面向6G的可泛化且计算高效的信道外推：一种从模块化视角构建的可配置AI驱动框架
Generalizable and Computational Efficient Channel Extrapolation for 6G: A Configurable AI-Driven Framework Built from a Modular Perspective
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中文总结 AI 辅助
针对6G信道外推泛化差、复杂度高的问题，提出三阶段模块化可配置AI框架，降低了信道外推误差与复杂度，性能优于混合专家模型。
中文摘要 AI 辅助
以可控开销获取信道状态信息（CSI）是提供高性能通信服务的关键，这在新兴的第六代（6G）移动网络中极具挑战性。信道外推技术被提出用于利用一小部分已知CSI推断完整CSI，人工智能（AI）可大幅提升其性能。然而，AI驱动的信道外推存在跨场景泛化能力差、计算复杂度高的问题，这在AI及大语言模型的广泛研究中十分常见。受人类大脑模块化功能的启发，我们从模块化视角提出一种可配置AI驱动框架，以实现可泛化且计算高效的信道外推。该框架为三阶段结构，包含专家涌现、专家构建和专家选择。该框架假设CSI相关性可通过少量专门功能模块（专家）捕获，这些专家在不同场景下以不同方式被激活。这种模块化特性在专家涌现阶段通过使用覆盖全面场景的CSI数据进行预训练而形成。在专家构建阶段，将具有相似权重空间模式的神经元分组为专家。在专家选择阶段，添加轻量级门控函数以控制专家的路由，并针对每个场景进行微调。仿真结果表明，所提出的三阶段框架将信道外推误差降低了1.1-19.1 dB，计算复杂度降低了38%。此外，由于所提出的专家涌现和选择模块，该框架在信道外推性能上显著优于其对应的混合专家（mix-of-expert）模型。
英文摘要
Acquiring channel state information (CSI) with manageable overhead has been essential to provide high-performance communication services, which is extremely challenging in the emerging sixth generation (6G) mobile network. Channel extrapolation has been proposed to infer complete CSI using a small portion of known CSI, its performance can be dramatically enhanced by artificial intelligence (AI). However, AI-driven channel extrapolation suffers from poor generalization across scenarios and high computational complexity, which is common in the broad research of AI and large language models. Inspired by the modular function of human brain, we propose a configurable AI-driven framework to achieve generalizable and computational efficient channel extrapolation from a modular perspective. We propose a three-stage framework, consisting of experts emergent, experts construction and experts selection. This framework assumes that CSI correlations can be captured by a small number of specialized functional modules (experts) that are activated differently across scenarios. Such modularity emerges in the experts emergent stage via pre-training using CSI data covering comprehensive scenarios. The neurons with similar weight-space patterns are grouped as experts in the experts construction stage. A lightweight gating function is added to control the routing of experts and is fine-tuned for each scenario in the experts selection stage. Simulation results demonstrate that the proposed three-stage framework reduce the channel extrapolation error and computational complexities dramatically by $1.1-19.1$ db and $38$ \%, respectively. In addition, attributed to the proposed experts emergent and section modules, the proposed framework outperforms its counterpart mix-of-expert model dramatically in terms of channel extrapolation performance.
