Transmission RIS position deployment optimization method based on combination of region division and moth fire suppression

Abstract

The invention relates to a transmission RIS position deployment optimization method based on combination of region division and moth fire suppression, and the method comprises the steps: building a STAR-RIS position optimization model through employing an STAR-RIS assisted scene according to the known channel state information CSI of a system; constructing transmission models of users on the two sides of the reflection area and the refraction area of the receiving end; according to the transmission models of the users on the two sides of the reflection area and the refraction area of the receiving end, obtaining signal to interference plus noise ratios of the users on the two sides; according to the transmission models of the users on the two sides of the reflection area and the refraction area of the receiving end and the signal to interference plus noise ratios of the users on the two sides of the receiving end, the communication rates of the users on the two sides of the receiving end are obtained, and a maximum receiving end total communication rate model is obtained; and dividing the STAR-RIS deployment range, i.e., an optimization region, into N sub-regions, and optimizing the sub-regions by adopting an MFO algorithm to obtain an approximate optimal solution of the total communication rate, i.e., an optimal position obtained by optimization. The method expands the actual application scenarios of STAR-RIS, including a high-density user environment and a two-way service scenario, so that the technology has wide application value.

Description

Technical Field

[0001] The present invention belongs to the field of communication technologies and relates to an optimization method for the position deployment of a transmissive RIS combining region division and moth - like optimization. Background Art

[0002] With the rapid development of wireless communication technologies, the demand for high - data - rate, low - latency, and high - reliability transmission is increasing day by day. However, the transmission efficiency of traditional wireless communication systems is often limited by problems such as multipath effects, blockages, and signal fading in complex environments. To address these issues, in recent years, Reconfigurable Intelligent Surface (RIS) has received extensive attention as a revolutionary communication technology. RIS can intelligently reshape the wireless channel by dynamically adjusting the reflection or transmission characteristics of its elements, improving communication performance while reducing system energy consumption. Traditional RIS mainly enhances the received signal strength of the target user by reflecting signals. However, in some scenarios, relying solely on signal reflection may cause communication bottlenecks, especially when the user is on both sides of the RIS or the reflection path is blocked. Therefore, the concept of Simultaneously Transmitting and Reflecting RIS (STAR - RIS) has been proposed, which can not only reflect signals but also transmit signals, flexibly supporting the communication needs of bilateral users. STAR - RIS not only expands the system deployment scenarios but also further optimizes the channel gain and improves system resource utilization. In addition, the deployment position of STAR - RIS is crucial for system performance. In a wireless communication system, the position of RIS directly affects the signal transmission path, channel gain, and the quality of the received signal by users. Therefore, reasonably selecting the position of RIS is a key link in optimizing system performance. Especially in the STAR - RIS scenario, since it serves users on both the transmission side and the reflection side simultaneously, the selection of the STAR - RIS position needs to comprehensively consider the channel environments of both sides of the users to achieve balanced optimization of channel conditions.

[0003] The main reason for using regional sampling is that the large-scale fading and small-scale fading of the wireless channel are highly coupled, and the attenuation of the signal is mainly affected by the large-scale fading. Through regional sampling, complex continuous optimization problems can be divided into more representative discrete regions, making the optimization process simpler. By ignoring the local variations of small-scale fast fading, the main impact of large-scale fading can be captured. In addition, regional partitioning can effectively improve the robustness of the algorithm. The sampled regions represent the overall channel characteristics and can better reflect the signal strength distribution. Moreover, regional partitioning effectively avoids point-by-point optimization for all possible positions, improving the computational efficiency. Combining regional sampling with large-scale fading characteristics can simplify the optimization and find the representative optimal positions of the RIS in different regions, which helps to improve communication performance.

