An intelligent reflective surface deployment position optimization system

Abstract

The present invention relates to the field of intelligent reflective surface deployment position optimization, and specifically to an intelligent reflective surface deployment position optimization system. The solution includes: a channel estimator cooperates with a transmitter, a receiver, and an intelligent reflective surface to carry out wireless channel estimation, and then sends the obtained wireless channel information to a deployment optimizer. After receiving the instruction signal sent by the coordinator and the wireless channel information sent by the channel estimator, the deployment optimizer obtains the optimal deployment position of the intelligent reflective surface according to the deployment optimization algorithm in a single receiver scenario, and then sends the optimal deployment position of the intelligent reflective surface to the position controller corresponding to the intelligent reflective surface. After receiving the optimal deployment position of the corresponding intelligent reflective surface, the position controller generates a control signal for the driver, and controls the driver to move the intelligent reflective surface to the corresponding optimal deployment position. The present invention is suitable for optimizing the deployment position of intelligent reflective surfaces.

Description

Technical Field

[0001] The present invention relates to the field of intelligent reflective surface deployment position optimization, and in particular to an intelligent reflective surface deployment position optimization system. Background Art

[0002] In wireless communications, obstructions can significantly attenuate wireless signal strength, limiting coverage. While increasing the number of base stations can mitigate this issue, this also increases operating costs and energy consumption. Metasurface technology is an effective approach to addressing this problem. A smart reflective surface is a passive wireless device fabricated from electromagnetic metamaterials. It consists of a large number of low-cost passive reflective elements, each of which introduces a wireless signal propagation path (also known as a reflection channel) from the transmitter to the receiver. Each reflective element in a traditional smart reflective surface is equipped with a phase shifter, allowing each reflective element to not only introduce a reflection channel but also add a specific phase shift to the reflected wireless signal. By coordinating the phase shifts introduced by all reflective elements on the smart reflective surface (also known as passive beamforming), the smart reflective surface can enhance wireless signal coverage in a specific area.

[0003] Traditional phase shifter-based smart reflector technology has significant hardware cost and system complexity issues, which can be summarized as follows:

[0004] 1. Large number of phase shifters: Since each smart reflector typically has a large number of reflective units (usually hundreds or thousands), each unit requires an independent phase shifter and its supporting control circuitry, resulting in overall costs that increase linearly or even exponentially with scale.

[0005] 2. High-precision phase shifters are expensive: Traditional phase shifter-based smart reflectors typically use high-precision phase shifters (such as 6-bit or above, or even continuous phase shifters) to achieve high-precision beam steering. However, high-precision phase shifters are typically expensive to manufacture (especially in the millimeter wave / terahertz frequency bands).

[0006] 3. Control link redundancy: Each phase shifter requires an independent control signal and multiple DAC modules, which complicates wiring, increases power consumption, and increases design difficulty.

[0007] Furthermore, traditional phase-shifter-based smart reflector technology typically assumes that the reflector's position cannot be changed after deployment. Therefore, communication performance can only be improved by designing passive beamforming on the reflector. However, in certain scenarios (such as when there is obstruction between the reflector and the user), simply adjusting passive beamforming is difficult to achieve improved signal quality for users. These issues severely restrict the application and promotion of smart reflectors in affordable IoT and ultra-large-scale 6G networks. Summary of the Invention

[0008] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an intelligent reflector deployment position optimization system, which does not require each reflector unit to be equipped with a high-cost phase shifter, thereby significantly reducing hardware costs and system complexity, while greatly enhancing channel quality.

[0009] The present invention adopts the following technical solutions to achieve the above-mentioned purpose. The present invention provides a smart reflector deployment position optimization system, including a transmitter, a receiver, a channel estimator, a coordinator, a deployment optimizer, a smart reflector, a driver, and a position controller;

[0010] The coordinator sends an indication signal to the channel estimator. After receiving the indication signal, the channel estimator coordinates with the transmitter, receiver, and smart reflective surface to perform wireless channel estimation. The smart reflective surface is deployed on the building ceiling. After completing the channel estimation, the channel estimator sends the obtained wireless channel information to the deployment optimizer and sends a feedback signal to the coordinator. After receiving the feedback signal from the channel estimator, the coordinator sends an indication signal to the deployment optimizer.

