A STAR-RIS-assisted joint optimization method for UAV-MEC networks

Abstract

The application discloses a STAR-RIS assisted UAV-MEC network joint optimization method, which applies STAR-RIS to the scene of a UAV assisted MEC wireless communication network, jointly optimizes three subproblems of task offloading decision, UAV hovering position and resource allocation, and minimizes the total energy consumption of the system. In the embodiment, a hovering unmanned aerial vehicle equipped with a MEC server and a STAR-RIS is used as an aerial BS to cooperatively provide computing and communication services for users with a ground BS, and the UAV position, the amplitude of the STAR-RIS, the phase shift and the task offloading ratio are jointly optimized. The system energy consumption in the energy splitting (ES) mode is minimized.

Description

Technical Field

[0001] This invention relates to the field of communications, and more specifically to a STAR-RIS-assisted joint optimization method for UAV-MEC networks. Background Technology

[0002] The advent of sixth-generation (6G) communication technology enables smart devices to provide ultra-high-speed and ultra-low-latency services to mobile terminals (MTs), such as extended reality and 8K ultra-high-definition video transmission. Cloud computing, as a centralized computing paradigm, can meet users' Quality of Service (QoS) requirements by offloading tasks to data centers. However, the long propagation distance from cloud computing to MTs is unsuitable for compute-intensive and latency-sensitive applications. Compared to cloud computing, multi-access edge computing (MEC) can quickly complete tasks offloaded from MTs by deploying computing and caching resources, such as base stations (BSs), at the network edge. However, the limited signal coverage of traditional BSs makes establishing reliable connections between mobile MTs and BSs challenging.

[0003] Unmanned Aerial Vehicle (UAV) assisted communication networks have become one of the most promising architectures in the field of task offloading in recent years due to their advantages such as flexible deployment, low cost-effectiveness, and higher line-of-sight (LoS) communication probability. UAVs equipped with MEC servers not only possess the computing power to improve task response speed but can also act as relays to other business units (BSs) for processing. Given the limited onboard power of UAVs, there is an urgent need to explore energy-saving solutions to improve the performance of UAV-assisted MEC systems.

[0004] Equipped with low-power, low-cost metasurfaces, reconfigurable intelligent surfaces (RIS) can enhance channel conditions and signal coverage by dynamically adjusting the wireless propagation environment, thereby improving the communication capacity and energy efficiency of UAV-assisted MEC systems. In RIS-assisted UAV communication networks, mobile UAVs may have the signal source and destination located on opposite sides of the RIS. However, in RIS, the incident signal can only be reflected, requiring the transmitter and receiver to be on the same side. Therefore, to provide a full-space radio environment, simultaneous transmission and reflection RIS (STAR-RIS) has recently been proposed. STAR-RIS-assisted UAV networks can overcome the geographical limitations of access terminals, improving the coverage and quality of wireless communication. In STAR-RIS-assisted UAV-MEC networks, the mobility of UAVs, the coefficient matrix of STAR-RIS, and offloading strategies need to be comprehensively considered to achieve minimum system energy consumption. Summary of the Invention

[0005] To address the aforementioned shortcomings in the prior art, this invention provides a TAR-RIS-assisted joint optimization method for UAV-MEC networks.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0007] A STAR-RIS-assisted joint optimization method for UAV-MEC networks includes the following steps:

[0008] S1. Based on the STAR-RIS-assisted UAV-MEC network model, construct the system model of the task offloading process and determine the objective function and constraints of the system model, including the network model, communication model and energy consumption model;

[0009] S2. Use the KKT conditional optimization method to solve the optimal task unloading decision in the task unloading process;

[0010] S3. Optimize the hovering position of the UAV using a continuous convex approximation method;

[0011] S4. Optimize the resource allocation of UAVs using the continuous convex approximation method;

[0012] S5. Alternately solve the unloading ratio, drone hovering position, and system model resource allocation in the unloading process, and set a threshold to make it converge, so as to obtain the optimal solution of task unloading ratio, drone hovering position, and system model resource allocation.