[0004] In addition, traditional RIS position optimization methods usually rely on exhaustive search or heuristic algorithms, which have high computational complexity and are difficult to adapt to the dynamically changing channel environment. Therefore, introducing intelligent optimization algorithms (such as genetic algorithms and particle swarm optimization algorithms) has become a trend to solve this problem. The Moth-Flame Optimization (MFO) algorithm is a new type of bionic intelligent optimization algorithm, which is inspired by the behavior of moths flying in a spiral pattern with the flame as a reference at night. The MFO algorithm has shown good performance in solving complex optimization problems due to its strong search ability, good global convergence, and ability to adapt to dynamic environments. In RIS position optimization, the MFO algorithm can search for the optimal deployment position of the RIS in an iterative manner by simulating the dynamic relationship between moths and flames, thereby maximizing the communication performance under the condition of meeting the system constraints. Summary of the Invention

[0005] Technical Problems to be Solved

[0006] To avoid the deficiencies of the prior art, the present invention proposes a transmission RIS position deployment optimization method based on regional partitioning and moth-flame optimization, which is used to solve the difficult problem of being unable to quickly determine the optimal position of the RIS in a multi-user scenario in the prior art.

[0007] Technical Solution

[0008] A transmission RIS position deployment optimization method based on regional partitioning and moth-flame optimization, characterized by the following steps:

[0009] Step 1: Utilize the scenario assisted by STAR-RIS, and based on the known channel state information CSI of the system, construct the position optimization model of STAR-RIS; obtain the transmission models of the users on both sides of the receiving end reflection area and refraction area under the condition of known CSI to construct the position optimization model of STAR-RIS.

[0010] Step 2: Obtain the signal-to-interference-plus-noise ratio (SINR) of users on both sides according to the transmission models of users on both sides of the reflection area and the refraction area at the receiving end;

[0011] Step 3: Obtain the communication rates of users on both sides of the receiving end according to the transmission models of users on both sides of the reflection area and the refraction area at the receiving end and the SINR of users on both sides of the receiving end, and obtain a model for maximizing the total communication rate at the receiving end;

[0012] Step 4: Divide the deployment range of STAR-RIS, i.e., the optimization area, into N sub-regions, and use the MFO algorithm to optimize the sub-regions to obtain an approximate optimal solution of the total communication rate, i.e., the optimized best position;

[0013] Calculate the rate of each user at the receiving end and sum up the rates of all users to obtain the total communication rate of the system.

[0014] The STAR-RIS adopts an energy splitting protocol, and the communication between the base station and the users is through the LOS channel.

[0015] There is a direct link from the base station to the users and a reflected / refracted link propagated through the STAR-RIS. The position optimization model of the STAR-RIS is constructed as a Rice fading channel model:

[0016]

[0017] where D is the actual transmission distance; α is the path loss exponent; K is the Rice factor; and is the phase.

[0018] The scenario assisted by the STAR-RIS is set with N electromagnetic units. The base station equipped with d antennas transmits signals for M1 users in the reflection area R and M2 users in the transmission area T through the STAR-RIS, and there is a direct link from the base station to the reflected users and the refracted users.

[0019] The transmission model of the i-th user in the reflection area at the receiving end is:

[0020]

[0021] The transmission model of the k-th user in the refraction area is:

[0022]

[0023] where is the channel state matrix from the base station to the i-th user in area R, is the channel state matrix from the base station to the k-th user in area T, is the channel state matrix from the STAR-RIS to the i-th user in area R, is the channel state matrix from the STAR-RIS to the i-th user in the T zone, is the reflection coefficient matrix of the STAR-RIS, is the refraction coefficient matrix of the STAR-RIS, where β N and θ N are respectively the modulation of the signal amplitude by the n-th reflection unit on the RIS and the phase shift of the reflection unit, is the channel state matrix from the base station to the STAR-RIS, X = ws is the transmitted signal, where w is beamforming, and represents the thermal noise.

[0024] According to the user's transmission model, the signal-to-interference-plus-noise ratio (SINR) at the receiving end of the user is expressed as:

[0025]

[0026] The SINR in the refraction region is expressed as:

[0027]

[0028] The communication rate RL of the user at the receiving end = ln(1 + SINR), and the total communication rate of the users on both sides of the receiving end is:

[0029] When divided into N sub-regions, the size of each sub-region is equal.

[0030] When the MFO algorithm is used to optimize the sub-regions, within each sub-region, the moth will search along the gradient of the objective function to find the optimal RIS position.

