Energy efficiency optimization method for hybrid RIS-assisted UAV mobile edge computing system

Abstract

This invention discloses an energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system, comprising: establishing a system model including a ground terminal, a fixed RIS, and a UAV-mounted RIS; configuring system parameters; defining offloading decisions; determining the total offloading workload of the ground terminal and the total system energy consumption, and defining system energy efficiency; constructing an optimization problem aimed at maximizing system energy efficiency, jointly optimizing the ground terminal offloading decisions, etc., and simplifying the optimization problem using the Tinkerbach method; comparing the offloading rates of the ground terminal offloading tasks via the fixed RIS and the UAV-mounted RIS, and solving the offloading decisions; using a semidefinite relaxation method and introducing a penalty function to solve the beamforming matrix of the fixed RIS and the UAV-mounted RIS, solving the time slot allocation, and using continuous relaxation to solve the UAV trajectory; and iterating the offloading decisions continuously using the block coordinate descent method until the optimal solution and the maximum system energy efficiency are obtained.

Description

Technical Field

[0001] This invention relates to the field of ground terminal task offloading technology, and in particular to an energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system. Background Technology

[0002] With the rapid development of mobile communication networks and Internet of Things (IoT) technologies, smart terminal devices will be widely used in people's daily lives. In the future, hundreds of billions of devices will be connected to the network. The increase in computationally intensive applications and the growth in user demand have placed higher demands on the computing and communication resources of IoT devices. To improve the service quality of mobile devices, Mobile Edge Computing (MEC) has been proposed as a means to solve problems such as limited device resources and scarce application resources due to its characteristics of proximity to users and distributed deployment. It can effectively improve communication and computing efficiency and overcome the problems of high time costs and huge resource consumption.

[0003] Due to the high energy consumption requirements of existing technologies, communication costs are also increasing. The low power consumption and high energy efficiency of Reconfigurable Intelligent Surfaces (RIS), along with their ability to reconfigure wireless communication transmission environments, have been extensively studied in the industry. In urban environments or areas with complex terrain where the line-of-sight (LoS) link between base stations and devices is blocked, constructing a reflective link using RIS can effectively improve the system's energy efficiency. However, RIS are mostly installed on building surfaces, resulting in limited coverage and potentially failing to respond well to emergencies. Unmanned Aerial Vehicles (UAVs), with their high mobility and low deployment costs, have attracted widespread attention. Utilizing UAVs equipped with RIS can effectively solve the problem of limited coverage of fixed RIS. However, UAVs are also limited by their own flight and hovering energy consumption. Combining fixed RIS with UAV-equipped RIS can address the limitations of both UAVs and RIS, further improving system performance. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides an energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system, which can solve the problem that the poor channel conditions of a fixed RIS-assisted mobile edge computing system are insufficient to support the ground terminal in completing its offloading task.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: an energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system, comprising:

[0008] A system model is established, including k ground terminals, a fixed RIS (Radio Router System), and a UAV-mounted RIS, and system parameters are configured. Offloading decisions are defined. The total offloading workload of the ground terminals and the total system energy consumption are determined, and system energy efficiency is defined. An optimization problem is constructed with the goal of maximizing system energy efficiency, jointly optimizing the ground terminal offloading decisions, time slot allocation, beamforming matrices of the fixed RIS and UAV-mounted RIS, and UAV trajectory. The Tinkelbach method is used to transform the optimization problem from fractional optimization to parameter optimization, while relaxation variables are introduced to simplify the optimization problem. The offloading rates of ground terminals offloading tasks via the fixed RIS and UAV-mounted RIS are compared, and the offloading decisions are solved. A semidefinite relaxation method is used, with a penalty function introduced, to solve the beamforming matrices of the fixed RIS and UAV-mounted RIS, solve the time slot allocation, and use continuous relaxation to solve the UAV trajectory. The offloading decisions, beamforming matrices of the fixed RIS and UAV-mounted RIS, time slot allocation, and UAV trajectory are iterated continuously using the block coordinate descent method until the optimal solution and the maximum system energy efficiency are obtained.

[0009] As a preferred embodiment of the energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system described in this invention, the configuration system parameters specifically include: a set of ground terminals. The horizontal coordinate z of the ground terminal k k =[x k ,y k ] T User k's transmit power P k Horizontal coordinate z of the hybrid access point A =[x a ,y a ] T The horizontal coordinate z of the fixed RIS R =[x r ,y r ] T The height of the fixed RIS r The instantaneous horizontal coordinates of the UAV are q[n] = [x[n], y[n]]. T UAV height Hu UAV start position q0, UAV end position q F UAV speed limit v max Fixed RIS offloading channel bandwidth B FRIS UAV equipped with RIS offload channel bandwidth B URIS Fixed RIS nth frame beamforming matrix UAV equipped with RIS nth frame beamforming matrix UAV flight total duration T, time frames Time frame set Ground terminal k unloading task lower limit L in frame n k,min [n], Channel gain between ground terminal k and hybrid access point Where β1 is the channel power gain of this channel when the reference distance is 1 meter, d GA,k Let k be the distance between the ground terminal k and the hybrid access point. The channel gain between the ground terminal k and the fixed RIS follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1. Where β2 is the channel power gain of this channel when the reference distance is 1 meter, d GR,k R is the distance between the ground terminal k and the fixed RIS. GR This is the Rice coefficient of the channel. The line-of-sight link gain between the ground terminal k and the fixed RIS is represented by λ, where λ is the carrier wavelength, M is the number of reflective elements on the RIS, and φ is the number of reflective elements on the RIS. GR,k Let be the cosine of the angle of arrival between ground terminal k and RIS, i.e. It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1; the channel gain between the fixed RIS and the hybrid access point is... Where β3 is the channel power gain of this channel when the reference distance is 1 meter, d RA R is the distance between the fixed RIS and the hybrid access point. RA This is the Rice coefficient of the channel. φ represents the line-of-sight link gain between the fixed RIS and the hybrid access point. RA The cosine of the angle of arrival between the fixed RIS and the hybrid access point, i.e. It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1; the channel gain between the ground terminal k and the RIS mounted on the UAV is... Where β4 is the channel power gain of this channel when the reference distance is 1 meter, d GU,k [n] represents the distance between ground terminal k and the RIS mounted on the UAV within time slot n.GU,k [n] is the cosine of the angle of arrival between the ground terminal and the RIS-equipped UAV within time slot n, i.e. UAV carries channel gain between RIS and hybrid access point Where β5 is the channel power gain of this channel when the reference distance is 1 meter, d UA [n] represents the distance between the UAV equipped with the RIS and the hybrid access point within time slot n, φ UA [n] is the cosine of the angle of arrival between the UAV carrying the RIS and the hybrid access point within time slot n, i.e.

