Intelligent reflector deployment position optimization system

Abstract

The invention relates to the field of intelligent reflecting surface deployment position optimization, in particular to an intelligent reflecting surface deployment position optimization system. According to the scheme, a channel estimator cooperates with a transmitter, a receiver and an intelligent reflecting surface to carry out wireless channel estimation, then obtained wireless channel information is sent to a deployment optimizer, and after the deployment optimizer receives an indication signal sent by a coordinator and the wireless channel information sent by the channel estimator, the wireless channel information is sent to the coordinator. The optimal deployment position of the intelligent reflecting surface is obtained according to a deployment optimization algorithm in a single-receiver scene, then the optimal deployment position of the intelligent reflecting surface is sent to a position controller corresponding to the intelligent reflecting surface, and after the position controller receives the optimal deployment position of the corresponding intelligent reflecting surface, a control signal of a driver is generated, and the driver is driven to control the intelligent reflecting surface according to the control signal. And the driver is controlled to move the intelligent reflecting surface to a corresponding optimal deployment position. The method is suitable for optimizing the deployment position of the intelligent reflecting surface.

Description

Technical Field

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

[0002] In wireless communication, obstacle occlusion will significantly attenuate the strength of wireless signals, thereby limiting the coverage range of wireless signals. Although this problem can be alleviated by increasing the number of base stations, this will also lead to an increase in operating costs and energy consumption. An effective way to solve the above problems is the metasurface technology. An intelligent reflecting surface is a passive and wireless device manufactured by electromagnetic metamaterials. It consists of a large number of low-cost passive and wireless reflecting units. Each reflecting unit can introduce a wireless signal propagation path (also called a reflection channel) from the transmitter, reflected by the reflecting unit, and finally reaching the receiver. Each reflecting unit of the traditional intelligent reflecting surface is equipped with a phase shifter, so that in addition to introducing a reflection channel, each reflecting unit can also add a specific phase shift value to the reflected wireless signal. By coordinating the phase shift values introduced by all reflecting units on the intelligent reflecting surface (also called passive beamforming), the intelligent reflecting surface can enhance the wireless signal coverage of a specific area.

[0003] The traditional phase shifter-based intelligent reflecting surface technology has significant problems in terms of hardware cost and system complexity, which are mainly summarized as follows: 1. A large number of phase shifters: Since each intelligent reflecting surface usually has a large number of reflecting units (usually hundreds or thousands), each unit needs to be equipped with an independent phase shifter and its supporting control circuit, resulting in the overall cost increasing linearly or even exponentially with the scale; 2. High-precision phase shifters are costly: The traditional phase shifter-based intelligent reflecting surface usually uses high-precision phase shifters (such as more than 6-bit or even continuous phase shifters) to achieve high-precision beam control, and high-precision phase shifters usually have high manufacturing costs (especially in the millimeter wave / terahertz frequency bands).

[0004] 3. Control link redundancy: Each phase shifter requires an independent control signal and multiple DAC modules, resulting in complex wiring, increased power consumption, and design difficulty.

[0005] In addition, the traditional phase shifter-based intelligent reflecting surface technology usually assumes that the position of the intelligent reflecting surface cannot be changed after deployment, and can only improve the communication performance by designing the passive beamforming of the reflecting surface. In some scenarios (such as when there is occlusion between the reflecting surface and the user), it is very difficult to improve the quality of the received signal by adjusting the passive beamforming alone. These problems seriously restrict the application and popularization of intelligent reflecting surfaces in inclusive scenarios such as low-cost Internet of Things and ultra-large-scale 6G networks. Summary of the Invention

[0006] The object of the present invention is to overcome the shortcomings of the prior art and provide an intelligent reflecting surface deployment position optimization system, which does not require high-cost phase shifters for each reflecting unit, thus significantly reducing the hardware cost and system complexity, and greatly enhancing the channel quality at the same time.