[0031] The best position is obtained by optimizing with the MFO algorithm. According to the best position, the LOS channels between the RIS and the base station, the RIS and the users in the reflection region and the transmission region are recalculated, the small-scale fading is re-estimated, and the large-scale fading is calculated to form the final channel model. According to the final channel model, the total communication rate at the receiving end is calculated.

[0032] Beneficial effects

[0033] A transmission RIS location deployment optimization method based on the combination of region division and moth-flame optimization proposed by the present invention can effectively optimize the location of STAR-RIS through region division and the MFO algorithm, maximize the total communication rate, and significantly improve the communication performance of the system. Moreover, this method has global search and dynamic adaptation capabilities, can optimize the STAR-RIS location in real time to cope with channel condition changes, shows excellent performance in mobile device and complex multi-user scenarios, and is applicable to line-of-sight communication environments. The region division algorithm can effectively simplify the process of searching for the optimal location of STAR-RIS, divide the complex continuous optimization problem into more representative discrete regions, make the optimization process simpler, ignore the local changes of small-scale fast fading, and focus on capturing the main effects of large-scale fading. The MFO algorithm has good global convergence, avoids falling into local optima, and makes the optimization results more stable and reliable. The lightweight framework of the MFO algorithm reduces the computational complexity, shortens the optimization time, and improves the resource utilization efficiency. In addition, this method expands the practical application scenarios of STAR-RIS, including high-density user environments and two-way service scenarios, making this technology have wide application value. Description of the Drawings

[0034] Figure 1 :: Step diagram of the transmission RIS location deployment optimization method based on the combination of region division and moth-flame optimization in the embodiment.

[0035] Figure 2 :: Scenario schematic diagram of the transmission RIS location deployment optimization method based on the combination of region division and moth-flame optimization in the embodiment.

[0036] Figure 3 :: Region division diagram of the transmission RIS location deployment optimization method based on the combination of region division and moth-flame optimization in the embodiment.

[0037] Figure 4 :: MFO algorithm convergence diagram of the transmission RIS location deployment optimization method based on the combination of region division and moth-flame optimization in the embodiment.

[0038] Figure 5 :: Optimal communication rate of the STAR-RIS selectable region in different region divisions of the transmission RIS location deployment optimization method based on the combination of region division and moth-flame optimization in the embodiment. Detailed Embodiment

[0039] The present invention will be further described in combination with the embodiments and the drawings:

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0041] In this embodiment, an optimization method for the position deployment of a transmissive RIS based on region division and moth-flame optimization is provided. As shown in Figure 1 , the RIS-aided wireless transmission system's RIS position optimization method based on region division and moth-flame optimization may include: Step 1 to Step 4.

[0042] To solve the above technical problems, the technical solution adopted by the present invention is:

[0043] Step S101: Based on the scenario assisted by STAR-RIS (transmissive RIS), under the condition that the system knows the CSI, construct the position optimization model of STAR-RIS to improve the total transmission rate performance of users in the system's downlink communication. Construct the transmission models of users on both sides of the receiving end reflection area and refraction area according to the position optimization model of STAR-RIS under the known CSI condition constructed by the STAR-RIS assisted scenario;

[0044] Step 2: According to the transmission models of users on both sides of the receiving end reflection area and refraction area, obtain the signal-to-interference-plus-noise ratio (SINR) of the users on both sides;

[0045] Step 3: According to the transmission models of users on both sides of the receiving end reflection area and refraction area and the communication rates of users on both sides of the receiving end, design a model to maximize the total communication rate of the receiving end;

[0046] Step 4: Use region division and the MFO algorithm to solve the optimization problem of the model to maximize the total communication rate of the receiving end, and obtain the final information transmission scheme.