[0010] As a preferred embodiment of the energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system described in this invention, the definition of the offloading decision includes defining the offloading decision of the ground terminal k in the nth frame as α. k [n], α k [n]∈{0,1}, if α k [n] = 1, indicating that ground terminal k performs task offloading via fixed RIS in the nth frame, if α k [n] = 0, indicating that the ground terminal k performs mission offloading in the nth frame via the fixed UAV equipped with RIS.

[0011] As a preferred embodiment of the energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system described in this invention, the determination of the total offloading task of the ground terminal and the total system energy consumption includes,

[0012] The unloading rate of the task by the fixed RIS in the nth frame of the kth ground terminal Represented as:

[0013]

[0014] The amount of tasks that ground terminal k offloads via fixed RIS in frame n Represented as:

[0015]

[0016] The offloading rate of the RIS offloading mission carried by the UAV on the nth frame of the k-th ground terminal Represented as:

[0017]

[0018] The workload of ground terminal k in frame n, carried by RIS and offloaded via UAV Represented as:

[0019]

[0020] Transmission power consumption of ground terminal k in the nth frame Represented as:

[0021]

[0022] UAV's flight energy consumption E in frame n UAV [n] is represented as:

[0023]

[0024] Where, P0, P1, U tip d0, ρ, s, G, and v0 are all fixed mechanical parameters. P0 represents the constant airfoil power of the UAV in hovering state, and P1 represents the inductive power of the UAV in hovering state. tip d represents the linear velocity at the blade tip. o ρ represents the air resistance ratio, s represents the rotor solidity, G represents the rotor disk area, and v0 represents the average rotor induced velocity in the hovering state.

[0025] As a preferred embodiment of the energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system described in this invention, the definition of system energy efficiency includes:

[0026] The system energy efficiency is defined as the ratio of the total offloading workload of the ground terminal to the total system energy consumption, expressed as:

[0027]

[0028] Where γ is the energy weighting coefficient.

[0029] As a preferred embodiment of the energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system described in this invention, the optimization problem aimed at maximizing system energy efficiency includes:

[0030] Construct an optimization problem P1 that aims to maximize system energy efficiency, jointly optimizing ground terminal unloading decisions, time slot allocation, fixed RIS, UAV-mounted RIS beamforming matrix, and UAV trajectory.

[0031]

[0032]

[0033]

[0034] C3:L k [n]≥L k,min [n]

[0035] C4:0≤θ F,k,m [N],θ U,k,m [n]≤2π

[0036] C5:||q[n+1]-q[n]||≤v max δ t

[0037] C6: q[0]=q0, q[N]=q f

[0038] Where C1 represents the unloading decision α k [n] is a binary variable. C2 ensures that the total unloading time of the ground terminal does not exceed the flight time of the UAV. C3 sets the minimum unloading task of the ground terminal k in each time period. C4 is the beamforming matrix phase constraint. C5 is the position change of the UAV during the entire flight process, which must be less than the position vector at the maximum speed. C6 is the starting position of the UAV trajectory.

[0039] As a preferred embodiment of the energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system described in this invention, the method of simplifying the optimization problem using the Tinkelbach method includes:

[0040] Let η denote the maximum energy efficiency of the system in the nth frame. * [n], the maximum energy efficiency of the system can be obtained if and only if the following formula holds:

[0041]

[0042] Using the Tinkelbach method, the fractional optimization is transformed into parameter optimization. By introducing an auxiliary variable u[n], problem P1 is transformed into the following form:

[0043]

[0044] stC1-C6

[0045] To simplify the problem, auxiliary variables ζ and u are introduced. F,k [n]、u U,k [n], Problem P2 is transformed into the following form:

[0046]

[0047] stC1-C2,C4-C6

[0048]

[0049]

[0050]

[0051]

[0052] Where C7 indicates that the introduced auxiliary variable ζ is the objective. The lower bound, C8 indicates the introduction of an auxiliary variable u. F,k After [n], B FRIS u F,k [n] is The lower bound, C9 indicates the introduction of an auxiliary variable u. U,k After [n], B URIS u U,k [n] is The lower bound is determined, and constraint C3 is rewritten as C10.

[0053] As a preferred embodiment of the energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system described in this invention, the solution for the unloading decision includes:

[0054] Given time slot allocation t k [n], Fixed RIS beamforming matrix Θ F,k [n], UAV equipped with RIS beamforming matrix Θ U,k After calculating [n] and UAV trajectory q[n], the unloading rate of the ground terminal k in the nth frame via the fixed RIS can be obtained. The offloading rate of the RIS offloading mission carried by the ground terminal k via the UAV in the nth frame.

[0055] like Then α k If [n] = 1, then Then α k [n] = 0;

[0056] According to the absolute value inequality |ab|≤|a|+|b|, equality holds when ab≤0. Since and Therefore, the following equation holds true:

[0057]

[0058] After deformation, we get:

[0059]

[0060] Therefore, we obtain α k The solution for [n] is:

[0061]

[0062] As a preferred embodiment of the energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system described in this invention, the step of using a semi-definite relaxation method and introducing a penalty function to solve the beamforming matrix and directly solve the time slot allocation includes,

[0063] Based on the obtained offloading decision, a given time slot allocation t k The subproblem of [n] and the UAV trajectory, represented by the fixed RIS beamforming matrix, is as follows:

[0064]

[0065] stC4,C7-C10

[0066] The subproblem of a UAV equipped with a RIS beamforming matrix is ​​represented as follows:

[0067]

[0068] stC4,C7-C10

[0069] By using the semidefinite relaxation method and introducing a penalty function to transform it into a convex optimization problem, the closed-form solution of the beamforming matrix of the fixed RIS and the UAV equipped with the RIS can be obtained.

