Wireless communication optimization method and system based on reconfigurable intelligent surface

Abstract

The invention discloses a wireless communication optimization method and system based on a reconfigurable intelligent surface, and relates to the technical field of wireless communication, and the method comprises the steps: collecting environment data and network load data, combining a user density with a network load index to carry out the product operation, calculating a resource demand index, and carrying out the calculation of the resource demand index. Comparing the resource demand index with a classification threshold range, selecting a BD-RIS architecture, calculating a corrected channel parameter by using a Rician fading formula, and generating a channel matrix; constructing an initial phase displacement matrix according to the BD-RIS architecture type, and constructing a target function based on the initial phase displacement matrix; through comprehensive analysis of network load data and user density, dynamic setting of an RIS architecture, enhancement of accurate evaluation and flexible adaptation of a network state, calculation of a channel gain reference through an Euclidean distance formula and a path loss formula, and combination of phase analysis in channel state information and a Rician fading formula, channel parameters are optimized, signal attenuation and interference are reduced, and the performance of the system is improved. And the main channel gain and the spectrum efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a wireless communication optimization method and system based on a reconfigurable intelligent surface. Background Art

[0002] With the rapid development of wireless communication technologies, the demands of the network have become increasingly complex. Especially in environments with high user density and wide coverage, how to optimize the wireless communication network to ensure efficient data transmission has become the focus of research. In current wireless communication systems, high-altitude platform stations (HAPS), as a new type of network infrastructure, have gradually become an important part of future communication networks because they can provide a wider coverage area and lower latency. By precisely controlling the reflection of signals, RIS optimizes the signal propagation path, reduces interference, and increases the channel capacity, which has been proven to have significant advantages in multiple scenarios.

[0003] Existing wireless communication optimization technologies based on RIS still face some problems. When considering network load and environmental data, existing solutions rely too much on a single network metric and fail to comprehensively consider complex user distributions and channel conditions. Although RIS can effectively improve the signal quality, how to dynamically adjust the working mode of RIS according to different network environments and load conditions, and how to optimize the communication strategy of high-altitude platform stations. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a wireless communication optimization method and system based on a reconfigurable intelligent surface, which solves the problems of how to dynamically adjust the working mode of RIS according to different network environments and load conditions, and how to optimize the communication strategy of high-altitude platform stations, although RIS can effectively improve the signal quality.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a wireless communication optimization method based on a reconfigurable intelligent surface, which includes: Collecting environmental data and network load data, performing a product operation by combining the user density and the network load index to calculate a resource demand index, comparing the resource demand index with a classification threshold range, selecting a BD-RIS architecture, using the Rician fading formula to calculate corrected channel parameters, and generating a channel matrix; Construct an initial phase shift matrix according to the BD-RIS architecture type. Based on the initial phase shift matrix, calculate the main channel gain and interference path gain respectively, calculate the spectral efficiency using the Shannon capacity formula, define the objective of maximizing the spectral efficiency, construct an objective function, set an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, and obtain the optimization result; Collect feedback data to monitor and adjust the optimization result, construct a visualization interface to display the monitoring result, and store the environmental data and network load data generated by collection and analysis.

[0007] As a preferred solution of the wireless communication optimization method based on a reconfigurable intelligent surface according to the present invention, wherein: the collecting of environmental data and network load data to generate a channel matrix includes: Use intelligent sensors to collect environmental data. The intelligent sensors include GPS sensors, wireless sensors, spectrum analyzers, temperature sensors, and power meters; The environmental data includes user location coordinates, high-altitude platform station location coordinates, channel state information, channel bandwidth, temperature, and signal transmission power; The channel state information includes the channel from the base station to the BD-RIS and the channel from the BD-RIS to the user, Use network monitoring tools to collect network load data, and use the weighted average method to calculate the density network load index. The network load data includes traffic rate, delay, and packet loss rate data; Preprocess the collected environmental data and network load data; Calculate the coverage area according to the coverage radius of the high-altitude platform station, calculate the user density using the user density calculation method, combine the user density and the network load index for multiplication operation, and calculate the resource demand index; Use the threshold decision method to set the classification threshold range, compare the resource demand index with the classification threshold range, and select the BD-RIS architecture; Use the Euclidean distance formula to calculate the Euclidean distance between the user and the high-altitude platform station, use the path loss formula to calculate the path loss, and convert the path loss from dB to a linear value as the gain reference; Extract the channels in the channel state information and perform phase analysis. Use the channel component decomposition method to select the path with the largest amplitude to separate from the channel state information to obtain the line-of-sight component, subtract the line-of-sight component from the channel state information to obtain the non-line-of-sight component, and use the channel measurement method to set the Rician factor , and use the Rician fading formula to calculate the corrected channel parameters; Use the matrix construction method to use the corrected channel parameters as the input of the matrix to generate a channel matrix.