[0047] The transmission RIS location deployment optimization method based on region division and moth - fluttering - like approach provided by the present invention can effectively optimize the location of the STAR - RIS through region division and the MFO algorithm, maximize the total communication rate, and significantly improve the communication performance of the system. Moreover, this method has global search and dynamic adaptation capabilities, can optimize the STAR - RIS location in real - time to cope with channel condition changes, shows excellent performance in mobile device and complex multi - user scenarios, and is applicable to line - of - sight communication environments. The region - division algorithm can effectively simplify the process of searching for the optimal location of the STAR - RIS, divide the complex continuous optimization problem into more representative discrete regions, make the optimization process simpler, ignore the local changes of small - scale fast fading, and focus on capturing the main effects of large - scale fading. The MFO algorithm has good global convergence, avoids falling into local optima, and makes the optimization results more stable and reliable. The lightweight framework of the MFO algorithm reduces the computational complexity, shortens the optimization time, and improves the resource utilization efficiency. In addition, this method expands the practical application scenarios of the STAR - RIS, including high - density user environments and two - way service scenarios, making this technology have broad application value.

[0048] A further technical solution of the present invention: In the step of using the region - division and MFO algorithms to solve the optimization problem of the maximized total communication rate model at the receiving end and obtaining the final information transmission scheme; it includes:

[0049] Decompose the maximized total communication rate model in the STAR - RIS - assisted scenario, and use the position coordinates x I , y I of the STAR - RIS as optimization variables to jointly optimize the maximized total communication rate model;

[0050] Obtain the final information transmission scheme under the framework of the region - division and MFO algorithms.

[0051] A further technical solution of the present invention: Decompose the maximized total communication rate model in the STAR - RIS - assisted scenario, and use the position coordinates x I , y I of the STAR - RIS as optimization variables to jointly optimize the maximized total communication rate model, including:

[0052] Set the fixed parameters of the STAR - RIS, users, and base stations, and combine the maximized total communication rate model in the STAR - RIS - assisted scenario to obtain the optimization strategy for the optimal location of the STAR - RIS in this scenario until the variables of the STAR - RIS position converge to obtain the maximized total communication rate scheme at the receiving end.

[0053] Next, with reference to Figures 1 to 5A more detailed description of each step of the RIS location optimization method based on region division and moth - fluttering in the STAR - RIS empowered wireless transmission system in the above - mentioned exemplary embodiment is given.

[0054] In step 1, based on the STAR - RIS - assisted scenario and under the condition that the system knows the CSI, a location optimization model of the STAR - RIS is constructed. Among them, while having the reflection function of the traditional RIS, the STAR - RIS can also transmit signals, flexibly supporting the communication needs of both - side users. According to the STAR - RIS - assisted scenario, a transmission model for users on both sides of the receiving - end reflection area and refraction area is constructed for the STAR - RIS location optimization model of CSI - assisted downlink communication. Considering that the STAR - RIS is placed in a relatively high and open position, and the communication between the base station and users only passes through the LOS channel. Among them, there is a direct link from the base station to the users and reflection / refraction links propagated through the STAR - RIS, and a Rice - fading channel model is constructed:

[0055]

[0056] where D is the actual transmission distance; α is the path - loss exponent; K is the Rice factor; and is the phase.

[0057] Specifically, the STAR - RIS is provided with N electromagnetic units and is equipped with d antenna base stations. Signals are transmitted to M1 users in the reflection area (R area) and M2 users in the transmission area (T area) through the STAR - RIS, and there is a direct link from the base station to the reflected users and refracted users.

[0058] In step 2, according to the transmission models of users on both sides of the receiving - end reflection area and refraction area, the signal - to - interference - plus - noise ratios of both - side users are obtained.

[0059] Specifically, the STAR - RIS adopts an energy - splitting protocol. Thus, a wireless signal transmission model in this system is constructed. In the actual scenario, the transmission model expression of the wireless signal at the receiving - end user is as follows.

[0060] Transmission model of the i - th user in the reflection area:

[0061]

[0062] Transmission model of the k - th user in the refraction area:

[0063]

[0064] where is the channel - state matrix from the base station to the i - th user in the R area, is the channel state matrix from the base station to the k-th user in area T, is the channel state matrix from the STAR-RIS to the i-th user in area R, is the channel state matrix from the STAR-RIS to the i-th user in area T, is the reflection coefficient matrix of the STAR-RIS, is the refraction coefficient matrix of the STAR-RIS, where β N and θ N are respectively the modulation of the signal amplitude by the n-th reflection unit on the RIS and the phase shift of the reflection unit, is the channel state matrix from the base station to the STAR-RIS, X = ws is the transmitted signal, where w is beamforming, and represents thermal noise.

