CARNet: Channel-Adaptive Receiver Network for Robust NextG Communications
2026-08-03 • Information Theory
Information TheoryMachine Learning
AI summaryⓘ
The authors address the problem that neural receivers, which help in detecting signals in communication systems, often work well only under specific conditions and struggle when conditions change. They propose CARNet, a new kind of neural receiver that uses several specialized expert networks combined with a smart system to choose the best expert based on the current channel situation. This system learns to understand features of the channel and picks the right expert to detect signals accurately. Their tests show CARNet works better than existing methods across different communication scenarios.
neural receiverschannel conditionsmixture-of-experts (MoE)ResNetrouting mechanismrepresentation learningsignal detectionlatent embeddingNextG communicationslink-level simulation
Authors
Chao Jiang, Zhuo Xu, Yongli Yan
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.