[0013] Furthermore, step S1 specifically includes the following steps:

[0014] S11. Construct a STAR-RIS-assisted UAV-MEC network model, including a single antenna. Drones equipped with MEC servers and STAR-RIS and two single-antenna users and users Each column and each row of STAR-RIS includes and The passive unit, the first The element represents the first in STAR-RIS Column and number Line elements;

[0015] S12. Construct a communication model, establish a Cartesian coordinate system, and the user... and single antenna The positions are respectively determined by vectors and Description, drone u Hovering in a fixed position to serve users, drone location, and STAR-RIS components. The positions are respectively and ;

[0016] S13. Construct an energy consumption model for users. Transmission energy consumption and nodes The calculated energy consumption is expressed as:

[0017]

[0018]

[0019] In the formula, For user i's transmission power consumption, Let k be the computational energy consumption of node k. For user i's transmit power, Indicates the capacitance coefficient. For nodes CPU frequency, For users Upload the task to the node Uplink transmission time, For nodes Calculate users Task duration;

[0020] S14. Determine the objective function and constraints of the system model.

[0021] Furthermore, the objective function and constraints in S14 are expressed as follows:

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] In the formula, The total energy of the STAR-RIS-assisted UAV-MEC communication network This is the transmission or reflection coefficient matrix of STAR-RIS. Location of the drone. The percentage of tasks unloaded. For users Task unloading ratio To handle users The computing resources required for the task , Let be the capacitance coefficients of the u-th and b-th nodes, respectively. Let be the CPU frequency of the b-th node; , In order, TAR-RIS number The amplitude and phase of each element.

[0029] Furthermore, step S2 specifically includes the following steps:

[0030] S21. Fix the UAV's position, transmission and reflection coefficient matrices, and rewrite the objective function and constraints;

[0031] S22. Introduce an approximate quadratic regularization term to optimize the STAR-RIS transmission or reflection coefficient matrix. and setting the drone's location The non-convexity problem arising from this;

[0032] S23. Use KKT conditions to solve for the optimal task unloading decision in the task unloading process.

[0033] Furthermore, the optimal task unloading decision in the unloading process of S23 is expressed as:

[0034]

[0035] In the formula, To make the most effective task-oriented uninstallation decision, The total energy consumption is given by the fixed position, transmission, and reflection coefficient matrices of the UAV. For fixed position, For the transmission and reflection coefficient matrix, This represents the percentage of unloaded tasks. It is a non-negative Lagrange multiplier.

[0036] Furthermore, step S3 specifically includes the following steps:

[0037] S31, Given the task unloading ratio and transmission and reflection coefficient matrix And introduce approximate variables;

[0038] S32. Solve for the lower bound of the non-convex constraint using the scaling method, and solve for the hovering position of the UAV using the Lagrange duality decomposition method. .

[0039] Furthermore, the hovering position of the UAV solved in S32 is represented as:

[0040]

[0041] In the formula, To obtain the optimal hovering position of the UAV. This is the hovering position of the drone. For non-negative Lagrange multipliers, It is a Lagrange function.

[0042] Furthermore, step S4 specifically includes the following steps:

[0043] S41. Set the fixed task unloading ratio and the drone hovering position, and rewrite the objective function and constraints;

[0044] S42. Introduce approximate variables to optimize the non-convexity problem under the conditions of fixed task unloading ratio and UAV hovering position;

[0045] S43. Solve the fixed transmission and reflection coefficient matrices using the Lagrange dual decomposition method to obtain the optimal resource allocation strategy.

[0046] Furthermore, the optimal resource allocation strategy in S3 is expressed as:

[0047]

[0048] In the formula, The optimal resource allocation strategy, The resource allocation status for user i. For Lagrange multipliers, It is the dual Lagrange function.

[0049] The present invention has the following beneficial effects:

[0050] (1) This invention proposes a novel task offloading, UAV hovering position, and resource allocation problem to minimize the energy consumption of STAR-RIS-assisted UAV-MEC wireless networks. This is the first study of energy-efficient task offloading and resource allocation for UAVs equipped with both MEC servers and STAR-RIS in a UAV communication network.

[0051] (2) This invention decomposes the energy minimization problem into a task offloading decision subproblem, a UAV hovering position subproblem, and a transmission resource allocation subproblem. For the task offloading decision subproblem, KKT conditions are used to optimize the task offloading ratio. For the UAV hovering position subproblem, SCA is used to optimize the UAV position. For the transmission resource allocation subproblem, SCA is used to optimize the STAR-RIS beamforming.

[0052] (3) Simulation results verify that compared with the existing (STAR-)RIS-assisted UAV-MEC wireless network, this method effectively reduces energy consumption and achieves fast convergence in a heterogeneous simulation environment. Attached Figure Description

[0053] Figure 1 This is a scenario diagram of the STAR-RIS-assisted UAV-MEC wireless communication according to the present invention;

[0054] Figure 2 This is a task execution time allocation diagram for the system of the present invention;

[0055] Figure 3 This is a schematic diagram of the solution framework for the present invention.