[0065] According to the user's transmission model, the signal-to-interference-plus-noise ratio (SINR) of the user at the receiving end can be expressed as:

[0066]

[0067] In step 3, according to the transmission models of the users on both sides of the reflection area and the refraction area at the receiving end and the SINR of the users on both sides of the receiving end, a model for maximizing the total communication rate at the receiving end is designed.

[0068] In the scenario assisted by the STAR-RIS, according to the SINR of the receiving-end users, the communication rate of the receiving-end users can be expressed as:

[0069] RL = ln(1 + SINR)

[0070] Then the total communication rate of the users on both sides of the receiving end is:

[0071]

[0072] In step 4, the region division and MFO algorithm are used to optimize the objective function of S103. This is because region division can effectively simplify the search process for the optimal position of the STAR-RIS, divide the complex continuous optimization problem into more representative discrete regions, make the optimization process simpler, and focus on capturing the main influence of large-scale fading. Moreover, the MFO algorithm has excellent global search ability, can explore the entire optional region of the STAR-RIS, avoid falling into local optimal solutions, and the MFO algorithm shows stronger solution ability in solving the overall non-convex optimization problem of optimizing the communication rate.

[0073] According to the region division algorithm, the selectable optimization region is divided into multiple sub-regions, and the following sub-steps are executed:

[0074] Step (1): Determine the selectable optimization area, i.e., the deployment scope of the STAR-RIS.

[0075] Step (2): Define the area division parameters and set the following parameters:

[0076] Number of sub-regions: Divide the area according to the movable area of the RIS to determine the number of sub-regions to be divided.

[0077] Area size: Calculate the size of each sub-region according to the size of the selectable area. The size of the sub-region should be adjusted according to the actual requirements of the communication environment. Generally, larger areas are suitable for scenarios with less channel variation, and smaller areas are suitable for high-dynamic environments.

[0078] Step (3): Uniformly divide the entire area into N sub-regions to ensure that the size of each sub-region is equal. The center point of each divided sub-region will be used as the initial optimization position for optimizing the deployment position of the RIS. After dividing the area, calculate the large-scale fading and small-scale fading parameters at the midpoint of each sub-region. Large-scale fading is mainly caused by large-scale factors such as distance and obstacles, and is calculated according to the distances between the midpoint of the current sub-region and the base station and the user. Small-scale fading is mainly caused by multipath effects, usually changes rapidly on the signal propagation path, and is calculated according to the set value of the system during the initial setting. In the subsequent optimization process, the large-scale fading parameters will be updated with the optimal position of each iteration, and recalculated according to the changes in the latest RIS position and user position. While the small-scale fading parameters remain unchanged and are still calculated using the initial set values, which can simplify the calculation process and reduce the complexity during the optimization process.

[0079] Step (4): Apply the MFO algorithm for optimization. After completing the area division, prepare to use the divided sub-regions for the optimization of the MFO algorithm. In each sub-region, the moths will search along the gradient of the objective function to find the optimal RIS position. Area division can effectively reduce the search space and improve the optimization efficiency and accuracy of the MFO algorithm. During each iteration of the MFO algorithm, the range and position of the sub-regions can be dynamically adjusted according to the current optimization state. For example, some sub-regions can be divided more finely during the search process, or some regions can be expanded according to the optimization results, so as to more accurately approximate the global optimal solution.

[0080] According to the process of the MFO algorithm, the sub-steps to be executed are as follows:

[0081] Step (a): Initialize the parameters of the moth-flame optimization algorithm, set parameters such as the maximum number of iterations T, the number of moth populations p, the dimension q, and the upper and lower limits of the independent variables. The moth population is represented by the following matrix:

[0082]

[0083] Step (b): Randomly and initially generate the positions of the moths, evaluate the fitness of each moth in the moth population through the fitness function, and solve its fitness value. The fitness values of the moths are stored in the array OM, and the fitness value corresponding to the i-th moth is OM i , which is expressed as follows:

[0084]

