelligent reflective surfaces. Based on three-dimensional visual reconstruction and the detection of signal blind spots in real-world scenarios, this deployment optimization method achieves flexible optimization of the placement and quantity configuration of intelligent reflective surfaces, so that the wireless signals reflected by the intelligent reflective surfaces are precisely aligned with the signal blind spots, achieving accurate signal coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a structural block diagram of a three-dimensional vision-based intelligent reflective surface deployment system provided by an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of dividing the grid into 24 grids provided by an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of 24 grid communication signal strength distribution data provided by an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of determining the optimal deployment position of the smart reflective surface provided by an embodiment of the present invention;

[0029] Figure 5 3 is a schematic diagram of communication signal strength of all grids after optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0031] The present invention provides an intelligent reflective surface deployment system based on three-dimensional vision, such as Figure 1 As shown, specifically including:

[0032] Transmitter, receiver, coordinator, 3D reconstructor, signal strength simulator, deployment optimizer, and controller.

[0033] The coordinator sends an instruction signal to the 3D reconstructor, indicating the start of the 3D visual scanning and reconstruction phase of the indoor scene;

[0034] After receiving the indication signal, the 3D reconstructor first performs a 3D visual scan and reconstruction of the indoor scene where the transmitter and receiver are located. This 3D visual scan reconstruction can be performed using a pure computer vision method based on Gaussian splattering or a method that combines computer vision with lidar data. The specific 3D visual scan reconstruction method is not limited. After the 3D visual scan reconstruction is completed, the 3D reconstructor transmits the resulting 3D scene model to the signal strength simulator and sends a feedback signal to the coordinator, indicating that the 3D visual scan reconstruction has been completed.

[0035] After receiving the feedback signal from the 3D reconstructor, the coordinator sends an instruction signal to the signal strength simulator, indicating that the simulation of the communication signal strength distribution in the 3D scene model should begin.

[0036] After receiving the 3D scene model of the indoor environment and the instruction signal from the coordinator, the signal strength simulator runs the communication signal strength simulation algorithm to generate the communication signal strength distribution in the scene. Then, the signal strength simulator sends the communication signal strength distribution data to the deployment optimizer and sends a feedback signal to the coordinator to indicate that the communication signal strength simulation has been completed.

[0037] After receiving the feedback signal from the signal strength simulator, the coordinator sends an instruction signal to the deployment optimizer, indicating that it should start determining the deployment location and number of smart reflective surfaces to be used.

[0038] After receiving the communication signal strength distribution data and the coordinator's instruction signal, the deployment optimizer runs the smart reflector deployment optimization algorithm to determine the optimal deployment location and number of smart reflectors. After the deployment optimization algorithm is completed, the deployment optimizer sends the optimal deployment location and number of smart reflectors to the controller.

[0039] After receiving the optimal deployment location and usage quantity data of the smart reflective surfaces from the deployment optimizer, the controller deploys the corresponding number of smart reflective surfaces to the corresponding locations and sends a feedback signal to the deployment optimizer after the deployment is completed.

[0040] After receiving the feedback signal from the controller, the deployment optimizer further sends a feedback signal to the coordinator, indicating that the smart reflective surface has been deployed to the corresponding position.

[0041] The communication signal strength simulation algorithm and the smart reflective surface deployment optimization algorithm are explained below.

[0042] Communication signal strength simulation algorithm:

[0043] Assume that the bottom surface of the 3D scene is parallel to plane, and in The projection area in the plane is , , then first place the three-dimensional scene in The projection area in the plane is divided into grids of equal size, and in each grid, randomly generate A fixed height and For each receiver location coordinate, the communication signal strength at that location is simulated by path tracing. There is no restriction on the platform and algorithm for running path tracing. After measuring the communication signal strength at each location, The average communication signal strength at each position is taken as the communication signal strength value of the grid. The above method is run on each grid in turn to obtain the communication signal strength distribution data under the corresponding three-dimensional scene model.