[0007] The present invention adopts the following technical solutions to achieve the above object. The present invention provides an intelligent reflecting surface deployment position optimization system, including a transmitter, a receiver, a channel estimator, a coordinator, a deployment optimizer, an intelligent reflecting 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 cooperates with the transmitter, the receiver, and the intelligent reflecting surface to carry out wireless channel estimation. The intelligent reflecting surface is deployed on the ceiling of the building. 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 intelligent reflecting surface according to the deployment optimization algorithm in the single-receiver scenario, and then sends the optimal deployment position of the intelligent reflecting surface to the position controller corresponding to the intelligent reflecting surface. After receiving the optimal deployment position of the corresponding intelligent reflecting surface, the position controller generates a control signal for the driver to control the driver to move the intelligent reflecting surface to the corresponding optimal deployment position.

[0008] Furthermore, the deployment optimization algorithm in the single-receiver scenario specifically includes: Divide the ceiling into mutually independent units. There are a total of discrete alternative positions for deploying the reflecting surface in each unit. Denote the set of all units as , denote the intelligent reflecting surface as the reflecting surface deployed in the th unit. Denote the set of all alternative positions in the th unit as . 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 the reflected channel introduced when the intelligent reflecting surface is deployed at the th position in the unit. And all are complex numbers. Let be a binary variable used to represent whether the intelligent reflecting surface is deployed at the position Let be a matrix of size . The -th element of is . Let be the vector formed by the -th row of , that is, the vector consisting of all variables related to the deployment of the -th intelligent reflecting surface. The goal is to maximize the overall channel strength from the transmitter to the receiver, so the optimization problem is modeled as: ; Subsequently, by introducing a continuous auxiliary variable satisfying , a new objective function is defined as: ; Then the optimization problem is equivalently transformed into: ; When is fixed, the problem is decomposed into multiple sub-problems based on the optimization of a single intelligent reflecting surface deployment. Each sub-problem only depends on . At this time, the optimal solution of each is determined as: , , , where is an indicator variable representing the label of the optimal placement position of the -th intelligent reflecting surface; Taking the optimal solution as a function of , denoted as , then optimizing simultaneously is transformed into only optimizing : ; is a matrix of size by . Its -th row and -th column is ; When moves on the unit circle, the optimal depends on the channel with the largest real-axis projection in the complex plane after counterclockwise rotation by an angle . , all solutions that are the same constitute an arc on the unit circle ; By calculating the transition points on the unit circle to indirectly determine each arc. For and the transition points between the corresponding arcs, they are determined by the following equation: ; For all and all combinations, solve this equation. Thus, a total of equations are generated, corresponding to a total of solutions. The solutions of the equations divide the entire unit circle into at most arcs, each arc being arc. The solutions of the equations contain all the transition points. Each arc contains at least one arc. When moves within each arc, the optimal solution for each remains unchanged. Only need to take the midpoint of each arc as the test point to find out which arc segment corresponds to being the optimal one. Then is the global optimal solution corresponding to the optimal deployment position of the intelligent reflecting surface.

[0009] Furthermore, if the number of receivers in the intelligent reflecting surface deployment position optimization system is at least 2, then the optimal deployment positions of each intelligent reflecting surface are obtained according to the deployment optimization algorithm in the multi-receiver scenario.

[0010] Furthermore, the deployment optimization algorithm in the multi-receiver scenario specifically includes: In the multi-receiver scenario, the goal becomes to maximize the worst signal-to-interference-plus-noise ratio among all receivers: ; The signal-to-interference-plus-noise ratio of the th receiver is defined as: ; In the formula, is the transmission power of the transmitter, is the background noise power, is the signal-to-interference-plus-noise ratio of the th receiver, is the direct channel from the transmitter to the th receiver, is when the intelligent reflecting surface Deployed at the location when introducing the reflected channel reaching the th receiver; A voting-based method is adopted to determine the deployment location of the intelligent reflecting surface. First, each receiver is used as the target user to separately run the deployment optimization in the single-receiver scenario. Denote as the operation result when the th receiver is used as the target user, and is the row. Then, collect all and determine the final optimization result by which location receives the most receiver votes , ; In the formula, U represents the number of all receivers.

