CARNet通过动态选专家，让神经接收机在不同信道下都稳定高效。
CARNet: Channel-Adaptive Receiver Network for Robust NextG Communications
- 用专家混合框架，按信道条件动态选择最适合的信号检测模块。
- 在多种信道条件下性能优于传统方法，提升接收鲁棒性。
- 适合研究下一代通信中自适应接收机的工程师和学者。
神经接收机被视为下一代（NextG）通信的有前景范式。然而，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面临重大挑战。为此，本文提出一种基于专家混合（MoE）框架的通道自适应神经接收机（CARNet）。该架构采用多个专家网络与高效的路由机制，实现对各类信道场景的信号检测。专家由堆叠的ResNet块构建，专注于特定信道条件下的鲁棒信号检测；路由机制包含轻量级表征学习模块，将粗略的信道估计映射为低维潜在嵌入。该嵌入刻画任务相关的信道特性，为精确的专家选择提供高效引导。链路级仿真实验表明，所提CARNet在多样信道条件下均表现出优越性能。
原文摘要 · Abstract (English)
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.
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