Heterogeneous uncrewed aerial vehicle (UAV) networks embedded with reconfigurable intelligent surfaces (RISs) present a promising paradigm for emergency wireless communications (EWC), offering enhanced coverage and resilience in harsh environments. However, extreme conditions in disaster areas necessitate robust performance evaluation under practical impairments, including outdated/imperfect channel state information (CSI) and discrete RIS phase shifts. Existing works lack a unified analytical framework for modeling CSI errors, employing inconsistent approaches that treat errors either as channel gain or as equivalent interference, leading to ambiguous benchmarks. To address this, we propose the <inline-formula> <tex-math notation="LaTeX">$\zeta $ </tex-math></inline-formula>-Model, a unified receiver-equivalent signal-to-noise (SNR) framework that continuously parameterizes residual-error exploitability via <inline-formula> <tex-math notation="LaTeX">$\zeta $ </tex-math></inline-formula>. This framework unifies the information-theoretic model (ITM) and the engineering baseline model (EBM) as the optimistic and pessimistic benchmark receiver treatments, while incorporating the simplified engineering model (SEM) as a tractable approximation. By employing the Fisher-Snedecor <inline-formula> <tex-math notation="LaTeX">$\mathcal {F}$ </tex-math></inline-formula> distribution to capture severe fading and shadowing, we derive moment-matching-based closed-form or finite-sum approximate expressions and asymptotic expressions for average capacity (AC), effective capacity (EC), and outage probability (OP) under the proposed unified framework and its boundary cases. Validated by Monte Carlo simulations, our framework quantifies performance limits and provides crucial insights for designing robust and efficient EWC systems under various channel conditions and system impairments.
Yinong Chen, Wenchi Cheng, Jing-Qing Wang et al.· IEEE Transactions on Wireles...· 0 citations
With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, the integration of WPT into non-terrestrial networks (NTNs), hereafter referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks. This paper proposes an energy-scheduling approach to jointly optimize energy efficiency, task completion rate, and task waiting time for power transfer from low Earth orbit satellites to terrestrial mobile user devices (UDs). To address the significant energy-scheduling challenges arising from satellite and UD mobility and further exacerbated by channel uncertainty due to stochastic propagation effects, we decompose the problem into three subproblems corresponding to a three-layer predictive framework: 1) a state prediction layer forecasts UD and satellite states; 2) an interaction mapping layer, employing a graph neural network (GNN), models the energy transfer efficiency between them; and 3) a decision-making layer determines the optimal energy allocation plan. We employ distinct machine learning (ML) methods within this framework, tailored to the specific requirements of each layer. Furthermore, balancing these competing objectives presents a challenging multi-objective optimization problem (MOP). We address this by adopting a key multi-objective reinforcement learning (MORL) technique: scalarizing the objectives into a single weighted-sum reward function. This scalarization transforms the MOP into a tractable, single-objective problem for the agents to solve. To help the agents balance these competing objectives effectively, we introduce a multi-agent deep learning model that integrates a self-attention mechanism with multi-agent proximal policy optimization (MAPPO). This approach provides a robust and efficient solution for WPT in NTNs, particularly for mission-critical scenarios. Simulation results show that the proposed approach can achieve a better overall trade-off than the baseline methods, maintaining competitive task completion rates and energy efficiency while reducing task waiting times. It also demonstrates robust performance under highly variable conditions.
