A method and system for maximizing throughput of a wireless powered communication network

Abstract

The application discloses a wireless power supply communication network throughput maximization optimization method and system, comprising the following steps: constructing a wireless power supply communication network system based on a UAV under the assistance of an intelligent reflecting surface, constructing a mathematical model of maximizing the minimum ground user throughput while meeting the energy constraint, scaling the composite channel gain and introducing an auxiliary variable, realizing two-dimensional optimal layout of the UAV based on an SCA optimization algorithm; converting the power optimization problem and the time optimization problem into a convex optimization problem; constructing an optimization algorithm of the intelligent reflecting surface reflection coefficient optimization problem through an SDR algorithm; and obtaining the optimal two-dimensional hovering position of the UAV, the user transmission power and the allocation time through an iterative optimization method. The application applies UAV communication and intelligent reflecting surface technology to the wireless power supply communication network, proposes a minimum throughput maximization problem, solves the non-convex problem through application of the SCA algorithm and the SDR algorithm, and significantly enhances the calculation performance.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and specifically to a method and system for maximizing throughput optimization in wireless power supply communication networks. Background Technology

[0002] With the development of the Internet of Things (IoT), the ever-increasing communication demands are placing higher requirements on communication technologies. Reducing deployment costs, extending battery life, expanding coverage, and improving communication quality have become major challenges. Meanwhile, the ever-growing functionality of today's mobile devices requires more power to support all-day operation. Processing power, feature sets, and sensors are limited by battery life. Although device lifespan can be extended by replacing batteries or charging, these operations are inconvenient and costly. Therefore, how to improve the lifespan of wireless devices while ensuring communication quality will be a major challenge for both academia and industry.

[0003] As radio signals radiated by ambient transmitters have become a viable new source of wireless energy harvesting, wireless-powered communication networks have begun to receive widespread attention. This technology uses energy harvested from ambient radio frequency signals to power wireless terminals in the network; at the same time, wireless devices use the received energy to communicate in the network, thus solving the problem of wireless device lifespan constraints.

[0004] Due to their autonomy, flexibility, and maneuverability, drones can be rapidly deployed to provide reliable services to users in rural and geographically limited areas. Based on previous research on drone-based wireless networks, drones can further shorten transmission distances and increase channel gain, thus eliminating the aforementioned drawbacks. Therefore, applying drones to communication networks will significantly improve communication quality. Furthermore, the emergence of smart reflectors has also contributed to improving the communication environment. This technology reduces energy consumption and improves the spectral efficiency of wireless networks by reconfiguring the propagation environment of electromagnetic waves. Smart reflectors can control the phase angle or amplitude of incident signals, thereby enhancing the desired signal and reducing interference signals.

[0005] In the past, the main research focus was on wireless power communication networks assisted by UAVs or those assisted by smart reflectors. However, combining UAVs and smart reflectors and applying them to wireless power communication networks involves optimizing the two-dimensional layout of UAVs, user transmission power, time resources, and the reflection coefficient of smart reflectors. The optimization problem is non-convex and difficult to solve directly, which is challenging. Summary of the Invention

[0006] To overcome the defects and shortcomings of existing technologies, this invention provides a method and system for maximizing throughput in wireless power supply communication networks. Under the condition of satisfying energy constraints, it optimizes the two-dimensional position of UAVs, user transmission power, time resources, and intelligent reflective surface coefficient, thereby improving the lifespan of wireless devices and communication quality.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] This invention provides a method for maximizing throughput optimization in wireless power communication networks, comprising the following steps:

[0009] A wireless power supply and communication network system based on UAVs is constructed with the assistance of intelligent reflectors. While satisfying energy constraints, a mathematical model is built to maximize the minimum throughput of ground users, including: UAV two-dimensional position optimization problem, power optimization problem, time optimization problem, and intelligent reflector reflection coefficient optimization problem.

[0010] A two-dimensional position optimization problem for UAVs is constructed. By scaling the composite channel gain and introducing auxiliary variables, the optimal two-dimensional layout of the UAVs is achieved based on the SCA optimization algorithm.

[0011] Based on the optimized two-dimensional position of the UAV, the power optimization problem and the time optimization problem are transformed into convex optimization problems;

[0012] Based on the optimized UAV two-dimensional position, user transmit power, and allocation time, an optimization algorithm for the intelligent reflective surface reflection coefficient optimization problem is constructed using the SDR algorithm.

