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
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 However, traditional reactive schemes often suffer from outdated Channel State Information (CSI) in highly dynamic multi-UAV environments, leading to severe latency and utility degradation.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.
Uncrewed aerial vehicles (UAVs) identified as agents are promising for future communications and networking due to their flexibility and intelligence. However, UAVs are subjected to the severe spectrum scarcity problem. To tackle this challenge, an embodied-enhanced cognitive UAV network is investigated, where an embodied UAV agent is deployed to autonomously perceive the environment, make adaptive decisions, and execute actions. Then, a dynamic spectrum aggregation and resource allocation problem is formulated to maximize the average sum throughput of the secondary network. Moreover, a hybrid action space deep reinforcement learning (DRL) framework is proposed to enable the embodied UAV agent to make joint discrete spectrum allocation and continuous trajectory decisions. Specifically, the proposed framework decomposes the hybrid policy into parallel continuous and discrete components with a shared state encoder, thereby optimizing the continuous and discrete actions simultaneously. By exploiting the proposed framework, an intelligent joint spectrum allocation and UAV trajectory optimization scheme is developed for the embodied-enhanced cognitive UAV network. Finally, simulation results validate the effectiveness of our proposed scheme, and demonstrate the superior performance in convergence and resource utilization relative to the traditional DRL-based schemes. Moreover, our proposed scheme maintains lower computational complexity and shorter inference time compared to the benchmark schemes.
Y. Diao, Rui Ding, Zhijie Zeng et al.· IEEE Transactions on Cogniti...· 0 citations
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