[0056] Figure 4 This is a simulation graph showing the convergence performance of the algorithm of this invention;

[0057] Figure 5 This is a simulation diagram showing the impact of the unloading ratio and UAV position on the performance of this invention.

[0058] Figure 6 This is a simulation diagram showing the impact of network bandwidth on performance according to the present invention.

[0059] Figure 7 This is a simulation diagram showing the impact of channel noise on the performance of this invention;

[0060] Figure 8 This is a simulation diagram showing the impact of user transmission power on performance in this invention;

[0061] Figure 9This is a simulation diagram showing the impact of the number of intelligent reflective surface elements on the performance of this invention;

[0062] Figure 10 This is a simulation diagram showing the impact of the computational requirements of this invention on performance. Detailed Implementation

[0063] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0064] A STAR-RIS-assisted joint optimization method for UAV-MEC networks, such as Figure 1 As shown, it includes the following steps:

[0065] S1. Based on the STAR-RIS-assisted UAV-MEC network model, construct the system model of the task offloading process and determine the objective function and constraints of the system model, including the network model, communication model and energy consumption model;

[0066] This embodiment specifically includes the following steps:

[0067] S11. Analyze and construct a STAR-RIS-assisted UAV-MEC network model.

[0068] The STAR-RIS-assisted UAV-MEC access network proposed in this invention, such as Figure 1 As shown. Includes a single antenna BS. A UAV equipped with a MEC server and STAR-RIS And two single-antenna users. Specifically, each column and each row of STAR-RIS is composed of... and It consists of passive units. The element represents the first in STAR-RIS Column and number The elements of the line. The two users in the transmission space and reflection space are respectively called users. and users This embodiment assumes that the direct communication links between the two users and the ground base station are blocked, and that each STAR-RIS element operates in Energy Splitting (ES) mode. In the proposed system, the UAV... It can partially process user-uploaded tasks and return the results to the user, while the remaining tasks are offloaded to the BS via STAR-RIS components. The process is as follows: Since the connection between the MEC and UAV uses low-latency, interference-free, and high-capacity optical fiber, the data transmission latency of the link between the UAV and the MEC server is not considered in this process. Furthermore, considering that UAVs and BSs often have high transmit power and the calculated results are usually small, this embodiment ignores the delays during local execution and task offloading phases.

[0069] This invention considers a partial offloading scheme for latency-sensitive computational tasks in a STAR-RIS-assisted UAV-MEC system. This computational offloading model allows for the offloading of computational tasks in UAVs. and BS Parallel processing of tasks in UAV. Tasks processed on the server are called local tasks, while those unloaded to the ground BS are called local tasks. The task that performs this task is called an unloading task. For example... Figure 2 As shown, the time allocation for task processing is illustrated in the STAR-RIS-assisted UAV-MEC system, where two users utilize tasks with durations... The same resource blocks are used to transfer and compute tasks. During the local execution phase, user-uploaded tasks are processed by a UAV with computing capabilities. The remaining tasks are processed on-site. During the task unloading phase, the remaining tasks are forwarded to the ground BS via STAR-RIS. Processing will begin. Once the task is complete, the UAV... and BS The calculated results will be returned to the user. In downlink communication, due to UAV... and BS They often have high transmission power and the calculation results are usually small, so the download time is relatively negligible during the local execution phase and the task unloading phase.

[0070] S12. Analyze and construct the communication model.

[0071] To describe the positions of the two users, the UAV, STAR-RIS, and the ground BS, this embodiment establishes a three-dimensional Cartesian coordinate system. and BS The positions are respectively determined by vectors sum vector To describe. This embodiment assumes a UAV. Hovering in a fixed position to serve the user. UAV Location and STAR-RIS components The positions are respectively and .

[0072] Due to the high probability of Loss of Service (LoS) links in UAV communication, this embodiment assumes that the user With UAV Communication channels and users With the The communication channels between the STAR-RIS components, and the first A STAR-RIS component and BS The communication channels between them are all LoS links and all follow the free space path loss model. Therefore, the nodes and The channel gain between them can be expressed as:

[0073] (1)

[0074] in, , , , It is the transmission power. distance The received power, and By node To the node The signal-to-interference-plus-noise ratio (SINR) of the wireless link is used as... It means, it means

[0075] (2)

[0076] in Let the transmission power be denoted as . From user to UAV or BS The variance of noise power in the link. Therefore, from node To the node The transmission rate is expressed as

[0077] (3)

[0078] in Indicates from node To the node The channel transmission bandwidth.