[0011] The beneficial effects of the present invention are as follows: The present invention deploys the intelligent reflecting surface on the ceiling of the building, which can ensure that there is no situation where the signal propagation path between each reflecting surface and the user is blocked. The position of each intelligent reflecting surface on the ceiling can be moved, and the reflected channel is dynamically adjusted by adjusting the positions of all reflecting surfaces to achieve enhanced wireless communication. Specifically, by adjusting the positions of the reflecting surfaces, the signal beam target can be flexibly optimized, thereby improving the signal coverage range and enhancing the channel quality. Compared with the traditional intelligent reflecting surface technology based on phase shifters, the core advantage of this solution is that there is no need to equip each reflecting unit with a high-cost phase shifter, thus significantly reducing the hardware cost and system complexity. This solution is particularly suitable for enhancing the wireless network coverage in indoor communication, and solves the key bottleneck that the traditional intelligent reflecting surface technology is difficult to be commercially used on a large scale due to the high cost of phase shifters and complex control. Description of the Drawings

[0012] Figure 1 is a block diagram of the system structure for optimizing the deployment position of an intelligent reflecting surface provided by an embodiment of the present invention. Detailed Embodiments

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0014] The present invention provides a system for optimizing the deployment position of an intelligent reflecting surface. As Figure 1 shown, it includes a transmitter, a receiver, a channel estimator, a coordinator, a deployment optimizer, an intelligent reflecting surface, a driver, and a position controller. The intelligent reflecting surface is deployed on the ceiling of the building and the deployment positions of all intelligent reflecting surfaces are initialized.

[0015] The coordinator sends an indication signal to the channel estimator, indicating the start of the channel estimation phase; After receiving the indication signal, the channel estimator collaborates with the transmitter, receiver, and intelligent reflecting surface to conduct wireless channel estimation. The wireless channel estimation can be carried out by methods based on traditional numerical optimization or neural networks. Specific channel estimation algorithms are 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, indicating that the channel estimation has been completed; After receiving the feedback signal from the channel estimator, the coordinator sends an indication signal to the deployment optimizer, indicating the start of the deployment optimization of the intelligent reflecting surface; After receiving the indication signal sent by the coordinator and the wireless channel information sent by the channel estimator, in the single-receiver scenario, the optimal deployment positions of each intelligent reflecting surface are obtained according to the deployment optimization algorithm for the single-receiver scenario. In the multi-receiver scenario, the optimal deployment positions of each intelligent reflecting surface are obtained according to the deployment optimization algorithm for the multi-receiver scenario. Subsequently, the deployment positions of the intelligent reflecting surfaces are sent to the corresponding position controllers of each intelligent reflecting surface; After receiving the optimal deployment position of the corresponding intelligent reflecting surface, the position controller generates a control signal for the driver, thereby instructing the driver to move the intelligent reflecting surface to the corresponding position. After completion, a feedback signal is sent to the deployment optimizer, indicating that the intelligent reflecting surface has been deployed to the corresponding position; After receiving the feedback from all position controllers, the deployment optimizer sends a feedback signal to the coordinator, indicating that all intelligent reflecting surfaces have been deployed to the optimal positions.

[0016] Deployment optimization algorithm for single-receiver scenario: Divide the ceiling into mutually independent units. There are a total of discrete alternative positions for deploying the reflecting surface in each unit. Denote the set of all units as , denote the intelligent reflecting surface as the reflecting surface deployed in the th unit. Denote the set of all alternative positions in the th unit as . 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 the reflected channel introduced when the intelligent reflecting surface is deployed at the th position in the unit. And all are complex numbers. Let be a binary variable used to represent the intelligent reflecting surface Whether it is deployed at the location is denoted as a matrix of size . The -th element of is , denoted as being -th row of , that is, the vector composed of all variables related to the deployment of the -th intelligent reflecting surface. The goal is to maximize the overall channel strength from the transmitter to the receiver, and then the optimization problem is modeled as: ; Subsequently, by introducing a continuous auxiliary variable satisfying , a new objective function is defined as: ; Then the optimization problem is equivalently transformed into: ; When is fixed, the problem is decomposed into a series of sub-problems based on the optimization of the deployment of a single intelligent reflecting surface. Each sub-problem only depends on . At this time, the optimal solution of each is determined as: , , is an indicator variable representing the label of the optimal placement position of the