[0013] The optimal two-dimensional hovering position of the UAV, user transmit power, and allocation time are obtained through iterative optimization methods.

[0014] As a preferred technical solution, the construction of a UAV-based wireless power supply and communication network system with the assistance of an intelligent reflective surface specifically includes:

[0015] The wireless power supply communication network system is deployed with one drone and multiple ground users. The drone and all ground users' equipment are equipped with a single antenna. The drone charges the ground users on the downlink and receives information sent by the users on the uplink. The intelligent reflector has multiple reflective elements.

[0016] As a preferred technical solution, a mathematical model is constructed to maximize the minimum ground user throughput, specifically expressed as follows:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] Where T represents the total flight time of the drone, B represents the total number of areas the drone flies, and K represents the number of ground users. This represents the reflection coefficient matrix of the smart reflector during downlink power transfer. This represents the reflection coefficient matrix of the intelligent reflector during uplink information transmission, where M represents the number of intelligent reflector units, and θ... 1,M θ 2,M Both represent the phase shift of the Mth reflecting unit, w b,u f represents the two-dimensional coordinate position of the UAV in the b-th region. k For the channel from the smart reflector to the ground user, G b For the channel from the UAV to the smart reflector, h b,k For the channel from the drone to the ground user, P1 represents the constant power of the drone, P 2k η represents the transmission power from the user to the drone, η is the energy conversion efficiency at the ground user, and τ is the power of the data transmission. bP For the time it takes to transfer energy, τ kI This refers to the time it takes for the information to be transmitted.

[0024] As a preferred technical solution, the channel from the intelligent reflector to the ground user adopts a Ricean fading channel, the channel from the UAV to the intelligent reflector adopts a line-of-sight wireless transmission channel, and the channel from the UAV to the ground user adopts a Ricean fading channel.

[0025] As a preferred technical solution, a two-dimensional position optimization problem for UAVs is constructed, expressed as:

[0026]

[0027]

[0028]

[0029] Where t is an auxiliary variable that transforms the original problem from a minimization optimization problem into a maximization problem, T represents the total flight time of the UAV, B represents the total number of UAV flight areas, and K represents the number of ground users. This represents the reflection coefficient matrix of the smart reflector during downlink power transfer. This represents the reflection coefficient matrix of the intelligent reflector during uplink information transmission, where M represents the number of intelligent reflector units, and θ... 1，Mθ 2，M Both represent the phase shift of the Mth reflecting unit, w b，u f represents the two-dimensional coordinate position of the UAV in the b-th region. k For the channel from the smart reflector to the ground user, G b For the channel from the UAV to the smart reflector, h b，k For the channel from the drone to the ground user, P1 represents the constant power of the drone, P 2k η represents the transmission power from the user to the drone, and η is the energy conversion efficiency at the ground user.

[0030] The hybrid channel from drone to user is represented as:

[0031]

[0032]

[0033] Among them, |H b，k |for h b，k The modulus of |f k，i |for f k The modulus, d UG d represents the distance from the drone to the user. UR Let β0 represent the distance from the UAV to the smart reflector, α represent the channel power gain at the reference location, and λ represent the path fading factor. c Let ξ be the carrier wavelength, and ξ be (f k ) H Θ2G b and h b，k The phase difference;

[0034] By taking a lower bound on the hybrid channel gain to tighten the constraints, the inequality constraints are constructed as follows:

[0035]

[0036] Introducing scaling variable u b，k and v b The set condition is: d UG ≤u b，k d UR ≤v b

[0037] The inequality constraint is transformed into:

[0038]

[0039] Where, σ 2 Indicates noise power;

[0040] make After performing a Taylor expansion on the left-hand side of the inequality, we obtain the following inequality:

[0041]

[0042] Converting the energy conversion efficiency at ground users into:

[0043]

[0044] Where l represents the number of iterations;

[0045] Scaling variable u b，k and v b satisfy:

[0046]

[0047] d UR 2 -2v b l-1 v b +(v b l-1 ) 2 ≤0

[0048] Therefore, the original UAV two-dimensional position optimization problem can be transformed into

[0049]

[0050]

[0051]

[0052] d UG 2 -2u b，k l-1 u b，k +(u b，k l-1 ) 2 ≤0

[0053] d UR 2 -2v b l-1 v b +(v b l-1 ) 2 ≤0

[0054] The standard optimization tool is used to solve the two-dimensional position optimization problem of the UAV, and the optimized two-dimensional position of the UAV is obtained.