[0079] Channel for tasks to be executed locally

[0080] UAV Equipped with computing resources, allowing users Upload some tasks to UAV Processing is required. UAV The received signal can be written as

[0081] (4)

[0082] in For users The signal It is a UAV Received additive white Gaussian noise.

[0083] According to equations (2) and (3), from the user To UAV The signal-to-noise ratio and transmission rate are respectively used as and express.

[0084] Channel for task unloading

[0085] Due to the limitations of UAV energy and mission time, UAV It can only execute some of the user's tasks. BS The remaining tasks are handled by STAR-RIS. This allows for better differentiation from the user's perspective. To STAR-RIS components The channel gain, from STAR-RIS number 1 Components to BS The channel gain is expressed as Therefore, from the user's perspective To STAR-RIS and from STAR-RIS to BS The channel gains are respectively expressed as and .

[0086] It comes from users The transmission or reflection coefficient matrix of the STAR-RIS incident signal, where the STAR-RIS number... Amplitude of each component phase Therefore, when STAR-RIS operates in ES mode, the following constraints apply:

[0087] (5)

[0088] Therefore, BS The signal received at the location is

[0089] (6)

[0090] in It is BS Received additive white Gaussian noise.

[0091] Similarly, according to equations (2) and (3), from the user From STAR-RIS to BS The signal-to-noise ratio and transmission rate are used and express.

[0092] S13. Analyze and construct an energy consumption model.

[0093] when and , indicating user Upload local tasks to UAV Processing. When and , indicating user Upload the remaining tasks to BS Process it. Two users uploaded tasks to the node. The maximum uplink transmission time, i.e. ,in User Upload the task to the node The uplink transmission time. Represented as nodes Calculate users The task's timeframe. Then... and They are respectively represented as

[0094] (7)

[0095] (8)

[0096] If , ;otherwise, , . For users The task uninstallation ratio. To handle users The computing resources required for the task, i.e., the number of CPU cycles required to complete 1 bit of input data; It is a node CPU frequency (cycles per second).

[0097] Therefore, during task execution, the user Transmission energy consumption and nodes The computational energy consumption is

[0098] (9)

[0099] (10)

[0100] in Represents the capacitance coefficient, affected by the nodes. The impact of the processor's chip architecture.

[0101] S14. Determine the objective function and constraints.

[0102] From equations (9) and (10), it can be seen that the total energy of the STAR-RIS-assisted UAV-MEC communication network proposed in this embodiment is used It means, and , ,but

[0103] (11)

[0104] According to equation (11), the energy minimization problem of the STAR-RIS-assisted UAV-MEC system can be expressed as follows:

[0105] (12a)

[0106] (12b)

[0107] (12c)

[0108] (12d)

[0109] (12e)

[0110] (12f)

[0111] (12g)

[0112] Constraint (12b) states that the task offload ratio for each user takes a value between 0 and 1. Constraint (12c) states that in the UAV... The tasks processed on the server must be within the allocated local time interval. Internal transfer and computation. Constraint (12d) requirements are specified by BS. The task being processed must be within the allocated unloading time interval. Transmission and computation are performed within the system. Constraints (12e) and (12f) are respectively related to the STAR-RIS 12th... The amplitude and phase shift requirements of each component. Constraint (12g) specifies the UAV. The range of hovering positions.

[0113] Flying UAVs can alter the channel gain between the user and the UAV, and between the user, the STAR-RIS base station, thereby affecting transmission energy consumption. For ease of calculation, this embodiment assumes that the UAV... of and It is fixed, and UAV It shares the same location as STAR-RIS. (For UAV) of Optimize the system to minimize energy consumption.

[0114] like Figure 3 As shown, this embodiment uses an alternating optimization technique to decompose problem (12) into three sub-problems:

[0115] Task unloading decision subproblem: Given , That is, when and When fixed, problem (12) optimizes UAV unloading decision. To achieve minimal energy consumption, this embodiment employs KKT conditional optimization for task offloading decisions. express.

[0116] Task unloading decision subproblem: Given , Question (12) Optimize UAV Hover position To achieve minimal energy consumption, this embodiment uses the SCA method to optimize the hovering position. express.

[0117] STAR-RIS resource allocation subproblem: Given and Transmission and reflection coefficient matrix It is optimized to minimize the transmission energy of user offloading tasks. This embodiment uses the SCA method to obtain an optimal solution, using... express.