[0008] As a preferred solution of the wireless communication optimization method based on reconfigurable intelligent surface according to the present invention, wherein: defining the maximization spectral efficiency objective and constructing the objective function includes: Setting the number of groups using the grouping analysis method, calculating the dimension of the channel matrix using the matrix dimension calculation method, and calculating the number of units in each group; Constructing the initial phase shift matrix according to the BD-RIS architecture type, setting the initial transmission power using the parameter initialization method, and calculating the base station signal component of the corrected channel parameter using matrix multiplication; Calculating the noise power spectral density based on the temperature data using the power spectral density analysis method ; Obtaining the interference channel using the channel generation method, and respectively calculating the main channel gain and the interference path gain based on the initial phase shift matrix; Calculating the power gain of the main channel using the complex signal power calculation method, calculating the power gain of the interference channel using vector multiplication, calculating the signal-to-noise ratio using the signal-to-noise ratio formula, and calculating the signal-to-interference-plus-noise ratio using the signal-to-interference-plus-noise ratio formula; Calculating the spectral efficiency using the Shannon capacity formula, defining the maximization spectral efficiency objective, and constructing the objective function.

[0009] As a preferred solution of the wireless communication optimization method based on reconfigurable intelligent surface according to the present invention, wherein: setting the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result, including: Setting the initial parameters using the default value assignment method, setting the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, substituting the updated initial phase shift matrix into the objective function to calculate the updated spectral efficiency, calculating the spectral efficiency difference using the absolute difference comparison method, setting the convergence threshold using the empirical rule, and stopping the iteration when the spectral efficiency difference is less than the convergence threshold to obtain the optimization result, including the optimized phase shift matrix and the maximized spectral efficiency.

[0010] As a preferred solution of the wireless communication optimization method based on reconfigurable intelligent surface according to the present invention, wherein: collecting feedback data to monitor and adjust the optimization result, including: Collecting feedback data and calculating the difference between the feedback data and the optimization result; Setting the judgment threshold using the statistical method, comparing the difference with the judgment threshold, and adjusting the difference greater than or equal to the judgment threshold using the PID control algorithm until the difference is less than the judgment threshold to stop the adjustment and continue to monitor the feedback data.

[0011] As a preferred solution of the wireless communication optimization method based on reconfigurable intelligent surface according to the present invention, wherein: constructing a visualization interface to display the monitoring result, including: Build a visualization interface using the front-end framework React.js to display the monitoring results and optimization results; Allow users who have passed real-name verification to access.

[0012] As a preferred solution of the wireless communication optimization method based on reconfigurable intelligent surface according to the present invention, wherein: storing the collected and analyzed environmental data and network load data includes: Store the collected environmental data and network load data, the optimization results and monitoring results generated by analysis into the central database, and set security access measures. The central database backs up the stored data to the cloud, and regularly detects the integrity of the stored data and the backup data. After the detection is completed, an integrity detection record is generated and synchronously stored into the central database.

[0013] In a second aspect, the present invention provides a wireless communication optimization system based on reconfigurable intelligent surface, including, A collection matrix module, configured to collect environmental data and network load data, perform a product operation by combining the user density and the network load index, calculate the resource demand index, compare the resource demand index with the classification threshold range, select the BD-RIS architecture, and use the Rician fading formula to calculate the corrected channel parameters to generate a channel matrix; A target optimization module, configured to construct an initial phase shift matrix according to the BD-RIS architecture type, calculate the main channel gain and the interference path gain respectively based on the initial phase shift matrix, calculate the spectral efficiency using the Shannon capacity formula, define the maximized spectral efficiency target, construct an objective function, set an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result; A detection and storage module, configured to collect feedback data to monitor and adjust the optimization result, construct a visualization interface to display the monitoring result, and store the collected and analyzed environmental data and network load data.

