CARNet: Channel-Adaptive Receiver Network for Robust NextG Communications

2026-08-03

AI Summary

This work addresses the limited generalization of neural receivers under diverse channel conditions by proposing a Channel-Adaptive Neural Receiver, termed CARNet. Built upon a Mixture-of-Experts (MoE) architecture, CARNet employs task-oriented low-dimensional channel embeddings and a lightweight routing mechanism to dynamically select ResNet-based expert modules tailored to varying channel environments. By jointly optimizing channel representation learning and expert routing, CARNet substantially enhances adaptability to dynamic channels. Link-level simulations demonstrate that CARNet consistently outperforms existing neural receivers across multiple channel conditions, achieving more robust signal detection performance while maintaining a lightweight model structure.

Abstract

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.

Problem

• neural receivers
• generalization
• channel conditions
• NextG communications
• robustness

Innovation

• channel-adaptive
• mixture-of-experts
• neural receiver
• representation learning
• NextG communications