[0070] Based on the obtained unloading decision, the given fixed RIS beamforming matrix Θ F,k [n], UAV equipped with RIS beamforming matrix Θ U,k The time slot allocation subproblem, involving [n] and the UAV trajectory q[n], can be expressed in the following form:

[0071]

[0072] stC2,C7-C10

[0073] For the time slot allocation subproblem, the objective function is a convex function, constraint C2 is a linear constraint, and constraints C7-C10 are all convex constraints. Therefore, the subproblem can be solved using the traditional convex optimization problem-solving method, just like CVX.

[0074] As a preferred embodiment of the energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system described in this invention, the step of solving the UAV trajectory using continuous relaxation includes:

[0075] Based on the obtained unloading decision, time slot allocation, fixed RIS beamforming matrix, and UAV-mounted RIS beamforming matrix, the UAV trajectory subproblem is expressed as:

[0076]

[0077] stC5-C6,C7-C10

[0078] Since the channel between the ground terminal and the hybrid access point is modeled as a Rayleigh channel, the channel gain h GA,k [n] follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1. Its random fluctuations are uncontrollable, so h is not considered. GA,k The influence of [n] will with h GU,k [n] Expression Substitution The result was:

[0079]

[0080] Introducing slack variables Rewrite constraint C9 as C11, which is expressed as:

[0081]

[0082] After performing a first-order Taylor expansion on the right side of the C11 inequality, it can be transformed into a convex constraint. Thus, the UAV trajectory subproblem is transformed into a convex optimization problem, which can be solved using CVX.

[0083] The beneficial effects of this invention are as follows: This invention proposes an energy efficiency optimization method for a joint mobile edge computing system assisted by a fixed RIS and a UAV-mounted RIS. This method combines a fixed RIS and a UAV-mounted RIS to jointly assist a mobile edge computing communication system. The ground terminal can choose to offload tasks via the fixed RIS or the UAV-mounted RIS. The UAV-mounted RIS solves the problem of limited coverage area of ​​the fixed RIS, and the fixed RIS shares the computational burden of the UAV-mounted RIS, avoiding excessive energy consumption during UAV flight, thus improving system performance. The method proposed in this invention maximizes system energy efficiency by jointly optimizing offloading decisions, beamforming matrices of the fixed RIS and UAV-mounted RIS, time slot allocation, and UAV trajectory, effectively increasing task offloading while controlling system energy consumption. Attached Figure Description

[0084] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0085] Figure 1 This is a schematic diagram of a model for an energy efficiency optimization method of a hybrid RIS-assisted UAV mobile edge computing system provided in an embodiment of the present invention.

[0086] Figure 2 This is a ground terminal task offloading protocol diagram for an energy efficiency optimization method of a hybrid RIS-assisted UAV mobile edge computing system provided in an embodiment of the present invention.

[0087] Figure 3 This is a basic flowchart illustrating the energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system provided in one embodiment of the present invention.

[0088] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0089] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0090] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0091] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0092] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0093] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0094] Example 1

[0095] Reference Figures 1-3 This is the first embodiment of the present invention, which provides an energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system, including:

[0096] S1: Establish a system model including k ground terminals, fixed RIS, and UAV-mounted RIS, and configure system parameters;

[0097] 11. Furthermore, the configuration system parameters specifically include: a set of ground terminals. The horizontal coordinate z of the ground terminal k k =[x k ,y k ] T User k's transmit power P k Horizontal coordinate z of the hybrid access point A =[x a ,y a ] T The horizontal coordinate z of the fixed RIS R =[x r ,y r ] T The height of the fixed RIS r The instantaneous horizontal coordinates of the UAV are q[n] = [x[n], y[n]]. T UAV height H u UAV start position q0, UAV end position q F UAV speed limit v max Fixed RIS offloading channel bandwidth B FRIS UAV equipped with RIS offload channel bandwidth B URIS Fixed RIS nth frame beamforming matrix UAV equipped with RIS nth frame beamforming matrix UAV flight total duration T, time frames Time frame set Ground terminal k unloading task lower limit L in frame n k,min[n], Channel gain between ground terminal k and hybrid access point Where β1 is the channel power gain of this channel when the reference distance is 1 meter, d GA,k Let k be the distance between the ground terminal k and the hybrid access point. The channel gain between the ground terminal k and the fixed RIS follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1. Where β2 is the channel power gain of this channel when the reference distance is 1 meter, d GR,k R is the distance between the ground terminal k and the fixed RIS. GR This is the Rice coefficient of the channel. The line-of-sight link gain between the ground terminal k and the fixed RIS is represented by λ, where λ is the carrier wavelength, M is the number of reflective elements on the RIS, and φ is the number of reflective elements on the RIS. GR,k Let be the cosine of the angle of arrival between ground terminal k and RIS, i.e. It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1; the channel gain between the fixed RIS and the hybrid access point is... Where β3 is the channel power gain of this channel when the reference distance is 1 meter, d RA R is the distance between the fixed RIS and the hybrid access point. RA This is the Rice coefficient of the channel. φ represents the line-of-sight link gain between the fixed RIS and the hybrid access point. RA The cosine of the angle of arrival between the fixed RIS and the hybrid access point, i.e. It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1; the channel gain between the ground terminal k and the RIS mounted on the UAV is... Where β4 is the channel power gain of this channel when the reference distance is 1 meter, d GU,k [n] represents the distance between ground terminal k and the RIS mounted on the UAV within time slot n. GU,k [n] is the cosine of the angle of arrival between the ground terminal and the RIS-equipped UAV within time slot n, i.e. UAV carries channel gain between RIS and hybrid access point Where β5 is the channel power gain of this channel when the reference distance is 1 meter, d UA [n] represents the distance between the UAV equipped with the RIS and the hybrid access point within time slot n, φ UA [n] is the cosine of the angle of arrival between the UAV carrying the RIS and the hybrid access point within time slot n, i.e.

