Intelligent metasurface optimal position deployment method and system in electric power scene

Abstract

The invention provides an intelligent metasurface optimal position deployment method and system in an electric power scene, and the method is characterized in that the method comprises the following steps: constructing a single-input single-output system model for the inspection of a multi-intelligent metasurface (RIS)-assisted unmanned aerial vehicle, the system comprises a base station, an unmanned aerial vehicle and a plurality of RISs, the RIS is deployed in a candidate site set # imgabs0 # between a base station and an inspection area, the height # imgabs1 # azimuth angle theta i and the number Ti of daughter boards of each RIS are adjustable, and the inspection area is divided into N grids; establishing a channel model, and calculating average power gains of direct connection and cascade channels; an optimization problem with the goal of minimizing the total deployment cost is constructed, the total deployment cost comprises RIS site fixed cost and daughter board hardware cost, and the constraint condition is that the system coverage rate is not lower than a preset threshold value eta min; and converting the optimization problem into an integer linear programming problem, introducing a binary variable to represent RIS deployment configuration, and solving through a branch definition algorithm to obtain the optimal RIS position, height, azimuth angle and daughter board number.

Description

Technical Field

[0001] The present invention discloses a method and system for optimally deploying intelligent metasurfaces in power scenarios. This method addresses the signal blind spot problem caused by complex terrain during power inspections by jointly optimizing the position, height, azimuth, and number of sub-boards of a RIS (Remotely Integrated Circuit) (RIS) to ensure communication coverage while reducing deployment costs. Background Art

[0002] In power scenarios, the optimal deployment of smart metasurfaces faces many challenges. Power transmission lines are widely distributed, and in complex scenarios such as mountainous and forested areas, the undulating terrain can block communication signals, creating signal blind spots. Therefore, further research is needed to determine the optimal deployment algorithm for smart metasurfaces in such complex electromagnetic environments. Traditional smart metasurface deployment methods are difficult to meet the special needs of power scenarios. Traditional algorithms are mostly based on simple signal propagation models. When faced with signal obstruction and special terrain in power scenarios, they cannot accurately consider the combined impact of factors such as multipath effects and electromagnetic interference on signal propagation. This makes it difficult to achieve the optimal deployment location and effectively improve communication quality and signal coverage.

[0003] The flight trajectory of drones during power inspections changes dynamically, requiring the intelligent metasurface's deployment to adapt to these dynamics, adjusting beam direction and angle in real time to ensure uninterrupted communication with the drone. Existing deployment algorithms lack real-time performance when dealing with these dynamics. Existing research on the optimal deployment of multiple RISs has limitations, lacking sufficient consideration of RIS orientation and height optimization. This paper considers the height and orientation of RISs and investigates the deployment of multiple RISs. Multiple RISs are optimally placed within a target area to improve signal coverage with the drone. The paper also considers the cost of RIS deployment and examines the impact of related factors on this cost. Summary of the Invention

[0004] The present invention aims to provide a method and system for optimal location deployment of smart metasurfaces in power scenarios, which mainly includes the following steps: first, construct a system model, divide the inspection area into grids, define parameters such as the location, height, azimuth, and number of sub-boards of RIS candidate sites, and establish direct channel and cascade channel models to calculate the signal power of each grid; then construct an optimization problem with the goal of minimizing the total deployment cost including fixed costs and hardware costs, and with the system coverage rate not lower than the threshold as a constraint, convert the problem into an integer linear programming model; finally, use a branch and bound algorithm to solve the model, and through operations such as variable conversion, linearization processing, and branch pruning, enumerate candidate solutions and screen out the optimal RIS deployment combination, including the location, height, azimuth, and number of sub-boards of specific sites, thereby achieving the optimal location deployment of smart metasurfaces in power scenarios. The specific technical solution is as follows:

[0005] Step 200: Build a system model. The drone inspection area must be divided into multiple square grids according to a specific rule (e.g., 10-meter side length) to enable detailed analysis of communication quality in each area. Simultaneously, a set of candidate deployment sites for the intelligent metasurface (RIS) is determined. Each site has parameters such as adjustable height (discrete values ​​such as 20 meters and 30 meters), azimuth angle (discrete angles such as 0° and 45°), and the number of daughterboards (maximum number limited by hardware).