[0118] S2. Use the KKT conditional optimization method to solve the optimal task unloading decision in the task unloading process;

[0119] First given Solve the problem (12). When At that time, the goal of problem (12) is to make the optimal unloading decision and the optimal hovering position of the UAV to minimize energy consumption.

[0120] S21. Fix the UAV position and transmission / reflection coefficient matrices, and rewrite the objective function and constraints.

[0121] have .

[0122] In this embodiment, the objective function can be rewritten as follows:

[0123] (13a)

[0124]

[0125] (13b)

[0126] in Defined as Fixed UAV hovering position Problem (13) can be rewritten as

[0127] (14a)

[0128]

[0129] (14b)

[0130] (14c)

[0131] S22. Introducing an approximate quadratic regularization term.

[0132] Consider in the given and To address the nonconvexity of problem (14) arising from the optimization objective (14a), a near-quadratic regularization term is added, namely... To overcome this problem, among which It is a positive scalar parameter. The regularization problem lies in... This is equivalent to the original problem. Therefore

[0133] (15)

[0134] Question (14) can be written as

[0135] (16)

[0136]

[0137] S23. Obtain the feasible solution set through KKT conditions.

[0138] According to (16) It is about The function of has a positive semi-definite Hessian matrix, therefore problem (16) can be classified as a convex optimization problem. The optimal value can be obtained using the KKT conditions. The Lagrangian function of problem (16) It can be represented as (17), where , and These are the non-negative Lagrange multipliers corresponding to constraints (12b), (14b), and (14c), respectively.

[0139] (17)

[0140] Based on the KKT conditions, the optimal task unloading decision It can be represented as

[0141] (18)

[0142] if For feasible solution set For any point satisfying the following KKT conditions, the optimal task unloading decision can be obtained, as shown in (19).

[0143] (19a)

[0144] (19b)

[0145] (19c)

[0146] (19d)

[0147] (19e)

[0148] (19f)

[0149] (19g)

[0150] (19h)

[0151] (19i)

[0152] (19j)

[0153] S3. Based on the optimal solution obtained in S2, the continuous convex approximation method is used to optimize the hovering position of the UAV and the resource allocation of the system model.

[0154] In this section, when At that time, the goal of problem (13) is to determine the optimal hovering position of the UAV with the goal of minimizing energy consumption.

[0155] S31, Given the task unloading ratio and transmission and reflection coefficient matrix Introducing approximate variables

[0156] The nonconvexity of the target (13a) stems from and , the constant term The sum of its given initial values Replace them with their approximate variables, respectively using and This means that by transforming (13a) into a convex function, problem (13) can be restated as:

[0157]

[0158]

[0159] (20a)

[0160] (20b)

[0161] (20c)

[0162] (20d)

[0163] S32. Use the scaling method to derive the lower bound of the non-convex constraint.

[0164] To address the nonconvexity of (20c) and (20d), this embodiment uses the SCA method to derive a near-optimal solution. This embodiment first utilizes logarithmic approximation to convexify... As shown in (21)

[0165] (twenty one)

[0166] Among them when It is very tight. and It is about Two approximate constants are defined as follows:

[0167] (twenty two)

[0168] (twenty three)

[0169] Then The constraint (20c) can be approximated as its concave lower bound.

[0170] (twenty four)

[0171] in Represented as

[0172] (25)

[0173] in .

[0174] Similarly, constraint (20d) can be approximated as its concave lower bound, given by the following equation.

[0175] (26)

[0176] Therefore, problem (20) can be approximated by a convex problem, as follows:

[0177]

[0178]

[0179] (27a)

[0180] (27b)

[0181] (27c)

[0182] (27d)

[0183] S33. Solving the UAV hovering position using the Lagrange dual decomposition method.

[0184] CVX can be used to effectively solve the problem of finding the optimal hovering position for drones. This embodiment utilizes the Lagrange duality method to improve solution efficiency. Lagrange function Given in (28), where , , , and It is the Lagrange multiplier corresponding to the constraint condition in (28).

[0185] (28)

[0186] The Lagrange dual function of (28) can be given by the following equation.

