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.

翻译：基于神经网络的接收机相较于传统接收机，近期已展现出更优的系统级性能。然而，其实际应用受到高内存与高功耗需求的限制，因为每个码率都需要存储独立的权重集。为应对这一挑战，我们提出了LOREN，一种基于低秩的码率自适应神经接收机，能以极小的开销实现自适应。LOREN将轻量级的低秩自适应适配器集成到卷积层中，冻结共享的基础网络，仅针对每个码率训练小型适配器。在3GPP CDL信道上的端到端训练框架确保了其在真实无线环境中的鲁棒性。LOREN相较于完全重新训练的基础神经接收机，实现了相当甚至更优的性能。在22纳米工艺中对LOREN的硬件实现表明，在支持三种码率时，硅片面积节省超过65%，功耗降低高达15%。