[0098] S2: Define the unloading decision; determine the total unloading task of the ground terminal and the total energy consumption of the system, and define the system energy efficiency; construct an optimization problem with the goal of maximizing the system energy efficiency, which jointly optimizes the ground terminal unloading decision, time slot allocation, fixed RIS, UAV-mounted RIS beamforming matrix and UAV trajectory, and uses the Tinkerbach method to transform the optimization problem from fractional optimization to parameter optimization, while introducing slack variables to simplify the optimization problem;

[0099] Furthermore, the definition of the offloading decision specifically includes the following steps: defining the offloading decision of ground terminal k in the nth frame as α. k [n], α k [n]∈{0,1}, if α k [n] = 1, indicating that ground terminal k performs task offloading via fixed RIS in the nth frame, if α k [n] = 0, indicating that the ground terminal k performs mission offloading in the nth frame via the fixed UAV equipped with RIS.

[0100] It should be noted that determining the total unloading task volume and total unloading energy consumption of the ground terminal includes,

[0101] The unloading rate of the task by the fixed RIS in the nth frame of the kth ground terminal Represented as:

[0102]

[0103] The amount of tasks that ground terminal k offloads via fixed RIS in frame n Represented as:

[0104]

[0105] The offloading rate of the RIS offloading mission carried by the UAV on the nth frame of the k-th ground terminal Represented as:

[0106]

[0107] The workload of ground terminal k in frame n, carried by RIS and offloaded via UAV Represented as:

[0108]

[0109] Transmission power consumption of ground terminal k in the nth frame Represented as:

[0110]

[0111] UAV's flight energy consumption E in frame n UAV [n] is represented as:

[0112]

[0113] Where, P0, P1, U tip d0, ρ, s, G, and v0 are all fixed mechanical parameters. P0 represents the constant airfoil power of the UAV in hovering state, and P1 represents the inductive power of the UAV in hovering state. tip d represents the linear velocity at the blade tip. o ρ represents the air resistance ratio, s represents the rotor solidity, G represents the rotor disk area, and v0 represents the average rotor induced velocity in the hovering state.

[0114] Furthermore, the defined system energy efficiency includes,

[0115] The system energy efficiency is defined as the ratio of the total offloading workload of the ground terminal to the total system energy consumption, expressed as:

[0116]

[0117] Where γ is the energy weighting coefficient.

[0118] It should be noted that the optimization problem described above, aimed at maximizing system energy efficiency, includes:

[0119] Construct an optimization problem P1 that aims to maximize system energy efficiency, jointly optimizing ground terminal unloading decisions, time slot allocation, fixed RIS, UAV-mounted RIS beamforming matrix, and UAV trajectory.

[0120]

[0121]

[0122]

[0123] C3:L k [n]≥L k,min [n]

[0124] C4:0≤θ F,k,m [n],θ U,k,m [n]≤2π

[0125] C5:||q[n+1]-q[n]||≤v max δ t

[0126] C6: q[0]=q0, q[N]=q f

[0127] Where C1 represents the unloading decision α k[n] is a binary variable. C2 ensures that the total unloading time of the ground terminal does not exceed the flight time of the UAV. C3 sets the minimum unloading task of the ground terminal k in each time period. C4 is the beamforming matrix phase constraint. C5 is the position change of the UAV during the entire flight process, which must be less than the position vector at the maximum speed. C6 is the starting position of the UAV trajectory.

[0128] It should be noted that the simplified optimization problem using Tinkelbach includes,

[0129] Let η denote the maximum energy efficiency of the system in the nth frame. * [n], the maximum energy efficiency of the system can be obtained if and only if the following formula holds:

[0130]

[0131] Using the Tinkelbach method, the fractional optimization is transformed into parameter optimization. By introducing an auxiliary variable u[n], problem P1 is transformed into the following form:

[0132]

[0133] stC1-C6

[0134] To simplify the problem, auxiliary variables ζ and u are introduced. F,k [n]、u U,k [n], Problem P2 is transformed into the following form:

[0135]

[0136] stC1-C2,C4-C6

[0137]

[0138]

[0139]

[0140]

[0141] Where C7 indicates that the introduced auxiliary variable ζ is the objective. The lower bound, C8 indicates the introduction of an auxiliary variable u. F,k After [n], B FRIS u F,k [n] is The lower bound, C9 indicates the introduction of an auxiliary variable u. U,k After [n], B URIS u U,k [n] is The lower bound is determined, and constraint C3 is rewritten as C10.

[0142] S3: Compare the offloading rates of the ground terminal using a fixed RIS and the UAV using a RIS, and solve for the offloading decision.

[0143] Furthermore, the solution for the unloading decision includes,

[0144] Given time slot allocation t k [n], Fixed RIS beamforming matrix Θ F,k [n], UAV equipped with RIS beamforming matrix Θ U,k After calculating [n] and UAV trajectory q[n], the unloading rate of the ground terminal k in the nth frame via the fixed RIS can be obtained. The offloading rate of the RIS offloading mission carried by the ground terminal k via the UAV in the nth frame.

[0145] like Then α k If n=1, if Then α k [n] = 0;

[0146] According to the absolute value inequality |ab|≤|a|+|b|, equality holds when ab≤0. Since and Therefore, the following equation holds true:

[0147]

[0148] After deformation, we get:

[0149]

[0150] Therefore, we obtain α k The solution for [n] is:

[0151]

[0152] S4: The beamforming matrix of the fixed RIS and the UAV equipped with the RIS is solved by using the semi-positive definite relaxation method and introducing a penalty function, the time slot allocation is solved, and the UAV trajectory is solved by continuous relaxation.

[0153] Furthermore, the method of using a semi-definite relaxation method and introducing a penalty function to solve the beamforming matrix and directly solve the time slot allocation includes,

[0154] Based on the obtained offloading decision, a given time slot allocation t k The subproblem of [n] and the UAV trajectory, represented by the fixed RIS beamforming matrix, is as follows:

[0155]

[0156] stC4,C7-C10

[0157] The subproblem of a UAV equipped with a RIS beamforming matrix is ​​represented as follows:

[0158]

[0159] stC4,C7-C10

[0160] By using the semidefinite relaxation method and introducing a penalty function to transform it into a convex optimization problem, the closed-form solution of the beamforming matrix of the fixed RIS and the UAV equipped with the RIS can be obtained.