[0187] (29)

[0188] By utilizing the convexity of problem (27), the optimal solutions to both the primal problem (27) and the dual problem (29) satisfy the KKT conditions. Through solving... To obtain the optimal solution Optimal hovering position ,in From (30)

[0189] (30)

[0190] Lagrange multipliers Updated using the following formula

[0191] (31)

[0192] (32)

[0193] (33)

[0194] (34)

[0195] (35)

[0196] In the above formula It is in the In the next iteration, the Lagrange multipliers are updated. The step size. Updated in each iteration. Without loss of generality, this embodiment assumes that... Each element has the same step size in the iteration.

[0197] S4. Optimize the resource allocation of UAVs using the continuous convex approximation method;

[0198] In this section, when , At that time, the goal of problem (12) is to determine the optimal STAR-RIS resource allocation with the goal of minimizing energy consumption.

[0199] S41, Given the task unloading ratio and UAV hovering position Rewrite the objective function and constraints

[0200] Therefore, problem (12) can be rewritten as

[0201] (36a)

[0202]

[0203] (36b)

[0204] in .

[0205] S42. Introduce approximate variables and use the scaling method to derive the lower bound of the non-convex constraint.

[0206] For a given and At that time, the nonconvexity of the objective (36a) and constraint (36b) comes from Similarly, an approximate variable replace To overcome its nonconvexity. Problem (36) is rewritten as

[0207] (37a)

[0208]

[0209] (37b)

[0210] (37c)

[0211] for , It can be written as

[0212] (38)

[0213] in , and Represented as

[0214] (39)

[0215] (40)

[0216] (41)

[0217] Therefore, constraints (12e) and (12f) are replaced by the following constraints.

[0218] (42)

[0219] in yes The Middle There are 42 elements. It is clear that (42) is convex.

[0220] To address the non-convex constraint (37c), the SCA method was employed. Specifically, due to... for Since it is convex, its lower bound can be derived as follows:

[0221] (43)

[0222] in, This was obtained in the previous iteration. Then, constraint (37c) can be replaced by the following constraint.

[0223] (44)

[0224] This is linear, therefore, problem (37) is rewritten as

[0225] (45)

[0226]

[0227] S44. Solve the fixed transmission and reflection coefficient matrices using the Lagrange duality method.

[0228] Although the convex problem (45) can be solved, for example using the CVX toolbox, this embodiment utilizes the Lagrangian duality method to improve computational efficiency. Lagrangian function Given in (46), where , and These are the Lagrange multipliers related to the three constraints of problem (45).

[0229] (46)

[0230] The Lagrange dual function is written as

[0231] (47)

[0232] By utilizing the convexity of problem (45), the optimal solutions to both the primal problem (45) and the dual problem (47) satisfy the KKT conditions. Through solving... The optimal solution was derived. The optimal result was obtained. for

[0233] (48)

[0234] Lagrange multipliers Updated using the following formula

[0235] (49)

[0236] (50)

[0237] (51)

[0238] In the formula It is in the In the next iteration, the Lagrange multipliers are updated. The step size vector.

[0239] S5. Alternately solve the unloading ratio, drone hovering position, and system model resource allocation in the unloading process, and set a threshold to make it converge, so as to obtain the optimal solution of task unloading ratio, drone hovering position, and system model resource allocation.

[0240] Use Matlab or other mathematical computing tools to solve the three sub-problems alternately, and set appropriate convergence thresholds until convergence is achieved, so as to obtain the optimal solution for task unloading, UAV location and resource allocation.

[0241] Analyze the impact of various factors on performance

[0242] This step will provide numerical results to demonstrate the performance of the proposed STAR-RIS-assisted UAV-MEC strategy in a two-user wireless communication system.

[0243] Analysis of algorithm convergence performance under different RIS and STAR-RIS

[0244] exist Figure 4 In this embodiment, the convergence performance of the proposed algorithm is investigated. Results show that the method guarantees energy consumption convergence to the optimal value after multiple iterations, and determines the optimal unloading ratio, UAV hovering position, and transmission and reflection coefficients. Furthermore, the convergence speed of the three benchmark schemes is slower than the solution proposed in this embodiment. The amplitude of the STAR-RIS / RIS element and the UAV hovering position are optimized using the random phase-shift method, while the reflection matrix and UAV hovering position are optimized using the RIS method. Although the number of optimization variables is reduced compared to the proposed scheme when the system is operational, the number of initial variables is greater than that in the solution of this embodiment. The setting of initial variables has a significant impact on the number of iterations required to obtain the optimal variables.

[0245] Analyzing the impact on performance under different unloading ratios and UAV hovering positions.

