Unmanned aerial vehicle assisted edge computing secure transmission method based on fluid antenna

Abstract

A kind of unmanned aerial vehicle assisted edge computing security transmission method based on fluid antenna, in the method, unmanned aerial vehicle UAV_S equipped with edge computing server assists multiple ground users to complete computing task in flight process, eavesdropping unmanned aerial vehicle UAV_E steals the task information unloaded by user to UAV_S in flight process;Ground jammer GJ is arranged to send interference signal, and ground user is equipped with fluid antenna, can improve legal channel quality and deteriorate eavesdropping channel condition using spatial degree of freedom, improves system physical layer security performance under the premise of not increasing additional resource overhead.By jointly optimizing time allocation factor, user transmit power, user local computing task amount, service unmanned aerial vehicle trajectory and fluid antenna position, effectively suppress eavesdropping, improve the minimum safe computing capacity of system.The present application can significantly enhance the ability of system to resist aerial eavesdropping using fluid antenna technology, and improve the security transmission performance in resource-constrained scenario.

Description

Technical Field

[0001] This invention belongs to the field of mobile edge computing technology in the field of wireless communication technology, and specifically relates to a secure transmission method for UAV-assisted edge computing based on a fluid antenna. Background Technology

[0002] Mobile edge computing aims to leverage computing resources at the network edge to provide users with assistance in handling computationally intensive tasks, alleviating their computational burden and improving the computing efficiency of wireless communication networks. Drones, with their high flexibility and real-time capabilities, can carry edge computing servers to quickly reach designated areas to meet temporary service needs, expanding the coverage of wireless communication networks and enhancing their computing power. However, due to the open nature of the wireless channels between drones and ground users, and the prevalence of line-of-sight transmission in air-to-ground links, information offloaded by ground users faces a severe threat of eavesdropping. Eavesdroppers can intercept task information sent from terminal devices to service drones, leading to the leakage of sensitive data and seriously damaging the service quality of legitimate networks. Furthermore, existing systems are subject to strict resource constraints, further limiting system performance improvements.

[0003] Fluid antennas can dynamically adjust their position within a confined space, thereby reshaping the wireless channel and utilizing additional spatial degrees of freedom. The spatial reconfigurability of fluid antennas can be applied to UAV-assisted edge computing systems to improve channel quality for legitimate links while actively reducing the channel quality of eavesdropping links, thus enhancing secure transmission capabilities without increasing additional spectrum or power resources. Existing UAV-assisted edge computing secure transmission methods lack the ability to flexibly adjust antenna positions within a confined space, cannot reshape the wireless channel to actively enhance legitimate links and suppress eavesdropping links, and struggle to leverage spatial degrees of freedom to improve secure transmission performance. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies and address the problem that UAV-assisted edge computing secure transmission methods cannot fully utilize spatial degrees of freedom to combat eavesdropping, this invention provides a UAV-assisted edge computing secure transmission method based on a fluid antenna, which can simultaneously improve the quality of legitimate link channels and actively reduce the quality of eavesdropping link channels to enhance secure transmission performance.

[0005] The technical solution adopted by this invention to solve its technical problem is: A method for secure transmission via UAV-assisted edge computing based on fluid antennas, comprising a service UAV_S equipped with an edge computing server, and an eavesdropping UAV_E. K The method for secure transmission of UAV-assisted edge computing based on fluid antennas comprises a ground user equipped with a fluid antenna and a ground interference source GJ, and includes the following steps: 1) Service drones (UAVs) assist during flight. K A ground user completes a computing task, while the UAV_E eavesdrops on the computing task information that the user has unloaded to the UAV_S during flight; in order to suppress the eavesdropping behavior of the UAV_E, the ground interference source GJ sends an interference signal; at the same time, each user adjusts the position of its fluid antenna to improve the quality of the legitimate transmission link and deteriorate the quality of the eavesdropping link by utilizing the spatial degrees of freedom. 2) Each ground user executes a partial offload strategy, meaning that the ground user computes a portion of the task locally, and the remaining portion is offloaded to the UAV_S for computation; the ground user uses time division multiple access for task transmission, and the transmission time within each time slot is allocated among different ground users; 3) Calculate the task transmission rate from the ground user to the UAV_S. and the eavesdropping rate to UAV_E Thus, a safe unloading rate is achieved. ; Calculate the energy consumption of user's local computing tasks Energy consumption of user-sent uninstallation tasks; 4) Joint optimization of time allocation factors Ground user transmission power Local computing task data volume UAV_S flight trajectory and the location of ground user fluid antennas Construct an optimization problem; By introducing auxiliary variables , , The original optimization problem is transformed into an equivalent form, and then decomposed into two sub-problems: resource optimization and trajectory optimization. First, the trajectory of UAV_S is fixed, and the time allocation factor, transmit power, local computational cost and fluid antenna position are optimized. Then, the flight trajectory of UAV_S is optimized based on the optimized variable set, and the solution is obtained through alternating iterations until convergence.