[0011] After receiving the indication signal sent by the coordinator and the wireless channel information sent by the channel estimator, the deployment optimizer obtains the optimal deployment position of the smart reflective surface based on the deployment optimization algorithm in the single-receiver scenario, and then sends the optimal deployment position of the smart reflective surface to the position controller corresponding to the smart reflective surface. After receiving the optimal deployment position of the corresponding smart reflective surface, the position controller generates a control signal for the driver to control the driver to move the smart reflective surface to the corresponding optimal deployment position.

[0012] Furthermore, the deployment optimization algorithm in the single-receiver scenario specifically includes:

[0013] Divide the ceiling into There are independent units, and in each unit there are discrete candidate locations for deploying reflective surfaces, and the set of all units is , remember the smart reflective surface For deployment in The reflecting surface within the unit is recorded as The set of all candidate positions within a unit is , the wireless channel information fed back by the channel estimator to the deployment optimizer is the direct channel from the transmitter to the receiver And when the smart reflective surface Deployed in the unit The reflection channel introduced when the , and all Are all plural, let Is a binary variable used to represent the smart reflective surface Whether deployed in location On, remember For one A matrix of size, No. The elements are ,remember for No. The vector composed of rows, that is, The optimization problem is modeled as follows:

[0014] ;

[0015] Then by introducing continuous auxiliary variables satisfy , define a new objective function :

[0016] ;

[0017] The optimization problem is equivalently converted to:

[0018] ;

[0019] when When fixed, the problem is decomposed into multiple sub-problems based on the optimization of a single intelligent reflective surface deployment, each sub-problem depends only on , at this time each The optimal solution is determined to be:

[0020] , , is an indicator variable in a single-receiver scenario, indicating the label of the optimal placement position of the m-th smart reflector;

[0021] The optimal solution As a about The function is recorded as , then both will be optimized Switch to optimize only :

[0022] ;

[0023] The ultimate goal is to find the The optimal value of , to maximize , is a take A matrix of size Rank Column is ;

[0024] when When moving on the unit circle, the optimal Depends on the counterclockwise rotation angle The channel with the largest real axis projection in the complex plane , all the solutions that make the same Constitute an arc on the unit circle;

[0025] By calculating the transition points on the unit circle, each arc segment is indirectly determined. and The transition points between the corresponding arcs are determined by the following equation:

[0026] ;

[0027] For all and all Solving the equation in combination yields equations, corresponding to a total solutions, the solution of the equation divides the entire unit circle into at most arc, the solution of the equation contains all the transition points. When moving in each arc, each The optimal solution remains unchanged, and only the midpoint of each arc needs to be taken. As a test point, find out which arc corresponds to is optimal, then This is the global optimal solution corresponding to the optimal deployment position of the smart reflective surface.

[0028] Furthermore, if the number of receivers in the smart reflective surface deployment position optimization system is at least 2, the optimal deployment position of each smart reflective surface is obtained according to the deployment optimization algorithm in a multi-receiver scenario.

[0029] Furthermore, the deployment optimization algorithm in the multi-receiver scenario specifically includes:

[0030] In a multi-receiver scenario, the goal becomes to maximize the worst signal-to-interference-and-noise ratio among all receivers:

[0031] ;

[0032] No. The signal-to-interference-and-noise ratio of a receiver is defined as:

[0033] ;

[0034] Where, is the transmitter’s transmission power, is the background noise power, For the The signal-to-interference-and-noise ratio of a receiver, From the transmitter to the The direct channel of the receiver, As a smart reflective surface Deployed in location Arrival reflection channels of the receivers;

[0035] A voting-based approach is used to determine the deployment location of the smart reflector. First, each receiver is treated as a target user and the deployment optimization under the single-receiver scenario is performed separately. To be the first The operating results when a receiver is used as the target user are: for No. Line, then collect all The final optimization result is determined by which position receives the most receiver votes. :

[0036] , ;

[0037] Where U represents the number of all receivers, Indicates an indicator variable in a multi-receiver scenario, indicating the label of the optimal placement position corresponding to the m-th smart reflector.

