Benefiting from the flexible deployment capability and favorable propagation condition, the unmanned aerial vehicle (UAV) relay networks are crucial for rapid coverage extension and resilient connectivity in adversarial communication environments. However, the transmission effectiveness is constrained by scarce energy resource and severe hostile jamming attacks. Motivated by this need, this paper investigates the semantic communication (SemCom) empowered UAV relay networks that integrates SemCom with simultaneous wireless information and power transfer (SWIPT). For the first time, the semantic encoding/ decoding is embedded into the two-hop communication while the power-splitting (PS) jointly coordinates information transmission and energy harvesting. With the imperfect channel state information (CSI), we formulate the worst-case sum SemCom rate maximization problem subject to the constraints of semantic decoding feasibility, energy harvesting and transmit power. To address the intractable problem, we develop a robust three-step monotonic-optimization (MO) framework with low-complexity feasibility checking algorithm (LFCA). Firstly, a robust generalized discretization approach is used to convert the imperfect CSI. Then, the MO framework with LFCA is presented to obtain optimal solution of transmit beamformer, receive decoder and PS ratio with dimension reduction method. Simulations results show that proposed MO-LFCA scheme outperforms conventional bit-level SWIPT scheme and the other benchmark schemes versus different jamming power levels and antenna configurations, demonstrating that the integration of SemCom and SWIPT mechanism can enhance anti-jamming and efficiency performance in UAV relay networks.

As an emerging technology, mobile edge computing (MEC) network paradigm provides great computing potential for edge services, which has been widely applied in friendly city environment. However, there are still many challenges to deploy MEC technology in harsh tactical communication environment due to poor communication conditions, limited computational resources, and hostile malicious interference. Thus, this article investigates the computational resource pricing and task offloading strategy in tactical MEC ad-hoc network, which consists of multiple tactical edge nodes, ground MEC servers, unmanned aerial vehicle-MEC (UAV-MEC) servers and a low-Earth orbit-MEC (LEO-MEC) satellite server. Each edge node can offload its partial computation-intensive task to the MEC servers to reduce computational delay and energy consumption. First, a multileader and multifollower Stackelberg game (MLMF-SG) which includes leader subgame for MEC servers and follower subgame for edge nodes, is proposed to formulate the interaction between servers and edge nodes. It has been proved that there exists a Stackelberg equilibrium (SE) in the proposed MLMF-SG. In order to decrease the delay, energy consumption, and resource overhead, the follower subgame is further formulated as a multimode computation task offloading game. With the help of the exact potential game (EPG), we prove that the follower subgame can converge to the Nash equilibrium (NE). To achieve the SE, a hierarchical distributed iterative algorithm is designed to maximize the utilities of the leaders and followers. Finally, the simulation results demonstrate that the proposed scheme can achieve better performance compared with the existing schemes.