[0161] Based on the obtained unloading decision, the given fixed RIS beamforming matrix Θ F,k [n], UAV equipped with RIS beamforming matrix Θ U,k The time slot allocation subproblem, involving [n] and the UAV trajectory q[n], can be expressed in the following form:

[0162]

[0163] stC2,C7-C10

[0164] For the time slot allocation subproblem, the objective function is a convex function, constraint C2 is a linear constraint, and constraints C7-C10 are all convex constraints. Therefore, the subproblem can be solved using the traditional convex optimization problem-solving method, just like CVX.

[0165] Furthermore, the method of solving the UAV trajectory using continuous relaxation includes,

[0166] Based on the obtained unloading decision, time slot allocation, fixed RIS beamforming matrix, and UAV-mounted RIS beamforming matrix, the UAV trajectory subproblem is expressed as:

[0167]

[0168] stC5-C6,C7-C10

[0169] Since the channel between the ground terminal and the hybrid access point is modeled as a Rayleigh channel, the channel gain h GA,k [n] follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1. Its random fluctuations are uncontrollable, so h is not considered. GA,k The influence of [n] will with h GU,k [n] Expression Substitution The result was:

[0170]

[0171] Introducing slack variables Rewrite constraint C9 as C11, which is expressed as:

[0172]

[0173] After performing a first-order Taylor expansion on the right side of the C11 inequality, it can be transformed into a convex constraint. Thus, the UAV trajectory subproblem is transformed into a convex optimization problem, which can be solved using CVX.

[0174] S5: Iteratively unloading decisions, fixed RIS and UAV-mounted RIS beamforming matrix, time slot allocation, and UAV trajectory are obtained through block coordinate descent method until the optimal solution and the maximum system energy efficiency are obtained.

[0175] Example 2

[0176] Reference Figures 1-3 As an embodiment of the present invention, an energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system is provided. To verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0177] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0178] like Figure 1 As shown, this invention proposes an energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system. For practical considerations, although the channel between the ground terminal and the base station is easily blocked, there is still a significant amount of scattering; therefore, the channel between the ground terminal and the base station is modeled as a Rayleigh channel. Since the fixed RIS is installed on a building at a certain height, there is an additional line-of-sight channel between it and the base station and the ground terminal; therefore, the channels between the fixed RIS and the base station, and between the fixed RIS and the ground terminal, are modeled as Ricean channels. The UAV has a certain height and strong mobility; therefore, the channel between the UAV-equipped RIS and the base station and the ground terminal is modeled as a line-of-sight channel. The ground terminal has two options for offloading computing tasks to the base station: either through a cascaded channel consisting of the fixed RIS and a direct link to the base station, or through a cascaded channel consisting of the UAV-equipped RIS and a direct link to the base station. The offloading rate of these two channels directly affects the ground terminal's offloading decision, and the phase of the RIS reflective element and the trajectory of the UAV affect the channel gain. As can be seen from the figure, GT1 chooses to offload tasks via a UAV equipped with a RIS, while GT2 chooses to offload tasks via a fixed RIS. In addition, both GT1 and GT2 have offload channels that can be directly offloaded to the base station.

[0179] Figure 2This is a ground terminal task offloading protocol diagram. The total flight time T of the UAV is divided into N frames, and each frame is further divided into K time slots. User k is in time slot t. k [n] Perform task unloading.

[0180] Figure 3 This is a flowchart of an energy efficiency optimization method for a fixed RIS and UAV-equipped RIS-assisted mobile edge computing system provided by the present invention, including the following steps:

[0181] 12.S1: Establish a system model including K ground terminals, a fixed RIS, and a UAV-mounted RIS, and configure system parameters, including: the set of ground terminals. The horizontal coordinate z of the ground terminal k k =[x k ,y k ] T User k's transmit power P k Horizontal coordinate z of the hybrid access point A =[x a ,y a ] T The horizontal coordinate z of the fixed RIS R =[x r ,y r ] T The height of the fixed RIS r The instantaneous horizontal coordinates of the UAV are q[n] = [x[n], y[n]]. T UAV height H u UAV start position q0, UAV end position q F UAV speed limit v max Fixed RIS offloading channel bandwidth B FRIS UAV equipped with RIS offload channel bandwidth B URIS Fixed RIS nth frame beamforming matrix UAV equipped with RIS nth frame beamforming matrix UAV flight total duration T, time frames Time frame set Ground terminal k unloading task lower limit L in frame n k,min [n], Channel gain between ground terminal k and hybrid access point Where β1 is the channel power gain of this channel when the reference distance is 1 meter, d GA,k Let k be the distance between the ground terminal k and the hybrid access point. The channel gain between the ground terminal k and the fixed RIS follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1. Where β2 is the channel power gain of this channel when the reference distance is 1 meter, d GR,k R is the distance between the ground terminal k and the fixed RIS. GR This is the Rice coefficient of the channel. The line-of-sight link gain between the ground terminal k and the fixed RIS is represented by λ, where λ is the carrier wavelength, M is the number of reflective elements on the RIS, and φ is the number of reflective elements on the RIS. GR,k Let be the cosine of the angle of arrival between ground terminal k and RIS, i.e. It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1; the channel gain between the fixed RIS and the hybrid access point is... Where β3 is the channel power gain of this channel when the reference distance is 1 meter, d RA R is the distance between the fixed RIS and the hybrid access point. RA This is the Rice coefficient of the channel. φ represents the line-of-sight link gain between the fixed RIS and the hybrid access point. RA The cosine of the angle of arrival between the fixed RIS and the hybrid access point, i.e. It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1; the channel gain between the ground terminal k and the RIS mounted on the UAV is... Where β4 is the channel power gain of this channel when the reference distance is 1 meter, dGU ,k [n] represents the distance between ground terminal k and the RIS mounted on the UAV within time slot n. GU,k [n] is the cosine of the angle of arrival between the ground terminal and the RIS-equipped UAV within time slot n, i.e. UAV carries channel gain between RIS and hybrid access point Where β5 is the channel power gain of this channel when the reference distance is 1 meter, d UA [n] represents the distance between the UAV equipped with the RIS and the hybrid access point within time slot n, φ UA [n] is the cosine of the angle of arrival between the UAV carrying the RIS and the hybrid access point within time slot n, i.e.