[0014] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and wherein: when the computer program is executed by the processor, any step of the wireless communication optimization method based on reconfigurable intelligent surface as described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and wherein: when the computer program is executed by the processor, any step of the wireless communication optimization method based on reconfigurable intelligent surface as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: By collecting environmental data and network load data, the present invention combines the user density and the network load index for multiplication operation to calculate the resource demand index, compares the resource demand index with the classification threshold range, selects the BD-RIS architecture, uses the Rician fading formula to calculate the corrected channel parameters, and generates a channel matrix; constructs an initial phase shift matrix according to the BD-RIS architecture type, based on the initial phase shift matrix, calculates the main channel gain and the interference path gain respectively, uses the Shannon capacity formula to calculate the spectral efficiency, defines the goal of maximizing the spectral efficiency, constructs an objective function, sets an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, and obtains the optimization result; enhances the accurate evaluation and flexible adaptation of the network state, reduces signal attenuation and interference, and improves the main channel gain and spectral efficiency. Brief Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the wireless communication optimization method based on the reconfigurable intelligent surface in Embodiment 1.

[0019] Figure 2 It is a schematic diagram of the wireless communication optimization system based on the reconfigurable intelligent surface in Embodiment 1.

[0020] Figure 3 It is a flowchart of generating the channel matrix in Embodiment 1.

[0021] Figure 4 It is a flowchart of the wireless communication optimization system module based on the reconfigurable intelligent surface in Embodiment 1. Detailed Embodiments

[0022] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will give a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0023] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it a separate or selectively exclusive embodiment with other embodiments.

[0025] Embodiment 1, referring to Figures 1 to 4 , which is the first embodiment of the present invention. This embodiment provides a wireless communication optimization method based on a reconfigurable intelligent surface, including the following steps: S1. Collect environmental data and network load data, perform a product operation by combining the user density and the network load index, calculate the resource demand index, compare the resource demand index with the classification threshold range, select the BD-RIS architecture, use the Rician fading formula to calculate the corrected channel parameters, and generate a channel matrix; Specifically, collecting environmental data and network load data and generating a channel matrix includes: Use intelligent sensors to collect environmental data. The intelligent sensors include GPS sensors, wireless sensors, spectrum analyzers, temperature sensors, and power meters; The environmental data includes user location coordinates, high-altitude platform station location coordinates, channel state information, channel bandwidth, temperature, and signal transmission power; The channel state information includes the channel from the base station to the BD-RIS and the channel from the BD-RIS to the user; Start signal collection on the base station, high-altitude platform station, and user terminal, activate the antenna array and signal processor, configure the beam direction of the antenna array by the wireless sensor to ensure coverage of the target area, perform beamforming settings, transmit a pilot signal through the antenna array, receive the pilot signal at the user terminal, use the pilot estimation method to measure the received signal strength and phase offset, and generate channel state information, including the channel from the base station to the BD-RIS and the channel from the BD-RIS to the user. The base station and the high-altitude platform station are responsible for transmitting signals and generating pilot signals, and the user terminal is responsible for receiving signals and estimating channel state information; Use network monitoring tools to collect network load data, and use the weighted average method to calculate the density network load index. The network load data includes traffic rate, delay, and packet loss rate data; Preprocess the collected environmental data and network load data, including denoising using a Gaussian filter and normalizing the environmental data and network load data; Calculate the coverage area according to the coverage radius of the high-altitude platform station, calculate the user density using the user density calculation method, perform a product operation by combining the user density and the network load index, and calculate the resource demand index; Set the classification threshold range using the threshold decision method, compare the resource demand index with the classification threshold range, select the BD-RIS architecture if the resource demand index is less than the classification threshold range, select the single connection architecture if the resource demand index is equal to the classification threshold range, select the grouped connection architecture if the resource demand index is greater than the classification threshold range, and select the full connection architecture if the resource demand index is greater than the classification threshold range; Calculate the Euclidean distance between the user and the high-altitude platform station using the Euclidean distance formula, calculate the path loss of the Euclidean distance between the user and the high-altitude platform station using the path loss formula, convert the path loss from dB to a linear value as the gain reference, and the formula is: , where is the gain reference and PL is the path loss; Extract the channel in the channel state information and perform phase analysis. Use the channel component decomposition method to select the path with the largest amplitude and separate it from the channel state information to obtain the line-of-sight component. Subtract the line-of-sight component from the channel state information to obtain the non-line-of-sight component. Use the channel measurement method to set the Rician factor , and use the Rician fading formula to calculate the corrected channel parameters. The formula is: , where is the corrected channel parameter, is the Euclidean distance between the user and the high-altitude platform station, is the line-of-sight component, calculated through the phase information in the CSI, is the non-line-of-sight component; Use the matrix construction method to take the corrected channel parameters as the input of the matrix to generate the channel matrix.