[0038] The beneficial effects of the present invention are:

[0039] The present invention deploys smart reflective surfaces on the ceiling of a building, which ensures that the signal propagation path between each reflective surface and the user will not be blocked. The position of each smart reflective surface on the ceiling can be moved, and the reflection channel can be dynamically adjusted by adjusting the positions of all reflective surfaces to achieve enhanced wireless communication. Specifically, by adjusting the position of the reflective surface, the signal beam target can be flexibly optimized, thereby improving the signal coverage range and enhancing the channel quality. Compared with traditional smart reflective surface technology based on phase shifters, the core advantage of this solution is that there is no need to equip each reflective unit with a high-cost phase shifter, thereby significantly reducing hardware costs and system complexity. This solution is particularly suitable for enhancing wireless network coverage in indoor communications, and solves the key bottleneck of traditional smart reflective surface technology that is difficult to commercialize on a large scale due to the high cost of phase shifters and complex control. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a structural block diagram of an intelligent reflective surface deployment position optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, 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.

[0042] The present invention provides a system for optimizing the deployment position of intelligent reflective surfaces. Figure 1 As shown, it includes a transmitter, a receiver, a channel estimator, a coordinator, a deployment optimizer, a smart reflective surface, a driver, and a position controller. The smart reflective surface is deployed on the ceiling of the building and the deployment position of each smart reflective surface is initialized.

[0043] The coordinator sends an indication signal to the channel estimator, indicating the start of the channel estimation phase;

[0044] After receiving the indication signal, the channel estimator coordinates with the transmitter, receiver, and smart reflector to perform wireless channel estimation. Wireless channel estimation can be performed using traditional numerical optimization methods or neural network-based methods. The specific channel estimation algorithm is not required. After completing the channel estimation, the channel estimator sends the estimated wireless channel information to the deployment optimizer and sends a feedback signal to the coordinator to indicate that the channel estimation is complete.

[0045] After receiving the feedback signal from the channel estimator, the coordinator sends an instruction signal to the deployment optimizer, indicating that the deployment optimization of the smart reflective surface should be started;

[0046] After receiving the indication signal sent by the coordinator and the wireless channel information sent by the channel estimator, in a single-receiver scenario, the optimal deployment position of each smart reflective surface is obtained according to the deployment optimization algorithm in the single-receiver scenario. In a multi-receiver scenario, the optimal deployment position of each smart reflective surface is obtained according to the deployment optimization algorithm in the multi-receiver scenario. The deployment position of the smart reflective surface is then sent to the position controller corresponding to each smart reflective surface;

[0047] After receiving the optimal deployment position of the corresponding smart reflective surface, the position controller generates a control signal for the actuator, thereby instructing the actuator to move the smart reflective surface to the corresponding position. After completion, it sends a feedback signal to the deployment optimizer, indicating that the smart reflective surface has been deployed to the corresponding position;

[0048] After receiving feedback from all position controllers, the deployment optimizer sends a feedback signal to the coordinator, indicating that all smart reflective surfaces have been deployed to the optimal position.

[0049] Deployment optimization algorithm for single receiver scenario:

[0050] Divide the ceiling into There are independent units, and in each unit there are discrete candidate locations for deploying reflective surfaces, and the set of all units is , remember the smart reflective surface For deployment in The reflecting surface within the unit is recorded as The set of all candidate positions within a unit is , the wireless channel information fed back by the channel estimator to the deployment optimizer is the direct channel from the transmitter to the receiver And when the smart reflective surface Deployed in the unit The reflection channel introduced when the , and all Are all plural, let Is a binary variable used to represent the smart reflective surface Whether deployed in location On, remember For one A matrix of size, No. The elements are ,remember for No. The vector composed of rows, that is, The optimization problem is modeled as follows:

[0051] ;

[0052] Then by introducing continuous auxiliary variables satisfy , define a new objective function :

[0053] ;

[0054] Then the optimization problem is equivalently converted to:

[0055] ;

[0056] when When fixed, the problem is decomposed into a series of sub-problems based on the optimization of a single intelligent reflective surface deployment, each sub-problem depends only on , at this time each The optimal solution is determined to be:

[0057] , , is an indicator variable in a single-receiver scenario, indicating the label of the optimal placement position of the m-th smart reflector.