[0055] As a preferred technical solution, the power optimization problem and the time optimization problem are transformed into a convex optimization problem, specifically expressed as:

[0056] Transmit power optimization problem:

[0057]

[0058]

[0059]

[0060] Time resource allocation optimization problem:

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] Where t is an auxiliary variable that transforms the original problem from a minimization optimization problem into a maximization problem, T represents the total flight time of the UAV, B represents the total number of UAV flight areas, and K represents the number of ground users. This represents the reflection coefficient matrix of the smart reflector during downlink power transfer. This represents the reflection coefficient matrix of the intelligent reflector during uplink information transmission, where M represents the number of intelligent reflector units, and θ... 1，M θ 2，M Both represent the phase shift of the Mth reflecting unit, f k For the channel from the smart reflector to the ground user, G b For the channel from the UAV to the smart reflector, h b，k For the channel from the drone to the ground user, P1 represents the constant power of the drone, P 2k η represents the transmission power from the user to the drone, η is the energy conversion efficiency at the ground user, and τ is the power of the data transmission. bP For the time it takes to transfer energy, τ kI This refers to the time it takes for the information to be transmitted.

[0068] As a preferred technical solution, the problem of optimizing the reflection coefficient of an intelligent reflective surface is expressed as follows:

[0069]

[0070]

[0071]

[0072] Where t is an auxiliary variable that transforms the original problem from a minimization optimization problem into a maximization problem, T represents the total flight time of the UAV, B represents the total number of UAV flight areas, and K represents the number of ground users. This represents the reflection coefficient matrix of the smart reflector during downlink power transfer. This represents the reflection coefficient matrix of the intelligent reflector during uplink information transmission, where M represents the number of intelligent reflector units, and θ... 1，M θ 2，M Both represent the phase shift of the Mth reflecting unit, f k For the channel from the smart reflector to the ground user, G b For the channel from the UAV to the smart reflector, h b，k For the channel from the drone to the ground user, P1 represents the constant power of the drone, P 2k η represents the transmission power from the user to the drone, η is the energy conversion efficiency at the ground user, and τ is the power of the data transmission. bP For the time it takes to transfer energy, τ kI For the time it takes to transmit information;

[0073] make Let |f| represent the reflected beamforming vectors of the smart reflector in the downlink and uplink, then: k ) H Θ1G b +h b，k | 2 =v1 H Q k v1+2Re{v1 H D k}+|h b，k | 2 ,|(f k ) H Θ2G b +h b，k | 2 =v2 H Q k v2+2Re{v2 H D k}+|h b，k | 2 ;

[0074] Among them, Q k =Φ k Φ k H D k =Φ k h b，k H Φ k =diag(f kH )G b ;

[0075] make in The problem of optimizing the reflection coefficient of a smart reflective surface is transformed into:

[0076]

[0077]

[0078]

[0079]

[0080] definition and satisfy and rank(V) i ) = 1, i = 1, 2;

[0081] Scaling the constraint condition rank(V) using the SDR algorithm i The optimization problem of the reflection coefficient of the intelligent reflective surface is ultimately expressed as:

[0082]

[0083]

[0084]

[0085] [V i ] n，n =1, n=1,..., M+1, i=1,2

[0086]

[0087] The problem of optimizing the reflection coefficient of a smart reflector is solved using standard optimization tools.

[0088] As a preferred technical solution, the optimal two-dimensional hovering position of the UAV, the user's transmission power, and the allocation time are obtained through iterative optimization methods, specifically including:

[0089] Initialize auxiliary variables, user power, and time allocation scheme;

[0090] Update the drone's two-dimensional hovering position, user transmit power, and time allocation until the increase in the objective function is less than a first preset threshold;

[0091] The optimal intelligent reflective surface reflection coefficient is obtained based on the optimized two-dimensional hovering position of the UAV, the user's transmission power, and the time allocation.

[0092] Repeat the iteration until the increase in the objective function value is less than the second preset threshold;

[0093] Output the optimal two-dimensional hovering position of the UAV, the optimal transmission power for the user, the optimal time allocation scheme, and the optimal reflection coefficient of the intelligent reflector.