[0006] Furthermore, in step 4), the following optimization problem is constructed: (1); The following conditions must be met (2); (3); (4); (5); (6); (7); (8); (9); (10); (11); (12); (13); (14); in, Indicates the time slot index. This represents the total number of time slots. The time slot length, For flight cycle, , For users In the time slot Two-dimensional coordinates of the fluid antenna Let be the side length of the movable range of the fluid antenna. and UAV_S and UAV_E in time slots respectively Horizontal position To ensure the minimum safe distance between drones, and Calculate the number of CPU cycles required per bit task for both the user and UAV_S. and users respectively And the maximum CPU computing frequency of UAV_S, The minimum amount of secure computation tasks that a user must complete within each time slot. For users The effective capacitance coefficient, For users Average power constraint.

[0007] Furthermore, in step 3), the user Task transfer rate to UAV_S and the eavesdropping rate to UAV_E They are represented as follows: (15); (16); in, and The signal-to-noise ratios at UAV_S and UAV_E are respectively expressed as: , (17); (18); in, Indicates user Channel coefficients to UAV_S Indicates user Channel coefficients to UAV_E This represents the channel coefficient from GJ to UAV_E. This indicates the power of the interference signal transmitted by GJ. and These represent the additive white Gaussian noise power at UAV_S and UAV_E, respectively. B For channel bandwidth. Safe offload rate. Defined as: (19); The channel coefficient Based on Ricean fading channel modeling and considering the influence of fluid antenna position on the channel, it is expressed as: (20); in For users To UAV_i( The distance, This is the path loss index. This is a small-scale fading coefficient, comprising the superposition of line-of-sight components and multiple non-line-of-sight components, and is related to the position of the fluid antenna. Related, represented as: (twenty one); in, Rice factor, This represents the number of non-line-of-sight paths. As the launch steering vector, Due to the difference in propagation distance, For wavelength, and These are the elevation angle and azimuth angle of arrival, respectively.

[0008] The technical concept of this invention is as follows: Existing UAV-assisted edge computing secure transmission methods lack the ability to flexibly adjust antenna positions within a limited space, cannot reshape the wireless channel to actively enhance legitimate links and suppress eavesdropping links, struggle to leverage spatial freedom to improve secure transmission performance, and face challenges such as limited communication and computing resources. This invention deploys a fluid antenna at the user end, dynamically adjusting the antenna position to utilize spatial diversity gain, enhancing the quality of legitimate links and degrading the quality of eavesdropping links without additional resource overhead. By jointly optimizing time allocation, transmit power, local computing, UAV trajectory, and fluid antenna position, eavesdropping is effectively suppressed, thereby improving the system's secure computing capacity.

[0009] The beneficial effects of this invention are mainly reflected in the following aspects: it solves the problems that existing UAV-assisted edge computing secure transmission methods lack the ability to flexibly adjust the antenna position in a limited space, cannot reshape the wireless channel to actively enhance the legitimate link and suppress the eavesdropping link, and find it difficult to improve the secure transmission performance by leveraging spatial degrees of freedom. By deploying a fluid antenna at the user end and jointly optimizing multi-dimensional resources and UAV trajectory, the system's secure computing capacity is significantly improved, and the security performance is superior to that of traditional fixed antenna schemes in complex multipath environments. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of a system model of a UAV-assisted edge computing secure transmission method based on a fluid antenna, according to the present invention. Figure 2 This is a comparison chart of the convergence performance of the fluid antenna scheme (FAS) and the fixed antenna scheme (FPA) under different numbers of multipath components according to the method of the present invention; Figure 3 This is a schematic diagram of the optimized trajectory of UAV_S using the method of the present invention when there is sufficient task time. Detailed Implementation

[0011] The present invention will now be further described with reference to the accompanying drawings.

[0012] Reference Figures 1-3 A method for secure transmission of UAV-assisted edge computing based on fluid antennas is proposed. The secure transmission system for UAV-assisted edge computing based on fluid antennas consists of a service UAV_S equipped with an edge computing server and an eavesdropping UAV_E. K It consists of a ground user equipped with a fluid antenna and a ground interference source GJ.