[0017] -th intelligent reflecting surface. Therefore, the optimal solution can be regarded as a function of , denoted as . Then, optimizing simultaneously is transformed into only optimizing : is a by matrix. Its -th row and -th column is ; Then the final goal is to find the optimal value of on the unit circle, denoted as , to maximize ; When moves 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 those that make the solutions the same constitute an arc on the unit circle , and it is proved that for each intelligent reflecting surface , at most only need to consider such candidate arcs. Therefore, to find the global optimal , only need to know which arc contains .

[0018] Compared with directly calculating each arc , the present invention indirectly determines each arc by calculating the transition points on the unit circle, that is, whenever passes through a transition point, the optimal will change. For the transition points between the arcs corresponding to and , they are determined by the following equation: ; This equation has two solutions, which are and .

[0019] The present invention solves this equation for all and all combinations, thus generating a total of equations, corresponding to a total of solutions. These solutions are sorted in the counterclockwise direction: ; The solutions of this equation divide the entire unit circle into at most arcs (there may be the same points). Each arc is arc. The solutions of the equation contain all the transition points. Each arc contains at least one arc. When moves within each arc, the optimal solution of each remains unchanged. Only need to take the midpoint of each arc as the test point to find out which arc segment corresponds to the optimal , then is the global optimal solution of the optimal deployment position of the corresponding intelligent reflecting surface.

[0020] The following combines specific data to illustrate the deployment optimization algorithm in the single-receiver scenario.

[0021] This example divides the ceiling into units, and there are alternative deployment locations in each unit. The direct channel fed back by the channel estimator, and all the feedback channel information is summarized in Table 1.

[0022] Table 1 Channel Information in the Example For , we first find all possible pairings and solve the equation , obtaining the following 6 solutions: ; Similarly, for , we can also obtain 6 solutions: ; Sorting the above solutions gives: ; The above points divide the unit circle into 12 circular arcs. Taking the midpoint of each circular arc as the test point, according to the formula calculate the corresponding to each circular arc and the function value . The calculation results are shown in Tables 2 and 3.

[0023] Table 2 Calculation Results I Table 3 Calculation Results II As can be seen from the above table, the function value corresponding to the 12th circular arc is the largest, so it can be concluded that: is the optimal intelligent reflecting surface deployment scheme.

[0024] Deployment Optimization Algorithm in the Multi-Receiver Scenario: If there are receivers in the system, in the multi-receiver scenario, the goal becomes to maximize the worst signal-to-interference-plus-noise ratio (SNR) among all receivers: ; The signal-to-interference-plus-noise ratio of the th receiver is defined as: ; In the formula, $P_T$ is the transmission power of the transmitter, $P_{n}$ is the background noise power, $h_{i}$ is the direct channel from the transmitter to the $i$-th receiver, $g_{i}( \theta )$ is the reflected channel to the $i$-th receiver introduced when the IRS is deployed at location $\theta$; In the multi-receiver scenario, the present invention adopts a voting-based method to determine the deployment locations of the IRSs. First, each receiver is regarded as a target user and the deployment optimization algorithm in the single-receiver scenario is run separately. Denote $r_{i}$ as the running result when the $i$-th receiver is regarded as the target user, and denote $v_{i}( \theta )$ as the $\theta$-th row. Subsequently, the present invention uses the voting rule of the minority obeying the majority to eliminate the differences. Specifically, collect all $v_{i}( \theta )$, and determine the final optimization result $\hat{\theta}$ by the location that receives the most receiver votes: $\hat{\theta}=\arg \max \limits_{\theta}\sum_{i = 1}^{K}v_{i}( \theta )$, $\theta \in \varTheta$.