[0094] The present invention also provides a wireless power supply communication network throughput maximization optimization system for implementing the above-mentioned wireless power supply communication network throughput maximization optimization method, comprising: a wireless power supply communication network system construction module, a mathematical model construction module for maximizing minimum ground user throughput, a UAV two-dimensional position optimization problem construction module, an optimization problem transformation module, a smart reflector reflection coefficient optimization problem construction module, and an iterative optimization module;

[0095] The wireless power supply and communication network system construction module is used to construct a UAV-based wireless power supply and communication network system with the assistance of an intelligent reflective surface.

[0096] The mathematical model building module for maximizing minimum ground user throughput is used to construct a mathematical model that maximizes minimum ground user throughput while satisfying energy constraints. This includes: two-dimensional position optimization problem, power optimization problem, time optimization problem, and intelligent reflector coefficient optimization problem for UAVs.

[0097] The UAV two-dimensional position optimization problem construction module is used to construct the UAV two-dimensional position optimization problem, scale the composite channel gain and introduce auxiliary variables, and realize the optimal two-dimensional layout of the UAV based on the SCA optimization algorithm;

[0098] The optimization problem transformation module is used to transform the power optimization problem and the time optimization problem into a convex optimization problem based on the optimized two-dimensional position of the UAV.

[0099] The intelligent reflector reflection coefficient optimization problem construction module is used to construct an optimization algorithm for the intelligent reflector reflection coefficient optimization problem based on the optimized UAV two-dimensional position, user transmit power and allocation time, using the SDR algorithm.

[0100] The iterative optimization module is used to obtain the optimal two-dimensional hovering position of the UAV, the user's transmit power, and the allocation time through iterative optimization methods.

[0101] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0102] This invention constructs a UAV wireless power supply and communication network model assisted by an intelligent reflector. It adopts the SCA optimization algorithm and the SDR algorithm to solve the technical problem of maximizing the minimum throughput of the system by optimizing the two-dimensional layout of the UAV, user transmission power, time resources and the reflection coefficient of the intelligent reflector. It also solves the problem of limited lifespan of wireless devices and greatly improves communication quality and the minimum throughput of the system. Attached Figure Description

[0103] Figure 1 This is a flowchart illustrating the method for maximizing throughput optimization in wireless power supply communication networks according to the present invention.

[0104] Figure 2 This is a model of the UAV wireless power supply and communication network system assisted by the intelligent reflective surface according to the present invention;

[0105] Figure 3 The figure shows the simulation results of this invention. Detailed Implementation

[0106] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0107] Example 1

[0108] like Figure 1 As shown, this embodiment provides a method for maximizing the throughput of a wireless power communication network, including the following steps:

[0109] Step 1: Construct a wireless power supply and communication network system based on UAVs with the assistance of intelligent reflective surfaces, and build a mathematical model that maximizes the minimum throughput of ground users while satisfying energy constraints.

[0110] like Figure 2 As shown, assume a wireless power communication network based on UAV communication and assisted by a smart reflector is deployed with one UAV and K ground users. The overall area is divided into B smaller areas, and the UAV and all ground equipment are equipped with a single antenna. The UAV charges the ground users via the downlink and receives information transmitted by the users via the uplink. The smart reflector has M reflecting elements. The coordinate position of ground user k is denoted as z. k =(x k y k The two-dimensional coordinate position of the UAV in the b-th region is represented as w. b，u =(x b，u y b，u And it remains at a height of h. The intelligent reflective surface is deployed at a height of H, with coordinates z. R=(x R y R H).

[0111] Time Division Multiple Access (TDMA) technology is used in wireless power supply communication networks. The total flight time T of the UAV is divided into B+K time slots. The first B time slots are used for downlink power transmission, and the remaining K time slots are used for uplink information transmission. Therefore, the constraint on the time variable can be expressed as:

[0112]

[0113]

[0114]

[0115] Assuming the drone transmits energy at a constant power P1, This represents the reflection coefficient matrix of the smart reflector during downlink power transfer. f represents the reflection coefficient matrix of the intelligent reflector during uplink information transmission. k For the channel from the smart reflector to the ground user, G b For the channel from the UAV to the smart reflector, h b，k The expressions for the channel from the drone to the ground user are as follows:

[0116]

[0117] Where β0 represents the channel power gain at the reference position. α represents the path fading factor, K R Represents Rice factor.