[0246] Figure 5The relationship between the offloading ratio, the hovering position of the UAV, and the total energy consumption of the system was explored. It was observed that the total energy consumption increases with the increase of the offloading ratio. This is because a higher offloading ratio means that user computing tasks are forwarded to the ground-based BS (Base Station) instead of being processed by the UAV, resulting in greater energy consumption due to long-distance transmission. Furthermore, the hovering position of the UAV has a certain impact on the total energy consumption of the system. By optimizing the hovering position of the UAV, computing services can be provided to users. Simultaneously, the STAR-RIS installed on the UAV can dynamically adjust the transmitted signal according to the change in the UAV's hovering position and reflect the signal back to the ground-based BS. Therefore, optimizing the offloading ratio and the UAV hovering position helps to reduce the total energy consumption of the system.

[0247] Analyzing the impact of network bandwidth on performance under different RIS and STAR-RIS standards

[0248] exist Figure 6 In this embodiment, the total energy consumption of the system under different network bandwidths is plotted. As the network bandwidth increases, the total energy consumption of all solutions decreases. This is because the increased network bandwidth improves the transmission rate between the user and the UAV, as well as the transmission rate from the user to the ground BS via STAR-RIS / RIS, reducing transmission latency and energy consumption. Simultaneously, by jointly optimizing the transmission and reflection matrices and task offloading decisions, the proposed solution consistently outperforms similar solutions, thus narrowing the gap between all solutions. Furthermore, wider network bandwidth can reduce the impact of random phase shifts, improve undesirable channel conditions caused by RIS, and consume less transmission energy.

[0249] Step 6.4: Analyze the impact of channel noise on performance under different RIS and STAR-RIS standards.

[0250] exist Figure 7 In this embodiment, the total energy consumption is plotted as the signal-to-noise ratio (SNR) changes. A higher SNR means a greater signal strength relative to noise. With the increase in SNR, the transmission energy is reduced due to the improved signal transmission environment, resulting in a decrease in the total energy consumption of all solutions. Meanwhile, as... Figure 7 As shown, the proposed solution is consistently superior to other solutions, and... Figure 7 This reduces the gap between all solutions. This embodiment concludes that the proposed solution has strong anti-interference performance and is more suitable for complex and challenging communication environments.

[0251] Analyzing the impact of user transmit power on performance under different RIS and STAR-RIS systems

[0252] This embodiment plots the total energy consumption trend as user transmission power changes. With increasing user transmission power, transmission energy consumption increases, and the performance of all solutions degrades. Because STAR-RIS introduces additional Loss of Service (LoS) links for both users to offload their tasks, the proposed solution performs better by jointly optimizing the transmission solution and reflection matrix, as well as the task offloading decision. Furthermore, the gap between STAR-RIS and RIS widens with increasing user transmission power. This is because STAR-RIS can improve the user's transmission rate in both the transmission and reflection regions, while RIS can only improve the user's transmission rate in the reflection region.

[0253] Analyzing the impact of the number of RIS / RIS elements on performance under different RIS and STAR-RIS models

[0254] exist Figure 9 This embodiment investigates the energy consumption of all schemes when the number of STAR-RIS / RIS components changes. With increasing component count, both STAR-RIS and RIS provide more transmission channels for incoming event signals, improving the transmission environment and reducing system energy. Simultaneously, the performance of the STAR-RIS scheme significantly outperforms the RIS-only scheme, with the gap widening through joint optimization of the transmission and reflection matrices. Furthermore, the gap between optimal and random phase shifts also increases with the number of components. This means that optimizing the phase shifts of all components can improve the performance of both STAR-RIS and RIS.

[0255] Analyzing the impact of task computational requirements on performance under different RIS and STAR-RIS systems.

[0256] exist Figure 10 This embodiment investigates the impact of different CPU cycles required to compute 1 bit of task data on the energy consumption of all solutions. As the CPU cycles required to compute 1 bit of task data increase, both the UAV and the ground BS consume more computational energy, reducing the performance of all solutions. Furthermore, the solution using STAR-RIS performs better than the solution using only RIS. This is because the computational power of the UAV is limited, and improvements to the transmission channel allow more tasks to be offloaded to the BS for processing. Similarly, as computational energy consumption increases, the gap between optimal phase shift and random phase shift also increases.

[0257] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0258] 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.

[0259] 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.