[0085] Step (c): Sort the spatial positions of the moths in ascending order of fitness values and assign them to the flames as the spatial positions of the flames in the first generation. When the iteration number is 1, the number of moths is the number of flames. In the MFO algorithm, each moth has a corresponding flame, and the moth flies along the corresponding flame to update its own position. This unique corresponding method enables the moths to search fully in the global exploration space, thus avoiding falling into the local optimum situation and enhancing the optimization ability. Therefore, the dimension of the flame is equal to the dimension of the moth, and the matrix of the flame positions is shown as follows:

[0086]

[0087] The fitness values of the flames are stored in the array OF, which is expressed as follows:

[0088]

[0089] Step (d): The moths update their positions according to the logarithmic spiral curve, re-sort the fitness values of the updated flame positions and the moth positions, and take the current optimal value as the flame position for the next iteration. The position update function for the moths flying around the flames is:

[0090] S(M i ,F j )=D i ·e bt ·cos(2πt)+F j

[0091] where b is the logarithmic spiral shape constant, the distance parameter t is a random number in [-1, 1]. When the value of t is 1, the distance of the moth relative to the flame is far, and when the value of t is -1, the distance of the moth relative to the flame is near. M i represents the i-th moth, F j represents the j-th flame, D i is the distance between the i-th moth and the j-th flame, and its formula is: D i =|F j -M i |.

[0092] The position update function S(M i , F j ) starts from the moth as the initial point and ends at the flame position. The amplitude is between the upper bound vector ub and the lower bound vector lb. At the beginning of the MFO algorithm, each moth and flame need to be initialized to a value less than the upper bound vector and greater than the lower bound vector. The dimensions of ub and lb are the same as those of the moth and flame, and are expressed as follows:

[0093] ub = [ub1, ub2, ub3, …, ub n-1 , ub n

[0094] lb = [lb1, lb2, lb3, …, lb n-1 , lb n

[0095] where, ub i represents the i-th upper bound vector, and lb i represents the i-th lower bound vector.

[0096] Step 5: Adaptively update the flame according to the following formula to reduce the number of flames:

[0097]

[0098] where, flame_no represents the current number of flames, N represents the maximum number of flames, that is, the number of flames after initialization, and T is the maximum number of iterations.

[0099] Step (e): Determine whether the maximum number of iterations T is reached. If so, proceed to the next step; otherwise, return to Step 3.

[0100] Step (f): Output the obtained optimal solution, which is the approximate optimal solution of the total communication rate searched. According to the optimized best position, recalculate the LOS channels between the RIS and the base station, the RIS and the users in the reflection area and the transmission area, re-estimate the small-scale fading and calculate the large-scale fading to form the final channel model. According to the final channel model, calculate the total communication rate at the receiving end. By calculating the rate of each user at the receiving end and summing up the rates of all users, the total communication rate of the system is obtained.

[0101] The effects of this application are described in detail below with reference to the simulation.

[0102] ​​This application conducts simulations on the RIS location optimization method based on regional division and moth-flame optimization in a transmissive RIS-enabled wireless transmission system to verify the superiority of the proposed solution in this application. The specific steps are as follows: The set basic parameters are the coordinates of the base station and users. Among them, there are two users in each of the refraction area and the reflection area. The base station coordinates are (0, 0), and the user coordinates are (20, -50), (80, -100), (30, 40), (90, 80). The coordinate range of the STAR-RIS is x I ∈[20, 100], y I ∈[-50, 40], and the number of electromagnetic units of the STAR-RIS is 8. When the reference distance is 1 meter, the large-scale fading coefficient is -30 dB, and the noise power σ 2 =-95 dBm. This application uses the Rice fading model for channel modeling, and the Rice factor is 5. Considering that the STAR-RIS is placed in a relatively high and open position, and the communication between the base station and users only passes through the LOS channel. Among them, the path loss exponent of the direct link from the base station to the users in the reflection area is 2.8, the path loss exponent of the direct link from the base station to the users in the refraction area is 3.0, the path loss exponent of the direct link between the base station and the STAR-RIS is 2.4, the path loss exponent of the direct link from the STAR-RIS to the users in the reflection area is 2.4, and the path loss exponent of the direct link from the STAR-RIS to the users in the refraction area is 2.2. The STAR-RIS adopts the energy splitting protocol, and all components work in the T&R mode, where the signal energy incident on each component is evenly divided into the energy of the transmitted signal and the reflected signal. When solving the optimization problem, the maximum number of iterations of the algorithm is T = 1000, and the threshold ε for ending the algorithm is 10 -3 .