[0006] Step 210: Establish a direct channel model from the base station to the grid, taking into account the impact of multipath effects and terrain obstruction on signal power. At the same time, build a cascade channel model from base station to RIS to grid, introduce effective reflection gain to characterize the signal amplification capability of RIS, and calculate the average power gain of the two types of channels using the far-field propagation model to provide a data basis for subsequent coverage analysis.

[0007] Step 220: Construct an optimization problem. Minimizing the total deployment cost is the core objective. The total cost is composed of the fixed costs of the RIS site (installation, control, etc.) and the daughterboard hardware cost (proportional to the number of daughterboards). A system coverage constraint is set, requiring that the proportion of grid cells within the inspection area that meet the signal power threshold be no less than a preset value (e.g., 90%). Parameters such as the RIS height, azimuth, number of daughterboards, and deployment status are converted into binary variables. An integer linear programming (ILP) model is established to transform the complex multidimensional optimization problem into a computer-solvable mathematical expression, ensuring that the model accurately reflects the balance between cost and coverage performance.

[0008] Step 230 uses a branch-and-bound algorithm to solve. First, the optimization variables are transformed, using binary variables to represent the specific deployment configuration of each candidate site (such as the combination of altitude, azimuth, and number of sub-panels), and a single-site single RIS constraint is imposed. By introducing auxiliary variables, the nonlinear coverage constraint is linearized to facilitate algorithm processing. Then, the branch-and-bound method is used to branch the undetermined variables (trying to select or not select). The cost lower bound is obtained by solving the relaxed linear programming problem. Branches with a lower bound higher than the current optimal solution are pruned to eliminate non-optimal solutions. After iterative enumeration and screening, the optimal RIS deployment plan is finally output, including specific parameters such as the altitude, azimuth, and number of sub-panels of each site, to achieve the optimal balance between coverage performance and deployment cost.

[0009] Beneficial effects

[0010] The beneficial effects of this invention are significant and multi-dimensional. Through technological innovation and optimized design, it systematically solves the core problem of deploying intelligent metasurfaces in power scenarios, which is specifically reflected in the following aspects:

[0011] In complex environments such as mountainous and forested areas, traditional deployment methods only optimize the horizontal position of the RIS, making it susceptible to terrain obstruction and resulting in signal blind spots. This invention constructs a non-line-of-sight (NLOS) communication link through the combined optimization of three-dimensional spatial parameters (altitude, azimuth, and position), effectively overcoming obstruction limitations. Through grid-level channel modeling (dividing the inspection area into 10m×10m grids), the effective channel power of each grid is accurately calculated, ensuring a coverage assessment error of less than 5%. Compared to random deployment solutions, this method improves system coverage by 20%-30%.

[0012] This invention significantly reduces deployment costs and efficiently allocates resources. By utilizing an integer linear programming model and a branch-and-bound algorithm, it achieves optimal control of deployment costs while meeting coverage requirements. Simulation data shows that compared to a fixed deployment solution, this invention can reduce the number of RIS sites by 30%-50%, lowering total costs by 37%. For example, when the coverage threshold is 0.95, the fixed deployment cost is 290, while the present invention only requires 180, a 37% reduction. The random deployment solution costs 250, but this method still reduces costs by 28%. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Deploy a flow chart for the optimal location of multiple RIS;

[0014] Figure 2 This is a schematic diagram of multi-RIS assisted drone inspection;

[0015] Figure 3 This is a simulation diagram of the relationship between system coverage threshold and RIS deployment cost;

[0016] Figure 4This is a simulation diagram of the relationship between the number of RIS daughter board units and deployment cost; DETAILED DESCRIPTION

[0017] In order to clarify the purpose, implementation scheme and technical advantages of the present invention, the present invention is further described in detail below in combination with specific examples and with reference to the accompanying drawings.

[0018] Step 200: Build a system model. The basic parameters are set as follows: the base station coordinates are (0, 0, 0) meters, the operating frequency is 5.8 GHz, and the transmission power is 20 dBm. The drone patrols along the x-axis at a height of about 30 meters. The size of each grid is 10 m × 10 m. The position coordinates of the RIS are obtained by the algorithm of the present invention. The fixed deployment cost of the RIS is c. s,0 The hardware cost of RIS daughter board is 40. h,0 20, RIS optional height set RIS optional angle set set up Represents the set of all candidate RIS deployment points in the region, Denote the coordinates of candidate RIS site i, where i∈I0. represents the subset of candidate sites selected for RIS deployment, i.e.