[0026] By combining the user density and the network load index, the resource demand index is obtained through multiplication operation. This index provides a basis for network optimization decisions. The system can dynamically adjust the resource allocation strategy according to the actual demands of different regions, avoid network overload or resource waste, and improve the overall performance of the network. Based on the comparison between the resource demand index and the classification threshold, selecting different connection architectures can intelligently adjust the network architecture to adapt to different network load demands. The optimization strategy effectively avoids over-configuration or under-configuration situations and improves the utilization rate of network resources. By calculating the path loss between the user and the high-altitude platform station using the Euclidean distance formula, converting the path loss from dB to a linear value as the gain reference, the signal attenuation situation can be calculated more accurately, thus providing a more accurate prediction model for signal transmission. Through the channel component decomposition method, the line-of-sight and non-line-of-sight components are extracted from the channel state information, and then the Rician fading model is used to correct the channel parameters, which can effectively optimize the channel quality, reduce the impact of signal fading on communication quality, and improve the stability and transmission rate of the network. Through effective channel measurement, path loss estimation, and resource demand prediction, the network architecture and resource allocation strategy can be dynamically adjusted to improve the performance and user experience of the wireless communication system.

[0027] S2. Construct an initial phase shift matrix according to the BD-RIS architecture type. Based on the initial phase shift matrix, calculate the main channel gain and the interference path gain respectively, use the Shannon capacity formula to calculate the spectral efficiency, define the objective of maximizing the spectral efficiency, construct an objective function, and set an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result; Specifically, define the objective of maximizing the spectral efficiency and construct an objective function, including: Use the grouping analysis method to set the number of groups, use the matrix dimension calculation method to calculate the dimension of the channel matrix, and calculate the number of units in each group. The formula is: , where is the number of units, N is the dimension of the channel matrix, and G is the number of groups; Construct an initial phase shift matrix according to the BD-RIS architecture type. If the BD-RIS architecture type is a single connection architecture, use the diagonal matrix construction method to initialize the phase shift matrix. If the BD-RIS architecture type is a grouped connection architecture, use the block diagonal matrix construction method to initialize the phase shift matrix. If the BD-RIS architecture type is a full connection architecture, use the identity matrix initialization method to construct a full matrix; Use the parameter initialization method to set the initial transmit power, and use matrix multiplication to calculate the base station signal component of the corrected channel parameters. The formula is: , where The base station signal component for the corrected channel parameters is the i-th initial phase shift matrix is the channel parameter from the base station to the BD-RIS after correction; Calculate the noise power spectral density using the power spectral density analysis method based on temperature data , the formula is: , where K is the Boltzmann constant, T is the temperature, and F is the noise figure, provided by the equipment manufacturer; Obtain the interference channel using the channel generation method. Based on the initial phase shift matrix, calculate the main channel gain and the interference path gain respectively. The formula is: , , where is the u-th main channel gain, is the channel parameter from the corrected BD-RIS to the user is the conjugate transpose of is the v-th interference path gain, is the conjugate transpose of the interference channel; Calculate the power gain of the main channel using the complex signal power calculation method, calculate the power gain of the interference channel using vector multiplication, calculate the signal-to-noise ratio using the signal-to-noise ratio formula, and calculate the signal-to-interference-plus-noise ratio using the signal-to-interference-plus-noise ratio formula. The formula is: , , where is the signal-to-noise ratio, is the signal-to-interference-plus-noise ratio, is the signal transmission power at time t, is the power gain of the main channel, is the power gain of the interference channel; Calculate the spectral efficiency using the Shannon capacity formula. The formula is: , where C is the spectral efficiency and B is the channel bandwidth; Define the objective of maximizing the spectral efficiency and construct the objective function. The formula is: .