[0103] As Figure 3 shown, it presents the regional division diagram of the transmissive RIS location deployment optimization method based on the combination of regional division and moth-flame algorithm in an exemplary embodiment. As can be seen from the figure, the optimization region of the transmissive RIS available for selection is divided into 2, 4, and 8 blocks using the uniform distribution method. This division method ensures that the size of each sub-region is equal, thus simplifying the complexity of the optimization calculation and being able to evenly cover the entire optional region. Based on this, the MFO algorithm will search within each sub-region to optimize the deployment location of the RIS. In this method, all sub-regions will independently perform the MFO algorithm optimization to ensure that a local optimal solution can be found within each sub-region and avoid being trapped in the dilemma of local optimal solutions through the global optimization algorithm. Through this optimization method combining regional division and moth-flame algorithm, it is possible to balance the search accuracy and calculation efficiency in different sub-regions, effectively improve the optimization effect of RIS deployment, and further improve the communication performance of the system.

[0104] As Figure 4As shown, it presents the convergence graph of the RIS position optimization method based on region division and moth-flame optimization in a transmissive RIS-enabled wireless transmission system under the parameters of an exemplary embodiment. It can be seen from the figure that after about 120 iterations of the MFO algorithm, the objective function value rapidly approaches the optimal solution and gradually stabilizes, indicating that the algorithm has good global search ability; after about 400 iterations, the convergence curve hardly shows obvious fluctuations, which indicates that the solution of the MFO algorithm has strong convergence in the later stage and is not easily trapped in local optima, and finally finds an approximate optimal solution; the output results show that after optimization by the MFO algorithm, the communication performance of the system has been improved and the optimization goal has been achieved.

[0105] As Figure 5 shown, to obtain a global optimal solution with higher precision, the search area of the STAR-RIS is divided into 2, 4, and 8 blocks, and compared with the total communication rate at random positions. It can be seen from the figure that the following conclusions can be drawn: as the search area of the STAR-RIS is continuously subdivided, the total communication rate gradually increases. This indicates that finer partitioning can search possible RIS positions more comprehensively and improve the accuracy of the global optimal solution of the system. The MFO algorithm is significantly better than random positions, and the blue solid line (MFO optimization result) in the figure is significantly higher than the red dashed line (random position), indicating that the MFO algorithm is significantly better than randomly selected positions in RIS position optimization and can better improve the communication performance of the system. The marginal effect of partitioning accuracy on performance improvement, when the number of partitions increases from 4 to 8, the improvement amplitude of the total communication rate tends to level off, indicating that further increasing the number of partitions has limited effect on performance improvement. A higher number of partitions can provide a more accurate RIS position optimization scheme, making the system closer to the global optimal solution. However, an excessive number of partitions may lead to a significant increase in computational complexity.

[0106] Through the above RIS position optimization method based on region division and moth-flame optimization in a transmissive RIS-enabled wireless transmission system, the position of the STAR-RIS can be effectively optimized by the MFO algorithm to maximize the total communication rate and significantly improve the communication performance of the system. Moreover, this method has global search and dynamic adaptation capabilities, can optimize the position of the STAR-RIS in real time to cope with channel condition changes, shows excellent performance in mobile devices and complex multi-user scenarios, and is applicable to line-of-sight communication and environments with severe blockages. The MFO algorithm has good global convergence, avoids being trapped in local optima, and makes the optimization results more stable and reliable. The lightweight framework of the MFO algorithm reduces computational complexity, shortens the optimization time, and improves resource utilization efficiency.