[0019] Since RIS can only reflect signals to and from its front half space, the direction of each RIS is another important parameter that affects its signal reflection and coverage performance. Assuming that the RIS plane is perpendicular to the ground, let RIS i Normal vector in 3D coordinate system, [·] T Denotes the transpose of the matrix. Let θ i Relative to w i The azimuth rotation angle of n i Denoted as θ i The function points outward from RIS, that is:

[0020]

[0021] In order to actually install the RIS at each candidate site, it is assumed that each RIS consists of many sub-panels of the same fixed size. Let M be the number of reflective units per sub-panel, T i RIS i The number of daughter boards on the RIS i The total number of reflection units on Given the practical limit on the number of RIS daughter boards, assume that the maximum number of daughter boards that can be deployed at each candidate site is expressed as For each site i where RIS is not deployed, set Ti = 0. For convenience, only the number of sub-boards related to the candidate sites where RIS is deployed is mentioned below, i.e. use A set representing the number of RIS daughter boards deployed at the candidate site.

[0022] Step 210: construct a channel model using h 0,n Characterizing the Base Station and Drone Grid For the uniform square area at the UAV grid n, a local two-dimensional (2D) coordinate system is established, where the xoy plane represents the plane where the UAV grid n is located and the origin is located at the center of the UAV grid n. 0,n represents the number of primary channel paths from the base station to the UAV grid n, and the path response vector from the base station to the reference point of the UAV grid n is: ,in represents the complex path gain of the lth path from the base station to the UAV grid n. In this invention, it is assumed that the antenna directional gain of the base station is fixed, so the complex path gain between them is known a priori. In addition, the position of any point in the UAV grid n is expressed in its local Cartesian coordinate system as Then using basic geometry, we can transform the position r n The signal propagation distance difference between the reference point and the lth path is expressed as:

[0023] in denote the elevation angle AOD and azimuth angle AOA of the lth path respectively. Therefore, the position r n The channel response of the lth path at the reference point has The phase difference is λ, where λ is the carrier wavelength. In order to consider all L 0,n These phase differences on the receiving path define the received field response vector of the UAV grid n in the receiving area from the base station as: Therefore, from the base station to any point r in the UAV grid n n The direct channel is modeled as: Use f respectively 0,i and g 0,i Indicates base station and RIS i The channels between any units on the network and between RIS and mesh n, note that unlike the direct channel between the base station and mesh n, RIS i The relevant channel depends on its deployment height h i and direction θ i For a uniform planar array (UPA) based RIS with any given height and orientation i, a local coordinate system parallel to the yoz plane is established, where the lower left corner of the array is defined as the origin and reference point. For any given s i , let L 0,i (s i ) and L i,n (s i ) represent the distance from the base station to the RIS i and from RIS i The number of paths to UAV grid n.

[0024] From the base station to the RIS i Reference point and from RIS i Vector representation of the reference point to the UAV grid n: in and Respectively represent the distance from the base station to the RIS i Reference point and from RIS i The complex path gain of the path l to the reference point of the UAV grid n is The same is known a priori. i Represents the mth reflection unit of the i-th RIS, and the RIS i At any specific unit m i The index pair is represented as Corresponding to y i and z i Direction, yes and There is a unique mapping between them, namely: Therefore, the mth i The coordinates of a reflection unit in the coordinate system are expressed as where Δ represents the cell spacing.

[0025] For any given s i ,RIS i mth i The response vector of the receiving base station of the unit is:

[0026]

[0027] Among them and Respectively represent the distance from the base station to the RIS i The AOD and AOA of the lth path, Similarly, RIS i and the mth of the UAV grid n i The sending and receiving signals of each unit are defined as h i,n (s i ,r n ) and g i,n(s i ,r n ). Based on the above formula, from the base station to the RIS i mth i The channels of each unit are: Similarly from RIS i The mth i cells to any point r in the grid n n The channels are: where w i,n (s i ) indicates that the altitude and direction s are determined in RIS i The coordinates of candidate site i are calculated by h 0,n (r n The average power gain of the base station to the grid channel n can be obtained by dividing the square of the amplitude of ) by the average value of n, as follows:

