发表机构
Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对神经接收机泛化能力不足的问题，提出基于混合专家框架的CARNet，通过专家网络与路由机制实现不同信道下的鲁棒信号检测，链路级仿真验证其性能优异。
AI 中文摘要
神经接收机被视为下一代（NextG）通信中极具前景的范式，但由于依赖针对特定信道条件优化的静态网络，其在不同场景下的泛化能力仍是重大挑战。为解决该问题，本文提出一种基于混合专家（MoE）框架的新型信道自适应神经接收机网络（CARNet）。该架构采用多个专家网络与高效路由机制，以实现各类场景下的信号检测；专家网络由堆叠的ResNet模块构建，专门针对特定信道条件下的鲁棒信号检测，而路由机制包含轻量型表示学习模块，将粗略信道估计投影为低维潜在嵌入，该嵌入表征与任务相关的信道条件，为精准选择专家提供有效指导。链路级仿真实验表明，所提CARNet在各类信道条件下均实现了优异性能。
英文摘要
Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications. However, due to the reliance on a static network optimized for specific channel conditions, their generalization capability across diverse scenarios remains a significant challenge. To address this issue, this paper proposes a novel channel-adaptive neural receiver network (CARNet) based on the mixture-of-experts (MoE) framework. The proposed architecture employs multiple expert networks together with an efficient routing mechanism to enable signal detection in various scenarios. The experts are constructed via stacked ResNet blocks and specialize in robust signal detection within specific channel conditions, while the routing mechanism incorporates a lightweight representation learning module, which projects the coarse channel estimate into a low-dimensional latent embedding. The learned embedding characterizes task-relevant channel conditions and provides efficient guidance for accurate expert selection. Link-level simulation experiments demonstrate that the proposed CARNet achieves superior performance across diverse channel conditions.
Comments5 pages, 3 figures