[0182] S2: Construct an optimization problem that maximizes the system's energy efficiency, which is simplified using the Tinkelbach method. The specific steps include:

[0183] S2.1: Define the offloading decision of ground terminal k in the nth frame as α k [n], α k [n]∈{0,1}, if α k[n] = 1, indicating that ground terminal k performs task offloading via fixed RIS in the nth frame, if α k [n] = 0, indicating that ground terminal k performs task offloading in the nth frame via a fixed UAV equipped with RIS;

[0184] S2.2: The offloading rate of the fixed RIS offloading task for the k-th ground terminal in the n-th frame. Represented as:

[0185]

[0186] The amount of tasks that ground terminal k offloads via fixed RIS in frame n Represented as:

[0187]

[0188] The offloading rate of the RIS offloading mission carried by the UAV on the nth frame of the k-th ground terminal Represented as:

[0189]

[0190] The workload of ground terminal k in frame n, carried by RIS and offloaded via UAV Represented as:

[0191]

[0192] Transmission power consumption of ground terminal k in the nth frame Represented as:

[0193]

[0194] UAV's flight energy consumption E in frame n UAV [n] is represented as:

[0195]

[0196] Where, P0, P1, U tip d0, ρ, s, G, and v0 are all fixed mechanical parameters. P0 represents the constant airfoil power of the UAV in hovering state, and P1 represents the inductive power of the UAV in hovering state. tip d represents the linear velocity at the blade tip. o ρ represents the air resistance ratio, s represents the rotor solidity, G represents the rotor disk area, and v0 represents the average rotor induced velocity in the hovering state.

[0197] S2.3: The system energy efficiency is defined as the ratio of the total offloading workload of the ground terminal to the total system energy consumption, expressed as:

[0198]

[0199] Where γ is the energy weighting coefficient.

[0200] S2.4: Construct an optimization problem P1 that aims to maximize system energy efficiency by jointly optimizing ground terminal unloading decisions, time slot allocation, fixed RIS, UAV-mounted RIS beamforming matrix, and UAV trajectory.

[0201]

[0202]

[0203]

[0204] C3:L k [n]≥L k,min [n]

[0205] C4:0≤θ F,k,m [n],θ U,k,m [n]≤2π

[0206] C5:||q[n+1]-q[n]||≤v max δ t

[0207] C6: q[0]=q0, q[N]=q f

[0208] Where C1 represents the unloading decision α k [n] is a binary variable. C2 ensures that the total unloading time of the ground terminal does not exceed the flight time of the UAV. C3 sets the minimum unloading task of the ground terminal k in each time period. C4 is the beamforming matrix phase constraint. C5 is the position change of the UAV during the entire flight process, which must be less than the position vector at the maximum speed. C6 is the starting position of the UAV trajectory.

[0209] S2.5: Let η denote the maximum energy efficiency of the system in the nth frame. * [n], the maximum energy efficiency of the system can be obtained if and only if the following formula holds:

[0210]

[0211] Using the Tinkerbach method, fractional optimization can be transformed into parametric optimization. By introducing an auxiliary variable u[n], problem P1 is transformed into the following form:

[0212]

[0213] stC1-C6

[0214] To simplify the problem, auxiliary variables ζ and u are introduced. F,k [n]、u U,k [n], Problem P2 is transformed into the following form:

[0215]

[0216] stC1-C2,C4-C6

[0217]

[0218]

[0219]

[0220]

[0221] Where C7 indicates that the introduced auxiliary variable ζ is the objective. The lower bound, C8 indicates the introduction of an auxiliary variable u. F,k After [n], B FRIS u F,k [n] is The lower bound, C9 indicates the introduction of an auxiliary variable u. U,k After [n], B URIS u U,k [n] is The lower bound is determined, and constraint C3 is rewritten as C10.

[0222] S3: Solve the unloading decision. The specific method is as follows: given the time slot allocation t k [n], Fixed RIS beamforming matrix Θ F,k [n], UAV equipped with RIS beamforming matrix Θ U,k After calculating [n] and UAV trajectory q[n], the unloading rate of the ground terminal k in the nth frame via the fixed RIS can be obtained. The offloading rate of the RIS offloading mission carried by the ground terminal k via the UAV in the nth frame.

[0223] like Then α k If [n] = 1, then Then α k [n] = 0.

[0224] According to the absolute value inequality |ab|≤|a|+|b|, equality holds when ab≤0. Since and Therefore, the following equation holds true:

[0225]

[0226] After deformation, we get:

[0227]

[0228] Therefore, we obtain α k The solution for [n] is:

[0229]

[0230] S4: Solving for the beamforming matrix of the fixed RIS and the UAV equipped with the RIS, solving for the time slot allocation, and solving for the UAV trajectory, specifically including:

[0231] S4.1: Based on the obtained offloading decision, the given time slot allocation t k The subproblem of [n] and the UAV trajectory, represented by the fixed RIS beamforming matrix, is as follows:

[0232]

[0233] stC4,C7-C10

[0234] Introducing auxiliary variables Since the channel between the ground terminal and the hybrid access point is modeled as a Rayleigh channel, the channel gain h GA,k [n] follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1. Its random fluctuations are uncontrollable, so h is not considered. GA,k The influence of [n], then Question P4.1 has been rewritten in the following form:

[0235]

[0236] stC4,C7,C9,C10

[0237]

[0238]

[0239] Among them, constraint C8 is rewritten as constraint C12.

[0240] Introducing auxiliary variables Question P4.2 has been rewritten in the following form:

[0241]

[0242] stC4,C7,C9,C10

[0243]

[0244]

[0245]

[0246]

[0247] Constraint C12 is rewritten as constraint C14, where C17 is a non-convex rank-one constraint. For a positive semi-definite matrix V... k [n], whose trace is not less than its largest eigenvalue, i.e., Tr(V k [n])≥λ max (V k [n]), when RankV k When [n] = 1, equality is achieved. To resolve the rank-one constraint, a penalty function is introduced into the objective function, expressed as follows:

[0248]

[0249] stC4,C7,C9,C10,C14-C17

[0250] Where β is the penalty function factor. Regarding λ in problem P4.4... max (V k After performing a first-order Taylor expansion on [n], the problem is transformed into the following form:

[0251]

[0252] stC4,C7,C9,C10,C14-C17

[0253] in, V represents k [n] represents the optimal solution in the m-th iteration, v k,max [n] represents λ max (V k The problem is a unit vector of [n]. After removing the constant term, the problem is rewritten in the following form:

[0254]

[0255] stC4,C7,C9,C10,C14-C17

[0256] When Tr(V) k [n])-λ max (V k When [n])≈0, we have:

[0257]

[0258] The subproblem of a UAV equipped with a RIS beamforming matrix is ​​represented as follows:

[0259]

[0260] stC4,C7-C10

[0261] Similar to solving the fixed RIS beamforming matrix, the closed-form solution of the beamforming matrix of the UAV equipped with RIS is obtained by transforming it into a convex optimization problem through the semidefinite relaxation method and the introduction of a penalty function.