[0028]

[0029] in, represents the l′th path from the base station to the UAV grid n, and l′<l, it can be observed that ρ l,l′ The upper bound is In addition, if the angle and different from each other so that their sine and cosine terms result in A l,l′ and B l,l′ is non-zero, then each term ρ in the above formula l,l′ will be close to zero, because the side length δ of each grid is considered to be much larger than the wavelength, i.e. δ>>λ. Therefore, It can be approximated as: This approximation describes the impact of the primary channel path from the base station to the UAV grid n on the large-scale channel power gain average on any grid n, which will be helpful for the subsequent RIS deployment design.

[0030] Similarly, for any given h i and θ i , base station and RIS i The average channel power gain between any two units is given by The square value of the average value of all its reflection units is given by the following formula:

[0031]

[0032]

[0033] in, It can be observed that, assuming all channel paths are distinguishable in the angular domain, then ρ l,l′Each term of will approach zero because therefore, It can be approximated as:

[0034] Similarly, for any given h i and θ i , the channel average power is calculated as the average of the square amplitudes of all its reflection units and all points on the grid, which is approximately: Finally, for any given s i and T i , computing cascade base station-RIS i -The average channel power gain of the UAV link, expressed as |h 0,n (s i ,T i )| 2 In practical applications, the phase shift matrix of each RIS should be designed as the actual channel of the UAV at a specific location in different grids to maximize the passive reflection gain. For line-of-sight links, its upper bound is T i 2 M 4 (including aperture gain T i M 2 and beamforming gain T i M 2 ). For the considered multipath channels, achieving an upper bound on the beamforming gain is generally infeasible, and designing the phase shift matrix of each RIS to obtain the maximum reflection gain for each UAV at any location is analytically intractable.

[0035] In order to make the RIS deployment problem tractable and the proposed solution robust to the spatially varying channels of UAVs within each grid, an approximate base station-RIS i -UAV cascade channel power gain without the need for complex phase shift design based on the exact channel at different locations of the UAV. To this end, the effective reflection gain of each RIS is defined to characterize the average power amplification it contributes to the cascade channel. On the one hand, the RIS i From the base station to the RIS i By collecting the signal power in the link, we can get T i M 2 For any given s i , let L 0,i (s i ) and L i,n (s i ) represent the distance from the base station to the RIS i and from RIS i The number of paths to UAV grid n. RIS iThe reflection gain needs to be allocated to L 0,i (s i )L i,n (s i ) of the incident and reflected channels, RIS i The effective power gain is Cascade Base Station-RIS i The average channel power gain of the UAV link can be approximated as in, Avoiding the complex phase shift design for each RIS while effectively capturing the fundamental reflection gain in practice simplifies the optimization of multi-RIS deployment in the next section.

[0036] Step 220, construct an optimization problem with the core goal of minimizing the total deployment cost. The total cost is composed of the fixed costs of the RIS site (installation, control, etc.) and the daughterboard hardware cost (proportional to the number of daughterboards). Set the system coverage constraint, that is, require that the proportion of grids that meet the signal power threshold in the inspection area is not less than a preset value (such as 90%). Convert the parameters such as the height, azimuth, number of daughterboards and deployment status of the RIS into binary variables, establish an integer linear programming (ILP) model, and convert the complex multi-dimensional optimization problem into a mathematical expression that can be solved by a computer to ensure that the model can accurately reflect the balance between cost and coverage performance. The overall average channel power gain (i.e., effective channel power gain) of the direct link and the cascaded link of the RIS can be expressed as: In addition, when the effective channel power gain is lower than the minimum threshold P min Therefore, for any given Define the system receiving coverage as where u(·) is a step function.

[0037] The coverage defined in the above formula is used to describe the long-term coverage performance of the inspection area based on the average channel power gain. However, during the RIS deployment phase, no instantaneous CSI of any UAV is available. In this paper, we focus on the CSI-based RIS deployment problem.

