Author

Wei Wu

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2026

UAV-Mounted IRS-Enhanced Secondary Transmission and Primary Covert Communication for Cognitive Radio Networks

A win-win scheme is investigated for uncrewed aerial vehicle-mounted intelligent reflecting surface (UIRS) enhanced secondary network (SN) data transmission and the primary network (PN) covert communication cooperative cognitive radio networks (UIRS-CCCRNs), where the moving UIRS can flexibly help SN to share the PN’s licensed spectrum and assist PN to hide its wireless transmission behavior. Based on this protocol, a SN’s average rate maximization problem is established under the imperfect channel state information (CSI), while ensuring the PN’s covert communication and weakening the warden’s detection capability. To solve the non-convexity and computational challenges, the original problem is decoupled into three subproblems, and the triangle inequality and the Cauchy-Schwarz inequality are employed to handle the CSI uncertainty. For both the SN’s transmit beamforming and the UIRS’s phase shift matrix subproblems, their optimal solution can be obtained by applying a low-complexity penalty dual decomposition with gradient projection (PDDGP) algorithm. Then, the non-convex UIRS’s trajectory subproblem is tackled by the successive convex approximation. Finally, an efficient joint trajectory and beamforming design that employs the PDDGP algorithm integrated into the block-coordinate-descent framework is proposed to obtain a suboptimal solution of the original problem. Simulation results demonstrate that by exploiting the UIRS, the proposed scheme flexibly assists the SN’s data transmission and PN’s covert communications, thereby achieving a win-win outcome for both PN and SN.

Wei Zhang, Xiaopeng Liang, Qian Deng et al. · IEEE Transactions on Wireles... · 0 citations

2026

Predictive and Adaptive Semantic Communication for QoE Optimization in Multi-UAV Networks

Deep learning-based semantic communication has demonstrated superior efficiency in wireless image transmission. However, traditional reactive schemes often suffer from outdated Channel State Information (CSI) in highly dynamic multi-UAV environments, leading to severe latency and utility degradation. To address this challenge, we propose the Predictive and Adaptive Semantic Communication (PASC) framework. PASC integrates a GRU-based predictor to anticipate channel evolution, enabling the proactive adjustment of compression rates by dynamically calibrating attention-weight thresholds. Furthermore, a deadline-aware deep reinforcement learning (DRL) algorithm is proposed to jointly assign sub-channels, allocate bandwidth, and adjust power based on predictive states, thereby preventing resource monopolization. Simulation results confirm that PASC achieves a 58.8% improvement in average Quality of Experience (QoE) compared to non-predictive baselines in low-SNR regimes. Crucially, the framework demonstrates formidable robustness to imperfect CSI, strictly bounding end-to-end latency and maintaining high semantic fidelity even in the presence of extreme prediction noise.

Yuxin Yang, Wei Wu, Fuhui Zhou et al. · IEEE Wireless Communications... · 0 citations