[0118]

[0119]

[0120] in,

[0121] Assume user P 2k If the power is used to transmit information to the drone, then the data transmission rate received by the drone is...

[0122]

[0123] Where, σ 2 This refers to the noise power of the information receiver at the drone.

[0124] To meet energy supply and demand, downlink energy transmission and uplink information transmission must satisfy the following constraints:

[0125] η|(fk ) H Θ1G b +h b，k | 2 P1τ bP ≥P 2k φτ kI (8)

[0126] Where η is the energy conversion efficiency at the user's location.

[0127] In wireless power communication networks, maximizing the system's minimum throughput can be described as:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] Step 2: First, optimize the two-dimensional position of the UAV. The original problem is described as a two-dimensional position optimization problem for the UAV. By scaling the composite channel gain and introducing auxiliary variables, an optimization algorithm based on SCA is constructed, achieving the optimal two-dimensional layout of the UAV.

[0135] To obtain the two-dimensional position of the drone, the following problems will be solved.

[0136]

[0137]

[0138]

[0139] By taking f k After being converted into the form of magnitude and phase angle, the hybrid channel of UAV-smart reflector-user can be expressed as:

[0140]

[0141] At the same time, h b，k It can also be expressed in the form of modulus and phase angle, as follows:

[0142]

[0143] Using the above expression, the hybrid channel from the UAV to the user can be represented as:

[0144]

[0145] Since the objective optimization problem is a minimization problem, the constraints can be tightened by taking a lower bound on the hybrid channel gain. Considering... Since all parameters are non-negative, the following inequalities hold.

[0146]

[0147] Meanwhile, due to the nonlinearity of the distance expression, a new scaling variable u is introduced. b，k and v b Both of these must satisfy d UG ≤u b，k d UR ≤v b Therefore, the inequality constraints in the original problem can be transformed into:

[0148]

[0149] For ease of expression, let A k =|H b，k | 2 ,

[0150] Since the first-order Taylor expansion of a convex function is a global lower bound, the Taylor expansion of the left side of the above inequality yields the following inequality.

[0151]

[0152] Similarly, (17) can be converted to

[0153]

[0154] Therefore, the original UAV two-dimensional position optimization problem can be transformed into

[0155]

[0156] (23)-(24) (26)

[0157] d UG 2 -2u b，k l-1 u b，k +(u b，k l-1 ) 2 ≤0 (27)

[0158] d UR 2 -2v b l-1v b +(v b l-1 ) 2 ≤0 (28)

[0159] This problem is a convex optimization problem, which can be solved using standard optimization tools such as CVX.

[0160] Step 3: Based on the obtained two-dimensional position of the UAV, the power optimization problem and the time optimization problem can be directly transformed into a convex optimization problem.

[0161] Transmit power optimization problem:

[0162]

[0163]

[0164]

[0165] Time resource allocation optimization problem:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172] Both of the above problems are convex optimization problems, which can be solved directly using standard solvers such as CVX.

[0173] Step 4: Based on the obtained UAV two-dimensional position, user transmission power and time allocation scheme, construct an optimization algorithm to optimize the reflection coefficient of the intelligent reflector using the SDR algorithm.

[0174] The problem of optimizing the reflection coefficient of a smart reflective surface is as follows:

[0175]

[0176]

[0177]

[0178] make Φ represents the reflected beamforming vectors of the smart reflector in the downlink and uplink. k =diag(fk H )G b Then we have |(f) k ) H Θ1G b +h b，k | 2 =u1 H Q k v1+2Re{v1 H D k}+|h b，k | 2 ,|(f k ) H Θ2G b +h b，k | 2 =v2 H Q k v2+2Re{v2 H D k}+|h b，k | 2 Q k =Φ k Φ k H D k =Φ k h b，k H The optimization problem can now be transformed into using v1 and v2 as optimization variables. However, the problem remains non-convex and can be solved using the SDR algorithm.

[0179] make in At this point, the original problem can be transformed into:

[0180]

[0181]

[0182]

[0183]

[0184] Next, by defining and The transformation problem must satisfy the following conditions: and rank(V) i ) = 1, i = 1, 2. But because rank(V i Since ) = 1 is non-convex, the SDR algorithm is applied to scale this constraint. Therefore, the final problem can be described as:

[0185]

[0186]

[0187]

[0188] [V i ] n，n =1, n=1,..., M+1, i=1, 2 (48)

[0189]

[0190] This problem is a convex semidefinite relaxation analysis, which can be solved using CVX. However, due to the scaling of the constraints, the results may not satisfy rank(V). i =1. When this condition is not met, the result can be processed using a Gaussian random process.