[0028] By setting the number of groups through the group analysis method and combining the matrix dimension calculation method to obtain the dimension of the channel matrix, resources can be reasonably allocated and signal processing can be optimized, ensuring the optimal dimension of the matrix during signal processing, effectively improving processing efficiency and reducing the computational burden, especially in large-scale communication systems, which can significantly improve the processing capability and real-time performance of the system. According to the different types of BD-RIS architecture, different matrix initialization methods are used to effectively control the phase of the signal. By optimizing the noise control strategy, the stability of signal transmission can be improved, especially in noisy environments, ensuring the robustness and reliability of the system. The main channel gain and interference path gain are calculated by the channel generation method, which can effectively distinguish the main signal from the interference signal. By improving the signal-to-noise ratio and signal-to-interference-to-noise ratio, the data transmission rate and signal quality can be effectively improved. Finally, the spectral efficiency is calculated by the Shannon capacity formula, and the objective function of maximizing the spectral efficiency is constructed, which can provide a clear performance optimization direction for system design, help to evaluate the performance of the system, and maximize the channel utilization efficiency by adjusting various parameters to improve the overall transmission rate of the system.

[0029] Furthermore, according to the BD-RIS architecture type, the optimization algorithm is set to calculate the gradient and update the initial phase shift matrix to obtain the optimization results, including: The default value assignment method is used to set the initial parameters, and the optimization algorithm is set according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, including if the BD-RIS architecture type is a single-connection architecture, using the block coordinate descent method to perform iterative optimization element by element, if the BD-RIS architecture type is a grouped connection architecture, using the gradient ascent method for iterative optimization, if the BD-RIS architecture type is a fully connected architecture, using the Riemannian optimization method for iterative optimization, based on the updated initial phase shift matrix introduced into the objective function to calculate the updated spectral efficiency, using the absolute difference comparison method to calculate the spectral efficiency difference, using the empirical rule to set the convergence threshold, when the spectral efficiency difference is less than the convergence threshold, stop the iteration, and obtain the optimization results, including the optimized phase shift matrix and the maximized spectral efficiency.

[0030] The appropriate optimization algorithm is selected according to the different BD-RIS architecture types. This process ensures that the algorithm can perform optimally under different architectures. By selecting the optimization strategy in a targeted manner, the optimization method can be adjusted according to the specific needs of the system to achieve the best signal quality and resource utilization. By reasonably initializing the phase shift matrix, the optimization process can converge more quickly, avoiding the low computational efficiency caused by starting the calculation from the disordered initial state. The adjustment of the phase shift matrix is ​​crucial for the optimization of beamforming and multipath signals, which can effectively improve the signal quality and system stability. The improvement of spectrum efficiency not only increases the data transmission rate, but also supports more users under the same spectrum resources, improving the network's carrying capacity and performance.

[0031] S3. Collect feedback data to monitor and adjust the optimization results, construct a visualization interface to display the monitoring results, and store the environmental data and network load data generated from the collection and analysis. Specifically, the collection of feedback data to monitor and adjust the optimization results includes: Collect feedback data and calculate the difference between the feedback data and the optimization results. Use statistical methods to set a judgment threshold, compare the difference with the judgment threshold, and use the PID control algorithm to adjust the difference greater than or equal to the judgment threshold until the adjustment stops when the difference is less than the judgment threshold, and continue to monitor the feedback data.

[0032] The difference calculation provides a quantitative basis for further adjustment, ensuring that the system can promptly identify and respond to potential performance issues. The statistical method makes the adjustment mechanism more precise, avoiding the instability that may be brought about by subjectively setting the threshold. Through the PID control algorithm, the optimization process can gradually approach the optimal solution more accurately, avoiding the situation where the system performance becomes unstable due to over-adjustment or slow response. This closed-loop control process can cope with the changing working environment and external interference, ensuring that the system is always in the best working state.

[0033] Furthermore, constructing a visualization interface to display the monitoring results includes: Use the front-end framework React.js to construct a visualization interface to display the monitoring results and the optimization results. Allow users who have passed real-name verification to view.

[0034] By using React.js to construct the visualization interface, its efficient virtual DOM and componentized structure can be fully utilized to ensure the real-time response ability and flexibility of the interface. Through real-name verification, unauthorized users can be effectively prevented from accessing sensitive information, ensuring data security. Through the visualization interface, users can perform operations such as data filtering, querying, and report exporting, greatly enhancing the interactivity and user participation of the system.

[0035] Even further, storing the environmental data and network load data generated from the collection and analysis includes: Store the collected environmental data and network load data, the optimization results and monitoring results generated from the analysis into the central database, and set security access measures. The central database backs up the stored data to the cloud and regularly performs integrity detection on the stored data and the backup data. After the detection is completed, an integrity detection record is generated and synchronously stored into the central database.

