Intelligent Secure Transmission for UAV-Assisted Mobile Edge Computing under Malicious Flight Jamming
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摘要: 无人机辅助移动边缘计算凭借其高机动性，显著提升了网络覆盖能力与服务灵活性。然而，这种机动性也使其可能成为动态的恶意干扰源，对计算安全构成严重威胁。为了克服这一挑战，本文考虑恶意飞行干扰下无人机辅助边缘计算安全传输系统，提出一种基于双延迟深度确定性策略梯度的智能轨迹与资源协作安全传输方案。该方案通过联合优化无人机飞行轨迹、传输功率及计算任务分配，以最大化系统的安全计算容量。由于该问题涉及多类耦合变量，呈现高维非凸特性，难以直接解决。本文设计双延迟深度确定性策略梯度算法，借助智能体与环境的持续交互，实现对通信与计算资源的高效调度及无人机轨迹的自主规划。仿真结果表明，所提方案能够有效适应存在恶意干扰的动态环境，并在收敛性与系统稳定性方面优于基准方案，显著提升了系统的安全计算容量，从而为高动态环境下无人机辅助边缘计算面临的恶意飞行干扰问题提供了可行的解决途径。Abstract:
Objective Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) extends network coverage and improves service flexibility by exploiting the high mobility and rapid deployment capability of UAV platforms. However, the same mobility can also be exploited by an adversary, allowing a malicious UAV to act as a dynamic jammer and continuously degrade the reliability and security of computation offloading links. Existing optimization-based solutions usually rely on convex approximation, block-coordinate iteration, or staged decomposition, and therefore have difficulty responding efficiently when the jammer moves rapidly and communication, computation, and mobility variables are tightly coupled. To address this problem, a secure transmission system for UAV-assisted MEC under malicious flight jamming is investigated. The objective is to maximize the system secure computing capacity by jointly coordinating the service UAV trajectory, user transmit power, and computation-task allocation, thereby improving the robustness of offloading and processing in a highly dynamic hostile environment. Methods A system composed of one MEC-enabled service UAV, one malicious aerial jammer, and multiple ground users is established ( Fig. 1 ). The service period is divided into equal time slots, and one user is served in each slot. Line-of-sight air-to-ground and air-to-air channels are adopted, while obstacle blockage, jammer interference power, receiver noise, and path loss are incorporated into the achievable transmission-rate model. A partial-offloading mechanism is used, in which each user divides its workload between local execution and UAV-assisted computation. Transmission delay, edge-computing delay, local-computing delay, UAV computing energy, propulsion energy, and user transmission energy are jointly modeled. The resulting optimization problem maximizes the sum of locally processed and securely offloaded tasks subject to user association, blockage-state, delay, flight-region, maximum-speed, transmit-power, UAV-energy, and minimum secure-computing-capacity constraints. Because the problem is high-dimensional, non-convex, and strongly coupled, it is reformulated as a Markov decision process. The state contains the service UAV position and residual energy, jammer position, user locations, remaining workloads, and blockage states. The action contains the user transmit power, UAV velocity, and task-allocation decision. A Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is then designed within an actor–critic framework (Fig. 2 ). Two critic networks are used to reduce value overestimation, delayed actor updates are introduced to improve policy stability, and soft target-network updates are employed to support smooth convergence. Simulations are implemented in Python 3.8 with PyTorch, and the main parameters are listed inTable 1 . The proposed method is compared with Deep Deterministic Policy Gradient (DDPG) and a genetic algorithm.Results and Discussions The learned trajectories show that the service UAV can identify the spatial threat caused by the moving jammer and adaptively detour when approaching the interference region, while still visiting users and completing offloading tasks ( Fig. 3 ). This behavior demonstrates that trajectory planning and resource allocation are coordinated rather than optimized independently. The convergence comparison shows that the DDPG baseline exhibits larger fluctuations and converges to a lower secure computing capacity, whereas the TD3-based scheme converges faster and more smoothly (Fig. 4 ). The improvement is mainly attributed to the twin-critic structure and delayed policy update, which suppress value overestimation and reduce unstable policy drift. The results further indicate that a higher UAV computing capability increases the secure computing capacity because the processing time per offloaded bit is reduced, encouraging users to offload more tasks (Fig. 5 ). A lower flight altitude also improves performance by shortening the service-UAV-to-user distance and strengthening the communication channel, thereby increasing the amount of data that can be securely transmitted and processed within each time slot (Fig. 6 ). Under different malicious jamming powers, the proposed scheme consistently achieves the highest secure computing capacity, while the genetic algorithm shows noticeable performance variation and weaker adaptation to time-varying channel conditions (Fig. 7 ). These findings confirm that the proposed learning-based design remains effective across changes in computing capability, flight altitude, and interference intensity.Conclusions A TD3-based intelligent trajectory and resource-cooperative secure transmission scheme is developed for UAV-assisted MEC under malicious flight jamming. By transforming the coupled trajectory, power, and task-allocation problem into a continuous-control learning process, the scheme enables autonomous trajectory planning and dynamic communication–computation resource scheduling. Simulation results verify that it provides faster convergence, better stability, and higher secure computing capacity than DDPG and genetic-algorithm baselines. The proposed approach therefore offers a practical solution for secure UAV-assisted edge computing in dynamic and adversarial environments. -
表 1 仿真参数设置
参数 值 参数 值 L(m) 1000 $ {\sigma }^{2} $(dBm) -110 $ {\beta }_{0} $(dB) -50 B(MHz) 1 $ {\beta }_{1} $(dB) -60 $ {t}_{f} $(s) 1 $ {f}_{\text{loc}} $(GHz) 0.8 $ h $(m) 100 $ {f}_{s} $(GHz) 1.2 $ c $ 1000 $ {P}_{J} $(W) 0.1 $ \xi $ 0.1 
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