[0025] The above are only the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the concept described herein through the above teachings or the techniques or knowledge in related fields. And the changes and variations made by those skilled in the art without departing from the spirit and scope of the present invention should all be within the protection scope of the appended claims of the present invention.

Claims

1. An intelligent reflecting surface deployment location optimization system, characterized in that It includes a transmitter, a receiver, a channel estimator, a coordinator, a deployment optimizer, an intelligent reflecting 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 cooperates with the transmitter, the receiver, and the intelligent reflecting surface to perform wireless channel estimation. The intelligent reflecting surface is deployed on the ceiling of the building. 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 intelligent reflecting surface according to the deployment optimization algorithm in the single-receiver scenario, and then sends the optimal deployment position of the intelligent reflecting surface to the position controller corresponding to the intelligent reflecting surface. After receiving the optimal deployment position of the corresponding intelligent reflecting surface, the position controller generates a control signal for the driver to control the driver to move the intelligent reflecting surface to the corresponding optimal deployment position.

2. The intelligent reflecting surface deployment location optimization system according to claim 1, wherein The deployment optimization algorithm in the single-receiver scenario specifically includes: Divide the ceiling into There are a total of 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 position , 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. Line 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; Each The optimal solution calculation method is as follows: , , is an indicator variable representing the label of the optimal placement position corresponding to the m-th intelligent reflecting surface. is the introduced continuous auxiliary variable; Take the optimal solution as a function of , denoted as , then the simultaneous optimization of is transformed into the optimization of only : ； The ultimate goal is then to find the optimal value on the unit circle denoted as to maximize , is a by matrix, and its th row and th column is ; When moving on the unit circle, the optimal depends on the channel with the largest real-axis projection in the complex plane after counterclockwise rotation by an angle ; all those that result in the same solution form an arc on the unit circle ; ; Indirectly determine each arc by calculating the transition points on the unit circle. For the transition points between the arcs corresponding to and , they are determined by the following equation: ； For all and all combinations to solve the equation, a total of equations are thus generated, corresponding to a total of solutions. The solutions of the equations divide the entire unit circle into at most circular arcs, each circular arc being a circular arc. The solutions of the equations include all the transition points. Each circular arc contains at least one circular arc. When moving within each circular arc, the optimal solution for each remains unchanged. Only by taking the midpoint of each circular arc as the test point and finding out which arc segment corresponds to the optimal one, then is the global optimal solution corresponding to the optimal deployment position of the intelligent reflecting surface.

3. The intelligent reflecting surface deployment location optimization system according to claim 2, wherein If the number of receivers in the intelligent reflecting surface deployment position optimization system is at least 2, the optimal deployment positions of each intelligent reflecting surface are obtained according to the deployment optimization algorithm in the multi-receiver scenario.

4. The intelligent reflecting surface deployment position optimization system according to claim 3, wherein The deployment optimization algorithm in the multi-receiver scenario specifically includes: In the multi-receiver scenario, the goal becomes to maximize the worst signal-to-interference-plus-noise ratio among all receivers: ； The signal-to-interference-plus-noise ratio of the ； Wherein, is the transmission power of the transmitter, is the background noise power, is the signal-to-interference-plus-noise ratio of the th receiver, is the direct channel from the transmitter to the th receiver, is the reflected channel arriving at the th receiver introduced when the intelligent reflecting surface is deployed at the location ; Use a voting-based approach to determine the deployment location of the intelligent reflecting surface. First, each receiver is used as the target user to separately run the deployment optimization in the single-receiver scenario. Denote as the running result when the th receiver is used as the target user, where the th row, and then collect all , and determine the final optimization result by which location receives the most receiver votes : ， ； In the formula, U represents the number of all receivers.

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