[0038] The goal of this paper is to jointly optimize the RIS deployment locations, as well as their associated heights, orientations, and number of daughterboards, to minimize the total deployment cost, subject to the constraints of coverage performance and practical RIS deployment considerations. The relevant optimization problem is formulated as:

[0039]

[0040] Where η0≤1 is the specified system coverage threshold. For special cases where RIS is not allowed, that is, The system receiving coverage is given by Given. This optimization problem is a combinatorial optimization problem. To solve this problem, we will first reformulate this optimization problem as an integer linear programming problem, which can be optimally solved using the branch and bound algorithm for feature subset selection.

[0041] Step 230, use the branch and bound algorithm to solve. First, transform the optimization variables, use binary variables to represent the specific deployment configuration of each candidate site (such as the combination of altitude, azimuth, and number of sub-boards), and impose a single-site single RIS constraint. By introducing auxiliary variables, the nonlinear coverage constraints are linearized to facilitate algorithm processing, and then the branch and bound method is used to branch the undetermined variables (try to select or not select), and the cost lower bound is obtained by solving the relaxed linear programming problem. The branches with a lower bound higher than the current optimal solution are pruned to eliminate non-optimal solutions. After iterative enumeration and screening, the optimal RIS deployment plan is finally output, including specific parameters such as the altitude, azimuth, and number of sub-boards of each site, to achieve the optimal balance between coverage performance and deployment cost. The first step is to introduce a set of binary variables, in j∈{1,...,|H i |}, represents the optional deployment configuration (i.e., number of daughter boards, height, and direction) of any candidate RIS site i. Specifically, if candidate site i is selected to deploy with t daughter boards and a height of And the direction is RIS, then If RIS is not deployed,

[0042] Each RIS site is allowed to deploy at most one RIS, and the variable The following linear constraints are imposed:

[0043] This constraint ensures that for any i, if If , RIS should be deployed at candidate site i; otherwise, RIS should not be deployed at the site. Therefore, the deployment cost can be reformulated in linear form Furthermore, the coverage constraint needs to be reformulated to achieve a linear representation where Can be rewritten as in, therefore can be rewritten as The nonlinear step function involved cannot be directly expressed as a single linear function, so an additional set of binary variables needs to be introduced in Indicates P n (Ξ)≥Pmin Each of these variables is subject to the following constraints Ensure that for any n, if but otherwise N is the total number of grids. Therefore, It can be expressed in linear form: Based on the variables and constraints introduced above, the optimization problem can be equivalently transformed into the following ILP problem:

[0044]

[0045] The above optimization problem involves binary variables and (1+N+I0) linear inequality constraints. For each RIS candidate site i, the number of variable combinations is Therefore, the number of all RIS site combinations is In addition, a set of binary variables are introduced in Indicates the coverage of each grid. There are N grids in total, so the total number of binary variables is Among the constraints, the constraint on coverage rate This is a linear inequality constraint, counted as 1, for each grid There are constraints There are N constraints. In addition, for each candidate site Constrained This yields I0 constraints. So the total number of linear inequality constraints is (1+N+I0), so the problem can be solved optimally using the branch-and-bound algorithm, which systematically solves a series of linear programming problems. In the worst case, the computational complexity of the branch-and-bound algorithm is close to the full enumeration, which is about However, the branch-and-bound algorithm usually takes much less running time than a full enumeration because it effectively prunes solution sets that do not produce an optimal solution.

[0046] In order to verify the performance of the RIS optimal deployment algorithm adopted by the present invention, a simulation verification was carried out, and the simulation results are analyzed as follows. Figure 3As shown in the figure, simulations were performed for the cases of 2 RIS and 4 RIS, respectively. As can be seen from the figure, the overall deployment cost of deploying 4 RIS is higher than that of deploying 2 RIS. Moreover, the deployment cost of the present invention is lower than that of the fixed deployment and random deployment schemes. Taking the deployment of 2 RIS as an example, as the system coverage threshold increases from 0.6 to 0.95, the cost of the fixed deployment RIS scheme increases linearly and rapidly (the cost is 120 when the system coverage threshold is 0.6, and increases to 290 when the system coverage threshold is 0.95). Although the random deployment RIS has a lower initial cost, the lack of optimization leads to low coverage efficiency, and the deployment cost at high coverage thresholds rises sharply to 250. In contrast, the optimal deployment algorithm proposed in the present invention ensures that the effective reflection gain of the RIS is maximized through intelligent node layout, and the RIS provides optimal coverage, thereby allowing the BS to establish a stronger channel path with the drone grid through the RIS. This method improves coverage and reduces the number of sites and sub-boards required for RIS deployment, thereby greatly saving deployment costs and showing significant economic advantages.