[0058] Therefore, the optimal solution can be As a about The function of , then both will be optimized Switch to optimize only :

[0059] , is a take A matrix of size Rank Column is ;

[0060] The ultimate goal is to find the The optimal value of , to maximize ;

[0061] when When moving on the unit circle, the optimal Depends on the counterclockwise rotation angle The channel with the largest real axis projection in the complex plane , all the solutions that make the same Construct an arc on the unit circle , and prove that for each smart reflective surface , at most you only need to consider Therefore, to find the global optimal , you just need to know which arc Contains .

[0062] Compared to directly calculating each arc The present invention indirectly determines each arc segment by calculating the transition point on the unit circle, that is, whenever After a transition point, the optimal will change. and The transition points between the corresponding arcs are determined by the following equation:

[0063] ;

[0064] This equation has two solutions, as well as ,in is a complex variable representing the transition point, which means and There are two transition points between corresponding arcs.

[0065] The present invention is for all and all Solving the equation in combination yields equations, corresponding to a total solutions, sort them in counterclockwise order:

[0066] ;

[0067] The solution of this equation divides the entire unit circle into at most arcs (may have the same points), the solution of the equation contains all the transition points, when When moving in each arc, each The optimal solution remains unchanged, and only the midpoint of each arc needs to be taken. As a test point, find out which arc corresponds to is optimal, then This is the global optimal solution corresponding to the optimal deployment position of the smart reflective surface.

[0068] The following describes the deployment optimization algorithm for a single receiver scenario with specific data.

[0069] This example divides the ceiling into units, each unit has Alternative deployment locations. The direct channel fed back by the channel estimator ,All feedback channel information is summarized in Table 1.

[0070] Table 1 Channel information in the example

[0071]

[0072] for , we first find all possible pairings Solving equations , and we get the following 6 solutions:

[0073] ;

[0074] Similarly, for , we can also get 6 solutions:

[0075] ;

[0076] Sorting the above solutions, we get:

[0077] ;

[0078] The above points divide the unit circle into 12 Arc, take each The midpoint of the arc is used as the test point, according to the formula Calculate the corresponding and function values The calculation results are shown in Table 2 and Table 3.

[0079] Table 2 Calculation results I

[0080]

[0081] Table 3 Calculation results II

[0082]

[0083] From the above table, we can see that the 12th Function value corresponding to the arc Maximum, so we can conclude that:

[0084] It is the optimal deployment solution for smart reflective surfaces.

[0085] Deployment optimization algorithm in multi-receiver scenario:

[0086] If there are a total of In a multi-receiver scenario, the goal becomes to maximize the worst signal-to-interference-and-noise ratio (SNR) among all receivers:

[0087] ;

[0088] No. The signal-to-interference-and-noise ratio of a receiver is defined as:

[0089] ;

[0090] Where, is the transmitter’s transmission power, is the background noise power, From the transmitter to the The direct channel of the receiver, As a smart reflective surface Deployed in location Arrival reflection channels of the receivers;

[0091] In a multi-receiver scenario, the present invention uses a voting-based approach to determine the deployment location of each smart reflector. First, each receiver is treated as a target user and the deployment optimization algorithm for a single-receiver scenario is run separately. To be the first The operating results when a receiver is used as the target user are recorded. for No. Then the present invention uses the majority rule to eliminate the differences. Specifically, all The final optimization result is determined by which position receives the most receiver votes. :

[0092] , , Indicates an indicator variable in a multi-receiver scenario, indicating the label of the optimal placement position corresponding to the m-th smart reflector.