[0191] Step 5: Based on the constructed optimization algorithm with different variables, the optimal two-dimensional hovering position and resource allocation scheme of the UAV are finally obtained through iterative optimization. The specific algorithm is as follows:

[0192] A1. Input: Initialize auxiliary variable u b，k (0) v b (0) User power P 2k (0) Time allocation scheme τ bP (0) τ kI (0)

[0193] A2. Repeat steps A3-A5:

[0194] A3. Repeat the following steps:

[0195] Update the optimal 2D hovering position of the drone, the optimal user transmit power, and the optimal time allocation scheme until the increase in the objective function is less than a certain threshold ∈ 1;

[0196] A4. Based on the optimal two-dimensional hovering position of the UAV, the user's transmission power, and the time allocation scheme obtained above, the optimal intelligent reflective surface reflection coefficient is obtained.

[0197] A5. Repeat the operation until the increase in the objective function value is less than a certain threshold ∈2;

[0198] A6. Output: Optimal 2D hovering position of the drone. b，u User's optimal transmit power P 2k Optimal time allocation scheme τ bP , τ kIThe optimal intelligent reflective surface reflectivity Θ1, Θ2.

[0199] like Figure 3 As shown, the simulation results of the present invention are obtained. It can be seen from the simulation results that the minimum throughput obtained by all schemes increases with the increase of the UAV's transmission power. At the same time, the comparison of different schemes shows that the minimum throughput obtained by the scheme that combines UAV and intelligent reflector is significantly greater than the other two schemes.

[0200] Example 2

[0201] This embodiment provides a wireless power supply communication network throughput maximization optimization system to implement the wireless power supply communication network throughput maximization optimization method of the above embodiment 1, including: a wireless power supply communication network system construction module, a mathematical model construction module for maximizing minimum ground user throughput, a UAV two-dimensional position optimization problem construction module, an optimization problem transformation module, a smart reflective surface reflection coefficient optimization problem construction module, and an iterative optimization module;

[0202] In this embodiment, the wireless power supply and communication network system construction module is used to construct a UAV-based wireless power supply and communication network system with the assistance of an intelligent reflector.

[0203] In this embodiment, the mathematical model construction module for maximizing minimum ground user throughput is used to construct a mathematical model that maximizes minimum ground user throughput while satisfying energy constraints, including: UAV two-dimensional position optimization problem, power optimization problem, time optimization problem, and intelligent reflector reflection coefficient optimization problem;

[0204] In this embodiment, the UAV two-dimensional position optimization problem construction module is used to construct the UAV two-dimensional position optimization problem, scale the composite channel gain and introduce auxiliary variables, and realize the optimal two-dimensional layout of the UAV based on the SCA optimization algorithm;

[0205] In this embodiment, the optimization problem transformation module is used to transform the power optimization problem and the time optimization problem into a convex optimization problem based on the optimized two-dimensional position of the UAV;

[0206] In this embodiment, the intelligent reflector reflection coefficient optimization problem construction module is used to construct an optimization algorithm for the intelligent reflector reflection coefficient optimization problem based on the optimized UAV two-dimensional position, user transmit power, and allocation time, using the SDR algorithm.

[0207] In this embodiment, the iterative optimization module is used to obtain the optimal two-dimensional hovering position of the UAV, the user's transmit power, and the allocation time through an iterative optimization method.