[0047] Figure 4 The relationship between the RIS deployment cost and the number of RIS daughter board units is presented. Taking two RIS as an example, as the number of RIS units increases, the RIS deployment costs under the three deployment methods all show an upward trend. That is to say, under the same coverage threshold conditions, as the number of units increases, the number of required daughter boards will decrease. However, the increase in the daughter board cost brought about by the number of units is greater than the production cost of the daughter board, so the overall deployment cost shows an upward trend. Under the same number of RIS units, the cost of fixed deployment RIS and random deployment RIS is always higher than the cost of the optimal deployment algorithm of the present invention. For example, the solution of the present invention controls the cost to 150 when the number of units is 300, which is 20% lower than the cost of fixed deployment and 15% lower than random deployment, and the cost growth rate curve shows an increasing effect, which verifies the superiority of the solution of the present invention in large-scale deployment scenarios.

Claims

1. A method and system for optimal deployment of intelligent metasurfaces in power scenarios, characterized by: The following steps are involved: Construct a single-input single-output system model of a multi-intelligent metasurface (RIS) to assist drone inspections. The system includes a base station, a drone, and multiple RISs, which are deployed at a set of candidate sites between the base station and the inspection area. The height of each RIS Azimuth angle θ i And the number of daughter boards T i The inspection area is divided into N grids; a channel model is established to calculate the average power gain of the direct channel from the base station to the grid, and the average power gain of the cascade channel from the base station to the RIS to the UAV grid; an optimization problem is constructed with the goal of minimizing the total deployment cost, which includes the fixed cost of the RIS site and the hardware cost of the daughterboard, with the constraint that the system coverage is not less than the preset threshold η min ; Convert the optimization problem into an integer linear programming problem, introduce binary variables to represent the RIS deployment configuration, and solve it through a branch and bound algorithm to obtain the optimal RIS position, height, azimuth angle and number of sub-boards.

2. The method according to claim 1, characterized in that The existing invention only considers the location of RIS deployment as a parameter, while the present invention additionally considers the height of RIS, the azimuth of RIS and the number of RIS sub-boards. The height of RIS is obtained from the discrete set Azimuth angle θ i By normal vector Represents, taken from the discrete set Number of daughter boards T i satisfy The maximum number of daughter boards allowed at a single site.

3. The method according to claim 1, characterized in that The channel model is based on the far-field wireless channel, takes into account the multipath effect, and calculates the average channel power gain of each grid through grid division. Effective reflection gain Propagation distance and channel gain calculation, where M is the total number of RIS reflector units.

4. The method according to claim 1, wherein The total deployment cost expression is: in, represents the number of RIS sites, T i RIS i The number of daughter boards, represents the set of deployment height and steering angle, c s,0 represents the fixed cost of deploying RIS at the candidate site, including installation cost, control cost and other related expenses, where c h,0 It represents the hardware cost of deploying the RIS daughterboard, which is related to the number of RIS units and can be determined based on its manufacturing cost and power consumption.

5. The method according to claim 1, wherein By introducing a set of binary variables in represents the optional deployment configuration (i.e., number of daughter boards, height, and direction) of any candidate RIS site i. If candidate site i is selected to deploy with t daughter boards and a height of And the direction is RIS, then If RIS is not deployed, The coverage constraint is linearized through auxiliary variables to solve the integer linear programming problem.

6. A system for optimal location deployment of intelligent metasurfaces in power scenarios, characterized by: include: The system modeling module is used to build a multi-RIS-assisted UAV inspection system model, divide the inspection area into grids, and define parameters such as the height, azimuth, and number of sub-boards of the RIS candidate sites; The channel calculation module is used to calculate the average power gain of the direct channel and the cascaded channel and evaluate the effective channel power of each grid. The optimization solution module is used to build an integer linear programming model with the goal of minimizing deployment cost and solve the optimal RIS deployment configuration through a branch and bound algorithm. The deployment execution module is used to adjust the height, azimuth angle and number of sub-boards of the RIS based on the solution results to achieve optimized communication coverage in the drone inspection area.

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