[0044] Intelligent reflective surface deployment optimization algorithm:

[0045] After receiving the communication signal strength distribution data and the instruction signal from the coordinator, the deployment optimizer first determines the signal blind area in the scene and makes For the corresponding grid The communication signal strength is , first determine a communication signal strength threshold , then compare the communication signal strength of all grids with the threshold to find Below threshold The grid is the signal blind area;

[0046] After determining which grids are blind spots, the next step is to determine the optimal deployment location of the smart reflective surface. The grid is the signal blind area, record the The coordinates of the center point of the grid are ,in , the average position of all grid center points is taken as the reference point of the blind area, and is the position coordinate of the reference point, where , , and note is the location coordinate of the transmitter, assuming that the reflective surface is deployed at , For any point on this line segment, the optimal placement of the smart reflector is determined based on the position coordinates of the transmitter and the signal blind spot reference point, that is, first calculate the slope of the line connecting the transmitter and the blind spot reference point. and the midpoint coordinates ,in , , , and then we get the equation of the line that passes through the midpoint of the line and is perpendicular to the line: , and finally substitute , you can get the y-axis coordinate at the optimal placement position: ,but It is determined to be the optimal deployment location for the smart reflective surface.

[0047] Determine the number of smart reflective surfaces to use:

[0048] After determining the optimal smart reflector deployment location, first deploy a smart reflector to the optimal deployment location. Then, using path tracing, re-simulate the communication signal strength of all grids after the smart reflector is deployed and the smart reflector beamforming is optimized. The specific smart reflector beamforming optimization method is not restricted.

[0049] Then, the communication signal strength of the signal blind area grid is compared with the previously determined threshold value. For comparison, if the communication signal strength of all signal blind spots is higher than the threshold, then only one smart reflective surface is deployed. Otherwise, gradually increase the number of smart reflective surfaces and repeat the above process until the communication signal strength of all blind spots is higher than the threshold. At this point, the number of deployed smart reflective surfaces is the number of smart reflective surfaces in use.

[0050] The present invention is described in detail below with reference to specific examples.

[0051] In this example, if Figure 2 As shown, the scene is divided into 24 grids, each grid size is 1 meter by 1 meter, and the communication signal strength distribution data obtained by the communication signal strength simulation algorithm is as follows Figure 3(units are dBm). First, determine the communication signal strength threshold, setting it to 10 dB below the average communication signal strength across all grids. The calculated average communication signal strength across the 24 grids is -64.6 dBm, so in this example, the threshold is -74.6 dBm.

[0052] By comparing the communication signal strength and threshold of all blind spots, we can find that grid numbers 1, 2, and 3 are blind spots. After determining the blind spots, calculate the position coordinates of the blind spot reference points, and the calculation results are: .like Figure 4 As shown, the transmitter's position coordinates are , so the coordinates of the midpoint of the line connecting the transmitter and the blind zone reference point can be calculated as , the slope of the line is -1.52, so the equation of the line passing through the midpoint of the line and perpendicular to the line is , substitute After that, the optimal deployment position of the intelligent reflective surface is obtained as .

[0053] After determining the optimal deployment position, the present invention simulates the deployment of intelligent reflective surfaces and optimizes the communication signal strength of all grids after beamforming through a communication signal strength simulation algorithm. The results are as follows: Figure 5 As shown in the figure, it can be found that the communication signal strength of all blind area grids is higher than the threshold of -74.6 dBm, indicating that deploying one smart reflective surface is sufficient to meet the requirements. Therefore, the number of smart reflective surfaces used is 1.

[0054] Compared to traditional solutions that don't consider the placement of smart reflective surfaces, the core advantage of this invention lies in its ability to flexibly configure the placement of smart reflective surfaces for different indoor scenarios, enabling them to better enhance communication signal strength in blind spots. This solution is particularly suitable for enhancing wireless network coverage in indoor communications, resolving the key bottleneck of traditional smart reflective surface technology, which lacks consideration of determining optimal deployment locations in real-world environments and hinders large-scale commercial adoption.