[0208] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for maximizing throughput optimization in a wireless power supply communication network, characterized in that, Includes the following steps: A wireless power supply and communication network system based on UAVs is constructed with the assistance of intelligent reflectors. While satisfying energy constraints, a mathematical model is built to maximize the minimum throughput of ground users, including: UAV two-dimensional position optimization problem, power optimization problem, time optimization problem, and intelligent reflector reflection coefficient optimization problem. A two-dimensional position optimization problem for UAVs is constructed. By scaling the composite channel gain and introducing auxiliary variables, the optimal two-dimensional layout of the UAVs is achieved based on the SCA optimization algorithm. The two-dimensional position optimization problem for UAVs is denoted as: ； ； ； in, To transform the original problem from a minimization optimization problem into a maximization problem, auxiliary variables are needed. This indicates the total flight time of the drone. This indicates the total number of areas where the drones fly. Indicates the number of ground users. This represents the reflection coefficient matrix of the smart reflector during downlink power transfer. This represents the reflection coefficient matrix of the intelligent reflector during uplink information transmission. Indicates the number of intelligent reflective surface units. , All indicate the first Phase shift of each reflecting unit, Indicates that the drone is in The two-dimensional coordinates of each region For the channel from the smart reflector to the ground user, For the channel from the drone to the smart reflector, For the channel from drones to ground users, This indicates the constant power of the drone. This indicates the transmission power of the information transmitted by the user to the drone. It refers to the energy conversion efficiency at the ground user's location; The hybrid channel from drone to user is represented as: ； ； in, for The modulus, for The modulus, The distance from the drone to the user, The distance from the drone to the smart reflective surface. This represents the channel power gain at the reference location. Indicates the path fading factor. For carrier wavelength, for and The phase difference; By taking a lower bound on the hybrid channel gain to tighten the constraints, the inequality constraints are constructed as follows: ； Introducing scaling variables and The set conditions are met as follows: ; The inequality constraint is transformed into: ； in, Indicates noise power; make , , After performing a Taylor expansion on the left side of the inequality, we obtain the following inequality: ； Converting the energy conversion efficiency at ground users into: ； in, Indicates the number of iterations; Scaling variables and satisfy: ； ； Therefore, the original UAV two-dimensional position optimization problem can be transformed into ； ； ； ； ； The standard optimization tools are used to solve the two-dimensional position optimization problem of the UAV, and the optimized two-dimensional position of the UAV is obtained. Based on the optimized two-dimensional position of the UAV, the power optimization problem and the time optimization problem are transformed into convex optimization problems, specifically expressed as: Transmit power optimization problem: ； ； ； Time resource allocation optimization problem: ； ； ； ； ； ； in, To transform the original problem from a minimization optimization problem into a maximization problem, auxiliary variables are needed. This indicates the total flight time of the drone. This indicates the total number of areas where the drones fly. Indicates the number of ground users. This represents the reflection coefficient matrix of the smart reflector during downlink power transfer. This represents the reflection coefficient matrix of the intelligent reflector during uplink information transmission. Indicates the number of intelligent reflective surface units. , All indicate the first Phase shift of each reflecting unit, For the channel from the smart reflector to the ground user, For the channel from the drone to the smart reflector, For the channel from drones to ground users, This indicates the constant power of the drone. This indicates the transmission power of the information transmitted by the user to the drone. It refers to the energy conversion efficiency at the ground user's location. For the time it takes to transfer energy, For the time it takes to transmit information; Based on the optimized UAV two-dimensional position, user transmit power, and allocation time, an optimization algorithm for the intelligent reflective surface reflection coefficient optimization problem is constructed using the SDR algorithm. The optimal two-dimensional hovering position of the UAV, user transmit power, and allocation time are obtained through iterative optimization methods.

2. The method for maximizing throughput optimization in a wireless power supply communication network according to claim 1, characterized in that, The construction of a UAV-based wireless power supply and communication network system with the assistance of an intelligent reflective surface specifically includes: The wireless power supply communication network system is deployed with one drone and multiple ground users. The drone and all ground users' equipment are equipped with a single antenna. The drone charges the ground users on the downlink and receives information sent by the users on the uplink. The intelligent reflector has multiple reflective elements.

3. The method for maximizing throughput optimization in a wireless power supply communication network according to claim 1, characterized in that, A mathematical model is constructed to maximize the minimum throughput of ground users, specifically expressed as follows: ； ； ； ； ； ； in, This indicates the total flight time of the drone. This indicates the total number of areas where the drones fly. Indicates the number of ground users. This represents the reflection coefficient matrix of the smart reflector during downlink power transfer. This represents the reflection coefficient matrix of the intelligent reflector during uplink information transmission. Indicates the number of intelligent reflective surface units. , All indicate the first Phase shift of each reflecting unit, Indicates that the drone is in The two-dimensional coordinates of each region For the channel from the smart reflector to the ground user, For the channel from the drone to the smart reflector, For the channel from drones to ground users, This indicates the constant power of the drone. This indicates the transmission power of the information transmitted by the user to the drone. It refers to the energy conversion efficiency at the ground user's location. For the time it takes to transfer energy, This refers to the time it takes for the information to be transmitted.