[0093] 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. An intelligent reflective surface deployment location optimization system, characterized in that: Includes a transmitter, a receiver, a channel estimator, a coordinator, a deployment optimizer, a smart reflective surface, a driver, and a position controller; The coordinator sends an indication signal to the channel estimator. After receiving the indication signal, the channel estimator coordinates with the transmitter, receiver, and smart reflective surface to perform wireless channel estimation. The smart reflective surface is deployed on the building ceiling. After completing the channel estimation, the channel estimator sends the obtained wireless channel information to the deployment optimizer and sends a feedback signal to the coordinator. After receiving the feedback signal from the channel estimator, the coordinator sends an indication signal to the deployment optimizer. After receiving the indication signal sent by the coordinator and the wireless channel information sent by the channel estimator, the deployment optimizer obtains the optimal deployment position of the smart reflective surface based on the deployment optimization algorithm in the single-receiver scenario, and then sends the optimal deployment position of the smart reflective surface to the position controller corresponding to the smart reflective surface. After receiving the optimal deployment position of the corresponding smart reflective surface, the position controller generates a control signal for the driver to control the driver to move the smart reflective surface to the corresponding optimal deployment position.

2. The intelligent reflective surface deployment position optimization system according to claim 1, characterized in that: The deployment optimization algorithm for a single receiver scenario specifically includes: Divide the ceiling into There are independent units, and in each unit there are discrete candidate locations for deploying reflective surfaces, and the set of all units is , remember the smart reflective surface For deployment in The reflecting surface within the unit is recorded as The set of all candidate positions within a unit is , the wireless channel information fed back by the channel estimator to the deployment optimizer is the direct channel from the transmitter to the receiver And when the smart reflective surface Deployed in the unit The reflection channel introduced when the , and all Are all plural, let Is a binary variable used to represent the smart reflective surface Whether deployed in location On, remember For one A matrix of size, No. Rank The column elements are ,remember for No. The vector composed of rows, that is, A vector of all variables related to the deployment of a smart reflective surface; Every one The optimal solution is calculated as follows: , , is an indicator variable in a single-receiver scenario, indicating the label of the optimal placement position of the m-th smart reflector. is the introduced continuous auxiliary variable; The optimal solution As a about The function of , then both will be optimized Switch to optimize only : ； The ultimate goal is to find the The optimal value of , to maximize , is a take A matrix of size Rank Column is ; when When moving on the unit circle, the optimal Depends on the counterclockwise rotation angle The channel with the largest real axis projection in the complex plane , all the solutions that make the same Constitute an arc on the unit circle; By calculating the transition points on the unit circle, each arc segment is indirectly determined. and The transition points between the corresponding arcs are determined by the following equation: , complex variables representing transition points; For all and all Solving the equation in combination yields equations, corresponding to a total solutions, the solution of the equation divides the entire unit circle into at most arc, the solution of the equation contains all the transition points. When moving in each arc, each The optimal solution remains unchanged, and only the midpoint of each arc needs to be taken. As a test point, find out which arc corresponds to is optimal, then This is the global optimal solution corresponding to the optimal deployment position of the smart reflective surface.

3. The intelligent reflective surface deployment position optimization system according to claim 2, characterized in that: If the number of receivers in the smart reflector deployment position optimization system is at least 2, the optimal deployment position of each smart reflector is obtained according to the deployment optimization algorithm in the multi-receiver scenario.

4. The intelligent reflective surface deployment position optimization system according to claim 3, characterized in that: The deployment optimization algorithm for multi-receiver scenarios specifically includes: In a multi-receiver scenario, the goal becomes to maximize the worst signal-to-interference-and-noise ratio among all receivers: ； No. The signal-to-interference-and-noise ratio of a receiver is defined as: ； Where, is the transmitter’s transmission power, is the background noise power, For the The signal-to-interference-and-noise ratio of a receiver, From the transmitter to the The direct channel of the receiver, As a smart reflective surface Deployed in location Arrival reflection channels of the receivers; A voting-based approach is used to determine the deployment location of the smart reflector. First, each receiver is treated as a target user and the deployment optimization under the single-receiver scenario is performed separately. To be the first The operating results when a receiver is used as the target user are: for No. Line, then collect all The final optimization result is determined by which position receives the most receiver votes. : ， ； Where U represents the number of all receivers, Indicates an indicator variable in a multi-receiver scenario, indicating the label of the optimal placement position corresponding to the m-th smart reflector.

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