[0013] In the method of this embodiment, the service drone UAV_S assists during flight. KEach user completes its computational tasks within a specified period. The eavesdropping drone UAV_E steals task information that users offload to UAV_S for computation during flight. To suppress UAV_E's eavesdropping behavior, a ground-based jamming source GJ sends jamming signals. Simultaneously, each user adjusts the position of its fluid antennas, utilizing spatial degrees of freedom to improve the quality of legitimate transmission links and degrade the quality of eavesdropping links. Users implement a partial offloading strategy: each user computes a portion of the task locally, and the remaining portion is offloaded to UAV_S for computation. Users employ time-division multiple access (TDMA) for task transmission. The task transmission rate from user to UAV_S is calculated. and the eavesdropping rate to UAV_E Calculate the energy consumption of the user's local computing tasks. And the energy consumption of users sending uninstallation tasks ,in As a time allocation factor, For user transmission power, This refers to the time slot length. The auxiliary computing capability of UAV_S is determined by its maximum CPU computing frequency. And the number of CPU cycles required to calculate each bit of task Apply constraints.

[0014] In this embodiment, the user Task transfer rate to UAV_S and the eavesdropping rate to UAV_E They are represented as follows: (15); (16); in, and The signal-to-noise ratios at UAV_S and UAV_E are respectively expressed as: (17); (18); in, Indicates user Channel coefficients to UAV_S Indicates user Channel coefficients to UAV_E This represents the channel coefficient from GJ to UAV_E. This indicates the power of the interference signal transmitted by GJ. and These represent the additive white Gaussian noise power at UAV_S and UAV_E, respectively. B For channel bandwidth; safe offload rate Defined as: (19); The channel coefficient Based on Ricean fading channel modeling and considering the influence of fluid antenna position on the channel, it is expressed as: (20); in For users To UAV_i( The distance, This is the path loss index. This is a small-scale fading coefficient, comprising the superposition of line-of-sight components and multiple non-line-of-sight components, and is related to the position of the fluid antenna. Related, represented as: (twenty one); in, Rice factor, This represents the number of non-line-of-sight paths. As the launch steering vector, Due to the difference in propagation distance, For wavelength, and These are the elevation angle and azimuth angle of arrival, respectively.

[0015] The methods for optimizing communication, computing resources, fluid antenna position, and trajectory in this embodiment are as follows: Joint optimization time allocation factor Ground user transmission power Local computing task data volume UAV_S flight trajectory and the location of ground user fluid antennas The following optimization problem is constructed: (1); The following conditions must be met: (2); (3); (4); (5); (6); (7); (8); (9); (10); (11); (12); (13); (14); in, Indicates the time slot index. This represents the total number of time slots. For the duration of the time slot, For flight cycle, , For users In the time slot The two-dimensional coordinates of the fluid antenna Let be the side length of the movable range of the fluid antenna. and UAV_S and UAV_E in time slots respectively Horizontal position To ensure the minimum safe distance between drones, and Calculate the number of CPU cycles required per bit task for both the user and UAV_S. and users respectively And the maximum CPU computing frequency of UAV_S, The minimum amount of secure computation tasks that a user must complete within each time slot. For users The effective capacitance coefficient, For users Average power constraint.

[0016] By introducing auxiliary variables , , The original optimization problem is transformed into an equivalent form, and then decomposed into two sub-problems: resource optimization and trajectory optimization. First, the time allocation factor, transmit power, local computational workload, and fluid antenna position for UAV_S trajectory optimization are fixed. Then, the flight trajectory of UAV_S is optimized through iterative solutions until convergence.

[0017] The UAV-assisted edge computing secure transmission method based on fluid antennas in this embodiment solves the problems of existing UAV-assisted edge computing secure transmission methods lacking the ability to flexibly adjust the antenna position in a limited space, being unable to reshape the wireless channel to actively enhance legitimate links and suppress eavesdropping links, and being unable to improve secure transmission performance by leveraging spatial degrees of freedom. By deploying fluid antennas at the user end and jointly optimizing multi-dimensional resources and UAV trajectories, the system's secure computing capacity is significantly improved, and the security performance is superior to traditional fixed antenna solutions in complex multipath environments.

[0018] In this embodiment, GJ and ground users are distributed within a specific area. UAV_E hovers along a predetermined trajectory to steal user mission information. UAV_S departs from the flight origin along an optimized trajectory. Towards the end of the flight Flight assists users in completing computing tasks.

[0019] Figure 2 The convergence performance of the fluid antenna scheme used in this invention and the traditional fixed antenna scheme are compared under different numbers of multipath components. The results show that the fluid antenna scheme is significantly better than the fixed antenna scheme, and the performance gain increases with the increase of multipath richness.