[0262] S4.2: Based on the obtained unloading decision, the given fixed RIS beamforming matrix Θ F,k [n], UAV equipped with RIS beamforming matrix Θ U,k The time slot allocation subproblem, involving [n] and the UAV trajectory q[n], can be expressed in the following form:

[0263]

[0264] stC2,C7-C10

[0265] For the time slot allocation subproblem, the objective function is a convex function, constraint C2 is a linear constraint, and constraints C7-C10 are all convex constraints. Therefore, this subproblem can be solved using traditional convex optimization methods such as CVX.

[0266] S4.3: Based on the obtained unloading decision, time slot allocation, fixed RIS beamforming matrix, and UAV-mounted RIS beamforming matrix, the UAV trajectory subproblem is expressed as:

[0267]

[0268] stC5-C6,C7-C10

[0269] Since the channel between the ground terminal and the hybrid access point is modeled as a Rayleigh channel, the channel gain h GA,k [n] follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1. Its random fluctuations are uncontrollable, so h is not considered. GA,k The influence of [n] will with h GU,k [n] Expression Substitution The result was:

[0270]

[0271] Introducing slack variables Rewriting constraint C9 to C11 means:

[0272]

[0273] Performing a first-order Taylor expansion on the right-hand side of the inequality C11, it can be rewritten in the following form:

[0274]

[0275] in,

[0276]

[0277]

[0278]

[0279] Question P4.4 is transformed into the following form:

[0280]

[0281] stC5-C6,C7-C8,C10,C18

[0282] This subproblem is a convex optimization problem, which can be solved using CVX.

[0283] S5: Iterate through the block coordinate descent method to make unloading decisions, fixed RIS beamforming matrix, UAV equipped with RIS beamforming matrix, time slot allocation, and UAV trajectory until the optimal solution and the maximum system energy efficiency are obtained.

[0284] This invention is an energy efficiency optimization technique for a hybrid RIS-assisted UAV mobile edge computing system. The method first establishes a system model including k ground terminals, a fixed RIS, and a UAV-mounted RIS, configures system parameters, and defines offloading decisions. It then determines the total offloading workload of the ground terminals and the total system energy consumption, defining the system energy efficiency. An optimization problem is constructed with the goal of maximizing system energy efficiency, jointly optimizing the ground terminal offloading decisions, time slot allocation, beamforming matrices of the fixed RIS and UAV-mounted RIS, and the UAV trajectory. The Tinkelbach method is used to transform the optimization problem from fractional optimization to parameter optimization, while relaxation variables are introduced to simplify the problem. The offloading rates of ground terminals offloading tasks via the fixed RIS and UAV-mounted RIS are compared to solve for the offloading decisions. A semidefinite relaxation method with a penalty function is used to solve for the beamforming matrices of the fixed RIS and UAV-mounted RIS, and the time slot allocation is solved. Continuous relaxation is used to solve for the UAV trajectory. Finally, the block coordinate descent method is used to iteratively optimize the offloading decisions, beamforming matrices of the fixed RIS and UAV-mounted RIS, time slot allocation, and UAV trajectory until the optimal solution and the maximum system energy efficiency are obtained.

