AI 中文总结
针对应急RIS-UAV网络实际损伤下的性能评估问题，提出ζ-模型统一分析框架，推导相关性能表达式并经仿真验证，为EWC系统设计提供关键见解。
AI 中文摘要
嵌入可重构智能表面（RIS）的异构无人机（UAV）网络为应急无线通信（EWC）提供了极具前景的范式，可在恶劣环境中增强覆盖与抗扰性。然而，灾区极端条件要求在实际损伤（包括过时/不完美的信道状态信息（CSI）和离散RIS相移）下进行鲁棒性能评估。现有研究缺乏用于建模CSI误差的统一分析框架，采用不一致方法将误差视为信道增益或等效干扰，导致基准模糊。为此，本文提出ζ-模型，一种统一的接收机等效信噪比（SNR）框架，通过ζ持续参数化残余误差的可利用性。该框架将信息论模型（ITM）和工程基准模型（EBM）分别作为乐观和悲观的基准接收机处理方式，同时纳入简化工程模型（SEM）作为易处理近似。采用Fisher-Snedecor F分布捕捉严重衰落和阴影效应，推导了所提统一框架及其边界情况下的平均容量（AC）、有效容量（EC）和中断概率（OP）的基于矩匹配的闭式或有限和近似表达式及渐近表达式。经蒙特卡洛仿真验证，该框架量化了性能极限，并为在各种信道条件和系统损伤下设计鲁棒高效的EWC系统提供了关键见解。
英文摘要
Heterogeneous unmanned 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extreme conditions in disaster areas necessitate robust performance evaluation under practical impairments, including outdated/imperfect channel state information (CSI) and discrete RIS phase shifts.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 $ζ$-Model, a unified receiver-equivalent signal-to-noise (SNR) framework that continuously parameterizes residual-error exploitability via $ζ$. 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 $\mathcal{F}$ 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.