[0055] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. Intelligent reflective surface deployment system based on three-dimensional vision, characterized by: include: transmitter, receiver, coordinator, 3D reconstructor, signal strength simulator, deployment optimizer, and controller; The coordinator sends an indication signal to the 3D reconstructor. Upon receiving the indication signal, the 3D reconstructor first performs a 3D visual scan and reconstruction of the indoor scene where the transmitter and receiver are located, and transmits the obtained 3D scene model to the signal strength simulator. At the same time, it sends a feedback signal to the coordinator, indicating that the 3D visual scan and reconstruction has been completed. After receiving the feedback signal from the 3D reconstructor, the coordinator sends an indication signal to the signal strength simulator. After receiving the 3D scene model of the indoor environment and the indication signal from the coordinator, the signal strength simulator generates communication signal strength distribution data under the 3D scene model using a communication signal strength simulation algorithm. The communication signal strength distribution data is sent to the deployment optimizer and a feedback signal is sent to the coordinator at the same time, indicating that the communication signal strength simulation has been completed. After receiving the feedback signal from the signal strength simulator, the coordinator sends an instruction signal to the deployment optimizer. After receiving the communication signal strength distribution data and the instruction signal from the coordinator, the deployment optimizer determines the optimal deployment location and number of smart reflective surfaces using the smart reflective surface deployment optimization algorithm, and sends the optimal deployment location and number of smart reflective surfaces to the controller. The controller deploys the corresponding number of smart reflective surfaces to the corresponding positions. After the deployment is completed, it sends a feedback signal to the deployment optimizer. After receiving the feedback signal sent by the controller, the deployment optimizer sends a corresponding feedback signal to the coordinator, indicating that the smart reflective surfaces have been deployed to the corresponding positions.

2. The intelligent reflective surface deployment system based on three-dimensional vision according to claim 1, characterized in that: The communication signal strength distribution data generated under the three-dimensional scene model by the communication signal strength simulation algorithm specifically includes: Assume that the bottom surface of the 3D scene is parallel to plane, and in The projection area in the plane is , , then first place the three-dimensional scene in The projection area in the plane is divided into grids of equal size, and in each grid, randomly generate A fixed height and For each receiver location coordinate, the communication signal strength at that location is simulated by path tracing. After measuring the communication signal strength at each location, The average communication signal strength at each position is taken as the communication signal strength value of the grid, and all After obtaining the communication signal strength value of each grid, the communication signal strength distribution data under the corresponding three-dimensional scene model is obtained.

3. The intelligent reflective surface deployment system based on three-dimensional vision according to claim 2, characterized in that: After receiving the communication signal strength distribution data and the coordinator's instruction signal, the deployment optimizer uses the smart reflector deployment optimization algorithm to determine the optimal deployment location of the smart reflector. Specifically, the algorithm includes: After receiving the communication signal strength distribution data and the instruction signal from the coordinator, the deployment optimizer first determines the signal blind area in the scene and makes For the corresponding grid The communication signal strength is , first determine a communication signal strength threshold , then compare the communication signal strength of all grids with the threshold to find Below threshold The grid is the signal blind area; Determine the optimal deployment position of the intelligent reflective surface. The grid is the signal blind area, record the The coordinates of the center point of the grid are ,in , the average position of all grid center points is taken as the reference point of the blind area, and is the position coordinate of the reference point, where , , and note is the location coordinate of the transmitter, assuming that the reflective surface is deployed at , For any point on this line segment, the optimal placement of the smart reflective surface is determined based on the position coordinates of the transmitter and the signal blind spot reference point.

4. The intelligent reflective surface deployment system based on three-dimensional vision according to claim 3, characterized in that: The optimal placement of the smart reflective surface is determined based on the coordinates of the transmitter and the signal blind spot reference point, specifically including: First calculate the slope of the line connecting the transmitter and the blind zone reference point and the midpoint coordinates ,in , , , and then we get the equation of the line that passes through the midpoint of the line and is perpendicular to the line: , and finally substitute , you can get the y-axis coordinate at the optimal placement position: ,but It is determined to be the optimal deployment location for the smart reflective surface.

5. The intelligent reflective surface deployment system based on three-dimensional vision according to claim 3, characterized in that: The number of smart reflective surfaces to be used is determined through the smart reflective surface deployment optimization algorithm, specifically including: After determining the optimal deployment location for the smart reflector, deploy one smart reflector to the optimal location. Path tracing is then used to re-simulate the communication signal strength of all grid cells after the smart reflector is deployed and its beamforming is optimized. Then, the communication signal strength of the signal blind area grid is compared with the previously determined threshold value. For comparison, if the communication signal strength of all signal blind spots is higher than the threshold, then deploy one smart reflective surface. Otherwise, gradually increase the number of smart reflective surfaces and repeat the path tracing, re-simulation, and comparison process until the communication signal strength of all blind spots is higher than the threshold. At this point, the number of deployed smart reflective surfaces is the number of smart reflective surfaces in use.

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