4. The method for maximizing throughput optimization in a wireless power supply communication network according to claim 3, characterized in that, The channel from the intelligent reflector to the ground user uses a Ricean fading channel, the channel from the UAV to the intelligent reflector uses a line-of-sight wireless transmission channel, and the channel from the UAV to the ground user uses a Ricean fading channel.

5. The method for maximizing throughput optimization in a wireless power supply communication network according to claim 1, characterized in that, The problem of optimizing the reflection coefficient of a smart reflective surface is expressed as: ； ； ； in, To transform the original problem from a minimization optimization problem into a maximization problem, auxiliary variables are needed. This indicates the total flight time of the drone. This indicates the total number of areas where the drones fly. Indicates the number of ground users. This represents the reflection coefficient matrix of the smart reflector during downlink power transfer. This represents the reflection coefficient matrix of the intelligent reflector during uplink information transmission. Indicates the number of intelligent reflective surface units. , All indicate the first Phase shift of each reflecting unit, For the channel from the smart reflector to the ground user, For the channel from the drone to the smart reflector, For the channel from drones to ground users, This indicates the constant power of the drone. This indicates the transmission power of the information transmitted by the user to the drone. It refers to the energy conversion efficiency at the ground user's location. For the time it takes to transfer energy, For the time it takes to transmit information; make Let the reflected beamforming vectors of the smart reflector in the downlink and uplink be represented by: ; in, , ; make , ,in The problem of optimizing the reflection coefficient of a smart reflective surface is transformed into: ； ； ； ； definition and ,satisfy as well as ; Scaling constraints using the SDR algorithm The optimization problem of the reflection coefficient of the intelligent reflective surface is ultimately expressed as: ； ； ； ； ； The problem of optimizing the reflection coefficient of a smart reflector is solved using standard optimization tools.

6. The method for maximizing throughput optimization in a wireless power supply communication network according to claim 1, characterized in that, The optimal UAV 2D hovering position, user transmit power, and allocation time are obtained through iterative optimization methods, specifically including: Initialize auxiliary variables, user power, and time allocation scheme; Update the drone's two-dimensional hovering position, user transmit power, and time allocation until the increase in the objective function is less than a first preset threshold; The optimal intelligent reflective surface reflection coefficient is obtained based on the optimized two-dimensional hovering position of the UAV, the user's transmission power, and the time allocation. Repeat the iteration until the increase in the objective function value is less than the second preset threshold; Output the optimal two-dimensional hovering position of the UAV, the optimal transmission power for the user, the optimal time allocation scheme, and the optimal reflection coefficient of the intelligent reflector.

7. A system for maximizing throughput optimization in a wireless power supply communication network, characterized in that, The method for maximizing throughput optimization of wireless power supply communication network according to any one of claims 1-6 includes: a wireless power supply communication network system construction module, a mathematical model construction module for maximizing minimum ground user throughput, a UAV two-dimensional position optimization problem construction module, an optimization problem transformation module, a smart reflective surface reflection coefficient optimization problem construction module, and an iterative optimization module. The wireless power supply and communication network system construction module is used to construct a UAV-based wireless power supply and communication network system with the assistance of an intelligent reflective surface. The mathematical model building module for maximizing minimum ground user throughput is used to construct a mathematical model that maximizes minimum ground user throughput while satisfying energy constraints. This includes: two-dimensional position optimization problem, power optimization problem, time optimization problem, and intelligent reflector coefficient optimization problem for UAVs. The UAV two-dimensional position optimization problem construction module is used to construct the UAV two-dimensional position optimization problem, scale the composite channel gain and introduce auxiliary variables, and realize the optimal two-dimensional layout of the UAV based on the SCA optimization algorithm; The optimization problem transformation module is used to transform the power optimization problem and the time optimization problem into a convex optimization problem based on the optimized two-dimensional position of the UAV. The intelligent reflector reflection coefficient optimization problem construction module is used to construct an optimization algorithm for the intelligent reflector reflection coefficient optimization problem based on the optimized UAV two-dimensional position, user transmit power and allocation time, using the SDR algorithm. The iterative optimization module is used to obtain the optimal two-dimensional hovering position of the UAV, the user's transmit power, and the allocation time through iterative optimization methods.

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