[0107] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A transmission RIS position deployment optimization method based on the combination of region division and moth-flame optimization, characterized in that The steps are as follows: Step 1: Utilize the STAR-RIS-assisted scenario. Based on the system's known channel state information (CSI), construct the location optimization model of the STAR-RIS; obtain the location optimization model of the STAR-RIS under the condition of known CSI, and construct the transmission models for the users on both sides of the receiving-end reflection area and refraction area; Step 2: According to the transmission models for the users on both sides of the receiving-end reflection area and refraction area, obtain the signal-to-interference-plus-noise ratio (SINR) of the users on both sides; Step 3: According to the transmission models for the users on both sides of the receiving-end reflection area and refraction area and the SINR of the users on both sides of the receiving end, obtain the communication rates of the users on both sides of the receiving end, and obtain the maximum total communication rate model of the receiving end; Step 4: Divide the deployment range of the STAR-RIS, i.e., the optimization area, into N sub-regions, and use the MFO algorithm to optimize the sub-regions to obtain the approximate optimal solution of the total communication rate, i.e., the optimized best location; By calculating the rate of each user at the receiving end and summing up the rates of all users, the total communication rate of the system is obtained.

2. The transmission RIS position deployment optimization method based on the combination of region division and moth-like navigation according to claim 1, characterized in that: The STAR-RIS adopts an energy splitting protocol, and the communication between the base station and the users is through the LOS channel.

3. The transmission RIS position deployment optimization method based on the combination of region division and moth-like optimization according to claim 2, wherein: There is a direct link from the base station to the users and a reflected / refracted link propagated through the STAR-RIS. The constructed location optimization model of the STAR-RIS is a Rice fading channel model: Wherein, D is the actual transmission distance; α is the path loss exponent; K is the Rice factor; and is the phase.

4. The transmission RIS location deployment optimization method based on the combination of region division and moth-like approach according to claim 2, characterized in that: The STAR-RIS-assisted scenario is set with N electromagnetic units and is equipped with d antennas. The base station transmits signals to M1 users in the reflection area R and M2 users in the transmission area T through the STAR-RIS, and there is a direct link from the base station to the reflected users and refracted users.

5. The transmission RIS position deployment optimization method based on the combination of region division and moth-like navigation according to claim 1, characterized in that: The transmission model of the i-th user in the receiving-end reflection area is: The transmission model of the k-th user in the refraction area is: Among them, is the channel state matrix from the base station to the i-th user in the R area, is the channel state matrix from the base station to the k-th user in the T area, is the channel state matrix from the STAR-RIS to the i-th user in the R area, is the channel state matrix from the STAR-RIS to the i-th user in the T area, is the reflection coefficient matrix of the STAR-RIS, is the refraction coefficient matrix of the STAR-RIS, where β N and θ N are the modulation of the signal amplitude by the n-th reflection unit on the RIS and the phase shift of the reflection unit respectively, is the channel state matrix from the base station to the STAR-RIS, X = ws is the transmitted signal, where w is beamforming, and represents thermal noise.

6. The transmission RIS position deployment optimization method based on the combination of region division and moth - like optimization according to claim 1, characterized in that: According to the user's transmission model, the SINR of the user at the receiving end is expressed as: The SINR of the refraction area is expressed as:

7. The transmission RIS position deployment optimization method based on the combination of region division and moth-like navigation according to claim 1, characterized in that: The communication rate \(R_{L}\) of the receiving - end user is \(R_{L}=\ln(1 + \text{SINR})\), and the total communication rate of the users on both sides of the receiving end is:

8. The transmission RIS position deployment optimization method based on the combination of region division and moth flying into the fire according to claim 1, characterized in that: When divided into N sub-regions, the size of each sub-region is equal.

9. The transmission RIS position deployment optimization method based on the combination of region division and moth - like optimization according to claim 1, characterized in that: When using the MFO algorithm to optimize the sub-regions, within each sub-region, the moths will search along the gradient of the objective function to find the optimal RIS location.

10. The transmission RIS position deployment optimization method based on the combination of region division and moth-like flocking according to claim 1, characterized in that: The MFO algorithm optimizes to obtain the best location. According to the best location, recalculate the LOS channels between the RIS and the base station, between the RIS and the users in the reflection area and transmission area, re-estimate the small-scale fading and calculate the large-scale fading to form the final channel model. According to the final channel model, calculate the total communication rate of the receiving end.

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