[0036] Integrate the scattered monitoring data into a systematic database to provide a comprehensive and accurate basis for subsequent analysis and decision-making. Cloud backup not only provides redundant storage of data to prevent data loss caused by hardware failures, but also improves data accessibility and recovery speed. By regularly detecting the integrity of the stored data and backup data, it can be ensured that the data has not been tampered with or damaged during storage.

[0037] This embodiment also provides a wireless communication optimization system based on reconfigurable intelligent surfaces, including: A collection matrix module, configured to collect environmental data and network load data, perform a product operation by combining the user density and the network load index, calculate the resource demand index, compare the resource demand index with the classification threshold range, select the BD-RIS architecture, use the Rician fading formula to calculate the corrected channel parameters, and generate a channel matrix; A target optimization module, configured to construct an initial phase shift matrix according to the BD-RIS architecture type, calculate the main channel gain and the interference path gain respectively based on the initial phase shift matrix, calculate the spectral efficiency using the Shannon capacity formula, define the target of maximizing the spectral efficiency, construct an objective function, set an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result; A detection and storage module, configured to collect feedback data to monitor and adjust the optimization result, construct a visualization interface to display the monitoring result, and store the environmental data and network load data generated by collection and analysis.

[0038] This embodiment also provides a computer device applicable to the case of the wireless communication optimization method based on reconfigurable intelligent surfaces, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the wireless communication optimization method based on reconfigurable intelligent surfaces as proposed in the above embodiment.

[0039] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0040] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method for optimizing wireless communication based on a reconfigurable intelligent surface proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0041] In summary, the present invention collects environmental data and network load data, combines the user density and the network load index for multiplication operation to calculate the resource demand index, compares the resource demand index with the classification threshold range, selects the BD-RIS architecture, uses the Rician fading formula to calculate the corrected channel parameters, and generates a channel matrix; constructs an initial phase shift matrix according to the BD-RIS architecture type, based on the initial phase shift matrix, calculates the main channel gain and the interference path gain respectively, uses the Shannon capacity formula to calculate the spectral efficiency, defines the objective of maximizing the spectral efficiency, constructs an objective function, sets an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result; enhances the accurate evaluation and flexible adaptation of the network state, reduces signal attenuation and interference, and improves the main channel gain and spectral efficiency.

[0042] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A wireless communication optimization method based on reconfigurable intelligent surfaces, characterized in that: Including, Collecting environmental data and network load data, multiplying the user density and the network load index, calculating the resource demand index, comparing the resource demand index with the classification threshold range, selecting the BD-RIS architecture, using the Rician fading formula to calculate the corrected channel parameters, and generating a channel matrix; Constructing an initial phase shift matrix according to the BD-RIS architecture type, calculating the main channel gain and the interference path gain respectively based on the initial phase shift matrix, calculating the spectral efficiency using the Shannon capacity formula, defining the objective of maximizing the spectral efficiency, constructing an objective function, setting an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, and obtaining the optimization result; Collecting feedback data to monitor and adjust the optimization result, constructing a visualization interface to display the monitoring result, and storing the environmental data and network load data generated by collection and analysis.

2. The wireless communication optimization method based on a reconfigurable intelligent surface according to claim 1, characterized in that: The collecting of environmental data and network load data and the generating of the channel matrix include: Collecting environmental data using intelligent sensors, where the intelligent sensors include GPS sensors, wireless sensors, spectrum analyzers, temperature sensors, and power meters; The environmental data includes user location coordinates, high-altitude platform station location coordinates, channel state information, channel bandwidth, temperature, and signal transmission power; The channel state information includes the channel from the base station to the BD-RIS and the channel from the BD-RIS to the user, Collecting network load data using network monitoring tools, calculating the density network load index using the weighted average method, where the network load data includes traffic rate, delay, and packet loss rate data; Preprocessing the collected environmental data and network load data; Calculating the coverage area according to the coverage radius of the high-altitude platform station, calculating the user density using the user density calculation method, multiplying the user density and the network load index, and calculating the resource demand index; Setting the classification threshold range using the threshold decision method, comparing the resource demand index with the classification threshold range, and selecting the BD-RIS architecture; Calculating the Euclidean distance between the user and the high-altitude platform station using the Euclidean distance formula, calculating the path loss using the path loss formula, converting the path loss from dB to a linear value as the gain reference; Extract the channel in the channel state information and perform phase analysis. Use the channel component decomposition method to select the path with the largest amplitude and separate it from the channel state information to obtain the line-of-sight component. Subtract the line-of-sight component from the channel state information to obtain the non-line-of-sight component. Use the channel measurement method to set the Rician factor , and use the Rician fading formula to calculate the corrected channel parameters; Using the matrix construction method to take the corrected channel parameters as the input of the matrix and generating a channel matrix.