[0260] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0261] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for joint optimization of a STAR-RIS assisted UAV-MEC network, characterized in that, The method comprises the following steps: S1, constructing a system model and determining a target function and constraint conditions of the system model in a task offloading process in a STAR-RIS assisted UAV-MEC network model, including a network model, a communication model and an energy consumption model; S2, solving an optimal task offloading decision in the task offloading process by using a KKT condition optimization solving method; S3, optimizing a hovering position of the unmanned aerial vehicle by using a continuous convex approximation method, and specifically comprising the following steps: S31, given task offloading ratio and transmission and reflection coefficient matrices and introduce an approximation variable; S32, solve the lower bound of non-convex constraint by using the relaxation method, and solve the hovering position of UAV by using Lagrange dual decomposition method ; S4, optimizing resource allocation of the unmanned aerial vehicle by using the continuous convex approximation method; S5, alternately solving an offloading ratio, the hovering position of the unmanned aerial vehicle and the resource allocation of the system model in the offloading process, and setting a threshold to make it converge to obtain an optimal solution of the task offloading ratio, the hovering position of the unmanned aerial vehicle and the resource allocation of the system model, and specifically comprising the following steps: S51, setting a fixed task offloading ratio and a hovering position of the unmanned aerial vehicle, and rewriting the target function and the constraint conditions; S52, introducing an approximation variable to optimize a non-convex problem under the condition of the fixed task offloading ratio and the hovering position of the unmanned aerial vehicle; S53, solving a fixed transmission and reflection coefficient matrix by using a Lagrange dual decomposition method to obtain an optimal resource allocation strategy, and the optimal resource allocation strategy is expressed as: wherein is the optimal resource allocation strategy, is the resource allocation state of user i, is the Lagrange multiplier, is the dual Lagrange function.

2. The STAR-RIS assisted UAV-MEC network joint optimization method according to claim 1, wherein, The S1 specifically comprises the following steps: S11. Construct a STAR-RIS-assisted UAV-MEC network model, including a single antenna. Drones equipped with MEC servers and STAR-RIS and two single-antenna users and users Each column and each row of STAR-RIS includes... and The passive unit, the first The element represents the first in STAR-RIS Column and number Line elements; S12. Construct a communication model, establish a Cartesian coordinate system, and the user... and single antenna The positions are respectively determined by vectors and Description, drone u Hovering in a fixed position to serve users, drone location, and STAR-RIS components. The positions are respectively and ; S13, construct energy consumption model, user transmission energy consumption and node computation energy consumption, expressed as: In the formula, is the transmission energy consumption of user i, is the computing energy consumption of node k, is the transmission power of user i, represents the capacitance coefficient, is the CPU frequency of node , is the uplink transmission time of user uploading the task to node , is the time for node to compute the task of user ; S14, determining the target function and the constraint conditions of the system model.

3. The STAR-RIS assisted UAV-MEC network joint optimization method of claim 2, wherein, The target function and the constraint conditions in the S14 are expressed as: wherein, is the total energy of the STAR-RIS assisted UAV-MEC communication network, is the transmission or reflection coefficient matrix of the STAR-RIS, is the UAV position, is the task offloading ratio, is the user task offloading ratio, is the computing resource required to process the user task, , are the capacitance coefficients of the u-th and b-th nodes, respectively, is the CPU frequency of the b-th node; , are the amplitude and phase of the TAR-RIS element, respectively, , , is the constant term.

4. The STAR-RIS assisted UAV-MEC network joint optimization method of claim 1, wherein, The S2 specifically comprises the following steps: S21, fixing the unmanned aerial vehicle position, the transmission and reflection coefficient matrix and rewriting the target function and the constraint conditions; S22, introducing an approximate quadratic regularization term to optimize the transmission or reflection coefficient matrix of the STAR-RIS and setting the drone position non-convexity problem generated below; S23, solving an optimal task offloading decision in the task offloading process by using the KKT condition.

5. The STAR-RIS assisted UAV-MEC network joint optimization method of claim 4, wherein The optimal task offloading decision in the S23 is expressed as: wherein is the most task offloading decision, is the total energy consumption with fixed UAV position, transmission and reflection coefficient matrix, is the fixed position, is the transmission and reflection coefficient matrix, is the offloading task proportion, is the non-negative Lagrange multiplier.

6. The STAR-RIS assisted UAV-MEC network joint optimization method of claim 1, wherein, The solved hovering position of the unmanned aerial vehicle in the S32 is expressed as: In the formula, The optimal unmanned aerial vehicle hovering position is obtained, The optimal unmanned aerial vehicle hovering position is obtained, The non-negative Lagrange multiplier is obtained, The Lagrange function is obtained.

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