用低秩适配器实现神经接收机的多码率兼容，大幅降低资源开销。
LOREN: Low Rank-Based Code-Rate Adaptation in Neural Receivers
- 在卷积层中嵌入轻量级低秩适配器，共享主网络参数
- 支持三种码率时硅面积减少超65%，功耗降低最多15%
- 适用于需要低功耗部署的5G无线通信系统
基于神经网络的接收机相比传统接收机展现出更优的系统级性能，但其实际应用受限于高内存与高功耗，因需为每种码率单独存储权重。为此，本文提出LOREN——一种基于低秩的码率自适应神经接收机。该方法在卷积层中引入轻量级低秩适配器（LOREN适配器），冻结共享的基础网络，仅对每种码率训练小型适配器。在3GPP CDL信道上的端到端训练确保了在真实无线环境中的鲁棒性。实验表明，LOREN在性能上可媲美甚至超越完全重训练的基础神经接收机。在22nm工艺下的硬件实现显示，支持三种码率时，硅面积节省超过65%，功耗最高降低15%。
原文摘要 · Abstract (English)
Neural network based receivers have recently demonstrated superior system-level performance compared to traditional receivers. However, their practicality is limited by high memory and power requirements, as separate weight sets must be stored for each code rate. To address this challenge, we propose LOREN, a Low Rank-Based Code-Rate Adaptation Neural Receiver that achieves adaptability with minimal overhead. LOREN integrates lightweight low rank adaptation adapters (LOREN adapters) into convolutional layers, freezing a shared base network while training only small adapters per code rate. An end-to-end training framework over 3GPP CDL channels ensures robustness across realistic wireless environments. LOREN achieves comparable or superior performance relative to fully retrained base neural receivers. The hardware implementation of LOREN in 22nm technology shows more than 65% savings in silicon area and up to 15% power reduction when supporting three code rates.
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