3. The wireless communication optimization method based on reconfigurable intelligent surface according to claim 2, wherein: The defining of the objective of maximizing the spectral efficiency and the constructing of the objective function include: Setting the number of groups using the grouping analysis method, calculating the dimension of the channel matrix using the matrix dimension calculation method, and calculating the number of units in each group; Constructing an initial phase shift matrix according to the BD-RIS architecture type, setting the initial transmit power using the parameter initialization method, and calculating the base station signal component of the corrected channel parameters using matrix multiplication; Calculating the noise power spectral density using the power spectral density analysis method based on temperature data ; Obtaining the interference channel using the channel generation method, and calculating the main channel gain and the interference path gain respectively based on the initial phase shift matrix; Calculating the power gain of the main channel using the complex signal power calculation method, calculating the power gain of the interference channel using vector multiplication, calculating the signal-to-noise ratio using the signal-to-noise ratio formula, and calculating the signal-to-interference-plus-noise ratio using the signal-to-interference-plus-noise ratio formula; Calculate the spectral efficiency using the Shannon capacity formula, define the goal of maximizing the spectral efficiency, and construct the objective function.

4. The wireless communication optimization method based on reconfigurable intelligent surfaces according to claim 3, wherein: Set the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result, including: Set the initial parameters using the default value assignment method, set the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, substitute the updated initial phase shift matrix into the objective function to calculate the updated spectral efficiency, use the absolute difference comparison method to calculate the spectral efficiency difference, use the empirical rule to set the convergence threshold, and stop the iteration when the spectral efficiency difference is less than the convergence threshold to obtain the optimization result, including the optimized phase shift matrix and the maximized spectral efficiency.

5. The wireless communication optimization method based on reconfigurable intelligent surface according to claim 4, characterized in that: Collect feedback data to monitor and adjust the optimization result, including: Collect feedback data and calculate the difference between the feedback data and the optimization result; Set the judgment threshold using statistical methods, compare the difference with the judgment threshold, and use the PID control algorithm to adjust the difference greater than or equal to the judgment threshold until the difference is less than the judgment threshold and then stop the adjustment and continue to monitor the feedback data.

6. The wireless communication optimization method based on a reconfigurable intelligent surface according to claim 5, wherein: Construct a visualization interface to display the monitoring result, including: Use the front-end framework React.js to construct a visualization interface to display the monitoring result and the optimization result; Allow users who have passed real-name verification to view it.

7. The wireless communication optimization method based on reconfigurable intelligent surface according to claim 5, wherein: Store the environmental data and network load data generated by collection and analysis, including: Store the collected environmental data and network load data, as well as the generated optimization result and monitoring result from analysis, in the central database, and set security access measures. The central database backs up the stored data to the cloud and regularly performs integrity detection on the stored data and the backup data. After the detection is completed, an integrity detection record is generated and synchronously stored in the central database.

8. A wireless communication optimization system based on a reconfigurable intelligent surface, based on the wireless communication optimization method based on a reconfigurable intelligent surface according to any one of claims 1 to 7, characterized in that: Including, A collection matrix module, which is used to collect environmental data and network load data, perform a product operation by combining the user density and the network load index, calculate the resource demand index, compare the resource demand index with the classification threshold range, select the BD-RIS architecture, and use the Rician fading formula to calculate the corrected channel parameters to generate a channel matrix; A target optimization module, which is used to construct an initial phase shift matrix according to the BD-RIS architecture type, based on the initial phase shift matrix, calculate the main channel gain and the interference path gain respectively, calculate the spectral efficiency using the Shannon capacity formula, define the goal of maximizing the spectral efficiency, construct the objective function, set the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result; A detection and storage module, which is used to collect feedback data to monitor and adjust the optimization result, construct a visualization interface to display the monitoring result, and store the environmental data and network load data generated by collection and analysis.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the wireless communication optimization method based on a reconfigurable intelligent surface according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the wireless communication optimization method based on a reconfigurable intelligent surface according to any one of claims 1 to 7.

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