[0020] Figure 3 The optimized trajectory of the UAV_S under sufficient flight cycles is shown. It can be seen that the UAV_S will stay as close to the user as possible during flight to maximize channel gain, and when the mission completion time is relatively generous, the UAV_S will hover briefly above each user.

[0021] The embodiments described in this specification are merely examples of implementations of the inventive concept and are for illustrative purposes only. The scope of protection of this invention should not be considered limited to the specific forms described in these embodiments; rather, it extends to equivalent technical means conceived by those skilled in the art based on the inventive concept.

Claims

1. A method for secure transmission of UAV-assisted edge computing based on fluid antennas, the system consisting of a service UAV_S equipped with an edge computing server, and an eavesdropping UAV_E. K It consists of a ground user equipped with a fluid antenna and a ground interference source GJ, characterized in that, The UAV-assisted edge computing secure transmission method based on fluid antennas includes the following steps: 1) Service drones (UAVs) assist during flight. K A ground user completes a computational task, while the UAV_E eavesdrops on the task information that the ground user unloads to the UAV_S for computation during flight; in order to suppress the eavesdropping behavior of the UAV_E, the ground interference source GJ sends interference signals; at the same time, the ground user adjusts the position of its fluid antenna to improve the quality of the legitimate transmission link and deteriorate the quality of the eavesdropping link by utilizing the spatial degrees of freedom. 2) Each ground user implements a partial offload strategy, meaning that the ground user calculates a portion of the task data locally, and the remaining portion is offloaded to the UAV_S for calculation; the ground user uses time division multiple access for task transmission, and the transmission time in each time slot is allocated among different ground users; 3) Calculate the task transmission rate from the ground user to the UAV_S. and the eavesdropping rate to UAV_E Thus, a safe unloading rate is achieved. ; 4) Joint optimization of time allocation factors Ground user transmission power Local computing task data volume UAV_S flight trajectory and the location of ground user fluid antennas Construct an optimization problem; By introducing auxiliary variables , , The original optimization problem is transformed into an equivalent form, and then decomposed into two sub-problems: resource optimization and trajectory optimization. First, the time allocation factor, transmit power, local computational workload, and fluid antenna position of the UAV_S trajectory optimization are fixed. Then, the flight trajectory of the UAV_S is optimized by iteratively solving the problem until convergence.

2. The UAV-assisted edge computing secure transmission method based on fluid antenna as described in claim 1, characterized in that, In step 4), the following optimization problem is constructed: (1)； The following conditions must be met (2)； (3)； (4)； (5)； (6)； (7)； (8)； (9)； (10)； (11)； (12)； (13)； (14)； in, Indicates the time slot index. This represents the total number of time slots. For the duration of the time slot, For flight cycle, , For users In the time slot The two-dimensional coordinates of the fluid antenna Let be the side length of the movable range of the fluid antenna. and UAV_S and UAV_E in time slots respectively Horizontal position, To ensure the minimum safe distance between drones, and Calculate the number of CPU cycles required per bit task for both the user and UAV_S. and users respectively And the maximum CPU computing frequency of UAV_S, The minimum amount of secure computation tasks that a user must complete within each time slot. For users The effective capacitance coefficient, For users Average power constraint.

3. The UAV-assisted edge computing secure transmission method based on fluid antenna as described in claim 2, characterized in that, In step 3), the user Task transfer rate to UAV_S and the eavesdropping rate to UAV_E They are represented as follows: (15)； (16)； in, and The signal-to-noise ratios at UAV_S and UAV_E are respectively expressed as: (17)； (18)； in, Indicates terminal device Channel coefficients to UAV_S Indicates user Channel coefficients to UAV_E This represents the channel coefficient from GJ to UAV_E. This indicates the power of the interference signal transmitted by GJ. and The additive white Gaussian noise power at UAV_S and UAV_E are respectively. B For channel bandwidth; safe offload rate Defined as: (19)； The channel coefficient Based on Ricean fading channel modeling and considering the influence of fluid antenna position on the channel, it is expressed as: (20)； in, For users Distance to UAV_i , This is the path loss index. This is a small-scale fading coefficient, comprising the superposition of line-of-sight components and multiple non-line-of-sight components, and is related to the position of the fluid antenna. Related, represented as: (21)； in, Rice factor, This represents the number of non-line-of-sight paths. As the launch steering vector, Due to the difference in propagation distance, For wavelength, and These are the elevation angle and azimuth angle of arrival, respectively.

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