Re-establishing emergency communication and ensuring rapid response are critical for rescue operations in natural disaster scenarios, such as earthquakes, floods, and wildfires. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks have emerged as a promising solution to re-establish communication links and provide flexible computational support in these complex environments. However, the existing UAV-assisted MEC research has not fully investigated the joint optimization of task offloading and resource allocation (JTORA) problem, considering both terrain obstacles and high-interference zones. In this paper, we investigate the JTORA problem to minimize the energy consumption for communication and computation in a rate-splitting multiple access (RSMA)-based UAV-assisted mobile edge computing (MEC) network. RSMA is utilized to enhance interference management and improve spectral efficiency. We propose a proximal policy optimization (PPO)-based method to optimize the task offloading ratio, message splitting ratio, and RSMA precoding matrix for the proposed JTORA problem. Simulation results show that the proposed approach effectively enhances system efficiency and sustainability.

Natural disasters often destroy critical infrastructure, such as terrestrial communication networks and transportation routes, thereby severely disrupting post-disaster rescue operations. To rapidly re-establish communication links and provide flexible computational support in disaster rescue scenarios, the integration of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has emerged as a promising solution. Nevertheless, the highly complex and resource-constrained characteristics of disaster environments pose significant challenges for UAV-assisted computation task offloading. In this paper, we investigate the joint task offloading and resource allocation (JTORA) problem to minimize the energy consumption associated with communication and computation during task offloading. Specifically, we develop a twin-delayed deep deterministic policy gradient (TD3)-based JTORA (JTORA-TD3) algorithm, which enables the UAV to optimize decisions of task offloading and resource allocation intelligently. To further enhance the training efficiency of the JTORA-TD3 algorithm in a complex disaster rescue environment, we integrate a large AI model (LAM) into the TD3 framework. Based on the textual interaction, we propose an LAM-enabled TD3-based JTORA (JTORA-LAM4TD3) algorithm. Simulation results demonstrate that the proposed JTORA-LAM4TD3 algorithm significantly outperforms baselines. These findings confirm the effectiveness of integrating LAMs with deep reinforcement learning (DRL) for solving the decision optimization problem.

Abstract Reactive jammers select jamming strategies according to the users’ responses; thus, conventional anti‐jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi‐functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti‐jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back‐to‐back coordination‐based learning algorithm to reach the optimal channel selection and power adaption in a non‐cooperative way. To alleviate the lack of mutual information exchange, the back‐to‐back coordination mechanism derives all users to deceive the jammer by inferring others’ strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency‐cost ratio compared to the state‐of‐the‐art cooperative schemes.

Abstract In this paper, the problem of mission planning and spectrum resource allocation for cooperative reconnaissance of ground targets with multiple unmanned aerial vehicles (UAVs) is studied. A joint mission planning and spectrum resource optimization algorithm for multi‐UAVs is proposed to improve the information transmission rate by reusing the spectrum of existing users. The joint optimization problem is formulated as mixed‐integer non‐linear programming. The block coordinate descent (BCD) method is further applied to achieve the optimal strategies of mission planning, channel allocation, and power control. Specifically, an improved genetic algorithm (GA) combined with the successive convex approximation (SCA) is used to solve the sub‐problem of mission planning. For the channel allocation sub‐problem, an iterative convergence channel allocation algorithm is proposed. Numerical results show that the proposed algorithm can achieve a higher UAV transmission rate and better robustness than existing algorithms.

Abstract In unmanned aerial vehicle (UAV)‐assisted wireless sensor networks (WSNs), UAVs are employed to collect sensing data from each sensor node (SN). Because of limited battery capacity, shortening the time of data collection by the UAV is necessary. Notably, the task completion time is related to UAV trajectory and spectrum allocation. In this paper, joint trajectory design and spectrum allocation is studied to minimize the task completion time of UAVs while ensuring the target upload data amount for each SN. The cases that all SNs are located within the communication range of a UAV is explored first; the formulated problem is non‐convex and difficult to solve directly. Hence, an iterative algorithm based on block coordinate descent and successive convex approximation is proposed to decompose and transformed the original problem into two convex optimizations. Furthermore, the proposed algorithm is applied to the general case that all SNs are widely distributed by leveraging the spiral algorithm and traveling salesman problem technique. Simulation results show that the proposed algorithm can effectively reduce the task time of UAV compared to other benchmark algorithms.