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.

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.

This study addresses the training sequence design problem for multipath channel estimation in faster‐than‐Nyquist (FTN) system. The authors process the estimation in the frequency domain and give the search criterion which is only the function of the power spectrum of the training sequence. The optimal sequence according to this criterion is found by using search strategy described in detail in this study and the performance of it is tested by Monte Carlo simulation.