用AI构建无线信道模型，提升精度与泛化能力。
COST CA20120 INTERACT Framework of Artificial Intelligence Based Channel Modeling
- 提出基于AI的信道建模框架，融合物理先验知识
- 解决预测不确定性、泛化能力差等关键挑战
- 适合通信系统设计与理论研究者参考
准确的信道模型是通信理论研究与系统设计的前提。传统建模方法依赖统计与确定性手段，但在精度、泛化能力与计算复杂度方面仍存在显著局限。根本原因在于现代通信系统中，物理环境与信道特性之间的定量映射日益复杂。在COST CA20120行动背景下，本文评估并探讨了利用人工智能（AI）进行信道建模的可行性与实现路径，展望该领域未来方向。首先，提出一种基于AI的信道建模框架，以刻画复杂无线信道；其次，详细分析三大核心挑战并给出可能解决方案：（i）估计AI预测的不确定性，（ii）融合传播先验知识以增强泛化能力，（iii）实现可解释的AI建模。通过典型数值结果展示AI信道建模的能力。
原文摘要 · Abstract (English)
Accurate channel models are the prerequisite for communication-theoretic investigations as well as system design. Channel modeling generally relies on statistical and deterministic approaches. However, there are still significant limits for the traditional modeling methods in terms of accuracy, generalization ability, and computational complexity. The fundamental reason is that establishing a quantified and accurate mapping between physical environment and channel characteristics becomes increasing challenging for modern communication systems. Here, in the context of COST CA20120 Action, we evaluate and discuss the feasibility and implementation of using artificial intelligence (AI) for channel modeling, and explore where the future of this field lies. Firstly, we present a framework of AI-based channel modeling to characterize complex wireless channels. Then, we highlight in detail some major challenges and present the possible solutions: i) estimating the uncertainty of AI-based channel predictions, ii) integrating prior knowledge of propagation to improve generalization capabilities, and iii) interpretable AI for channel modeling. We present and discuss illustrative numerical results to showcase the capabilities of AI-based channel modeling.
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