[0285] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0286] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0287] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0288] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0289] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0290] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0291] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0292] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system, characterized in that: include, Establish A system model including k ground terminals, a fixed RIS, and a UAV equipped with a RIS, and system parameters are configured. Define the uninstallation decision; The total unloading workload of the ground terminal and the total energy consumption of the system are determined, and the system energy efficiency is defined. An optimization problem is constructed with the goal of maximizing the system energy efficiency, which jointly optimizes the ground terminal unloading decision, time slot allocation, fixed RIS, UAV-mounted RIS beamforming matrix and UAV trajectory. The Tinkelbach method is used to transform the optimization problem from fractional optimization to parameter optimization, while slack variables are introduced to simplify the optimization problem. Compare the offloading rates of ground terminals performing mission offloading via fixed RIS and UAVs carrying RIS, and solve for the offloading decision. The beamforming matrix of the fixed RIS and the UAV equipped with the RIS are solved by using the semidefinite relaxation method and introducing a penalty function, the time slot allocation is solved, and the UAV trajectory is solved by continuous relaxation. The optimal solution and the maximum system energy efficiency are obtained by iteratively applying the block coordinate descent method to unload the decision, the fixed RIS and UAV equipped RIS beamforming matrix, time slot allocation, and UAV trajectory until the block coordinate descent method is used. The configuration system parameters specifically include: ground terminal set. Ground terminal horizontal coordinates ,user Transmission power Horizontal coordinates of hybrid access point Horizontal coordinates of fixed RIS The height of the fixed RIS UAV horizontal instantaneous coordinates UAV height UAV starting point UAV endpoint UAV speed limit Fixed RIS offload channel bandwidth UAV equipped with RIS offload channel bandwidth Fixed RIS Frame beamforming matrix UAV equipped with RIS Frame beamforming matrix Total UAV flight time time frame Time frame set Ground terminal No. Frame unloading task volume lower limit Ground terminal Channel gain between hybrid access points ,in This represents the channel power gain of the channel at a reference distance of 1 meter. Let k be the distance between the ground terminal k and the hybrid access point. It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1; ground terminal Channel gain with fixed RIS ,in This represents the channel power gain of the channel at a reference distance of 1 meter. Let k be the distance between the ground terminal k and the fixed RIS. This is the Rice coefficient of the channel. This represents the line-of-sight link gain between the ground terminal k and the fixed RIS. Where M is the carrier wavelength, and M is the number of reflective elements on the RIS. Let be the cosine of the angle of arrival between ground terminal k and RIS, i.e. , It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1; the channel gain between the fixed RIS and the hybrid access point is... ,in This represents the channel power gain of the channel at a reference distance of 1 meter. The distance between the fixed RIS and the hybrid access point. This is the Rice coefficient of the channel. This indicates the line-of-sight link gain between the fixed RIS and the hybrid access point. The cosine of the angle of arrival between the fixed RIS and the hybrid access point, i.e. , It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1; ground terminal The channel gain between the UAV and the RIS equipped with it is ,in This represents the channel power gain of the channel at a reference distance of 1 meter. Let n be the distance between ground terminal k and the RIS mounted on the UAV within time slot n. Let be the cosine of the angle of arrival between the ground terminal and the RIS-equipped UAV within time slot n, i.e. Channel gain between UAV equipped with RIS and hybrid access point ,in This represents the channel power gain of the channel at a reference distance of 1 meter. The distance between the UAV equipped with the RIS and the hybrid access point within time slot n. The cosine of the angle of arrival between the UAV equipped with the RIS and the hybrid access point within time slot n, i.e. ; The definition of the offloading decision specifically includes the following steps: defining the ground terminal. In the The frame unloading decision is , ,like Instructing ground terminals In the nth frame, task offloading is performed via a fixed RIS. Instructing ground terminals In the nth frame, the task is offloaded via a fixed UAV equipped with a RIS. The determination of the total unloading workload of the ground terminal and the total system energy consumption includes, No. The first ground terminal The offloading rate of the frame through the fixed RIS offloading task Represented as: Ground terminal In the The workload of frame offloading via fixed RIS Represented as: No. The first ground terminal The offloading rate of the frame via the RIS offloading task carried by the UAV Represented as: Ground terminal In the The workload of Frame offloading via UAV and RIS Represented as: Ground terminal In the Frame transmission power consumption Represented as: UAV in Frame flight energy consumption Represented as: in, , , , , , , , All are fixed mechanical parameters. This represents the constant airfoil power of the UAV in hovering mode. This indicates the sensing power of the UAV while it is hovering. This represents the linear velocity at the blade tip. Indicates the fuselage drag ratio. Indicates air density, Indicates rotor solidity, Indicates the rotor disk area, This represents the average rotor induced velocity in the hovering state; The system energy efficiency is defined as the ratio of the total offloading workload of the ground terminal to the total system energy consumption, expressed as: in, Energy weighting coefficient; Construct an optimization problem P1 that aims to maximize system energy efficiency, jointly optimizing ground terminal unloading decisions, time slot allocation, fixed RIS, UAV-mounted RIS beamforming matrix, and UAV trajectory. Where C1 represents the unloading decision. As a binary variable, C2 ensures that the total unloading time of the ground terminal does not exceed the flight time of the UAV, and C3 sets the ground terminal... The minimum amount of unloading tasks in each time period, C4 is the beamforming matrix phase constraint, C5 is the position vector of the UAV during the entire flight process where the position change is less than that at the maximum speed, and C6 is the starting position of the UAV trajectory.

2. The energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system as described in claim 1, characterized in that: The simplified optimization problem using the Tinkelbach method includes... The first The maximum energy efficiency of the frame system is denoted as The maximum energy efficiency of the system can be obtained if and only if the following equation holds: The Tinkelbach method is used to transform fractional optimization into parameter optimization, and auxiliary variables are introduced. Problem P1 is transformed into the following form: To simplify the problem, auxiliary variables are introduced. , , Problem P2 is transformed into the following form: Where C7 represents the introduced auxiliary variable. For the goal The lower bound of C8 indicates the introduction of an auxiliary variable. back, for The lower bound, C9 indicates the introduction of an auxiliary variable. back, for The lower bound is determined, and constraint C3 is rewritten as C10.

3. The energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system as described in claim 2, characterized in that: The solution for the unloading decision includes, Given time slot allocation Fixed RIS beamforming matrix UAV equipped with RIS beamforming matrix UAV trajectory The ground terminal can then be calculated. In the The offloading rate of the frame through the fixed RIS offloading task With ground terminal In the The offloading rate of the frame via the RIS offloading task carried by the UAV ; like ,but ,like ,but ; According to the absolute value inequality ,when When to take, because and Therefore, the following equation holds true: After deformation, we get: Therefore, we get The solution is: 。 4. The energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system as described in claim 3, characterized in that: The method of using a semidefinite relaxation method and introducing a penalty function to solve the beamforming matrix and directly solve the time slot allocation includes... Based on the obtained offloading decision, a given time slot allocation The subproblem of fixed RIS beamforming matrix, along with the UAV trajectory, is expressed as: The subproblem of a UAV equipped with a RIS beamforming matrix is ​​represented as follows: By using the semidefinite relaxation method and introducing a penalty function to transform it into a convex optimization problem, the closed-form solution of the beamforming matrix of the fixed RIS and the UAV equipped with the RIS can be obtained. Based on the obtained unloading decision, the given fixed RIS beamforming matrix UAV equipped with RIS beamforming matrix UAV trajectory The time slot allocation subproblem can be expressed in the following form: For the time slot allocation subproblem, the objective function is a convex function, constraint C2 is a linear constraint, and constraints C7-C10 are all convex constraints. Therefore, the subproblem can be solved using the traditional convex optimization problem-solving method, just like CVX.

5. The energy efficiency optimization method for the hybrid RIS-assisted UAV mobile edge computing system as described in claim 4, characterized in that: The method of solving the UAV trajectory using continuous relaxation includes... Based on the obtained unloading decision, time slot allocation, fixed RIS beamforming matrix, and UAV-mounted RIS beamforming matrix, the UAV trajectory subproblem is expressed as: Since the channel between the ground terminal and the hybrid access point is modeled as a Rayleigh channel, the channel gain... It follows a circularly symmetric complex Gaussian distribution with mean 0 and variance 1. Its random fluctuations are uncontrollable, so they are not considered. The impact will and Expression Substitution The result was: Introducing slack variables , Rewrite constraint C9 as C11, which is represented as: After performing a first-order Taylor expansion on the right side of the C11 inequality, it is transformed into a convex constraint. Thus, the UAV trajectory subproblem is transformed into a convex optimization problem, which can be solved using CVX.

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