Efficient Deep Neural Receiver with Post-Training Quantization

Deep learning has recently garnered significant interest in wireless communications due to its superior performance compared to traditional model-based algorithms. Deep convolutional neural networks (CNNs) have demonstrated notable improvements in block error rate (BLER) under various channel models and mobility scenarios. However, the high computational complexity and resource demands of deep CNNs pose challenges for deployment in resource-constrained edge systems. The 3rd Generation Partnership Project (3GPP) Release 20 highlights the pivotal role of artificial intelligence (AI) integration in enabling advanced radio-access networks for 6G systems. The hard real-time processing demands of 5G and 6G require efficient techniques such as post-training quantization (PTQ), quantization-aware training (QAT), pruning, and hybrid approaches to meet latency requirements. In this paper, we focus on PTQ to reduce model complexity by lowering the bit-width of weights, thereby enhancing computational efficiency. Our analysis employs symmetric uniform quantization, applying both per-tensor and per-channel PTQ to a neural receiver achieving performance comparable to full-precision models. Specifically, 8-bit per-channel quantization maintains BLER performance with minimal degradation, while 4-bit quantization shows great promise but requires further optimization to achieve target BLER levels. These results highlight the potential of ultra-low bitwidth PTQ for efficient neural receiver deployment in 6G systems.