# Efficient Quantization-Aware Neural Receivers: Beyond Post-Training Quantization

arXiv: https://arxiv.org/abs/2509.13786 · HTML: https://mlsanity.com/paper/2509.13786/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Data-Free Quantization Through Weight Equalization and Bias Correction](https://mlsanity.com/paper/1906.04721/) (2019)
- [Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets](https://mlsanity.com/paper/1903.05662/) (2019)
- [LSQ+: Improving low-bit quantization through learnable offsets and better initialization](https://mlsanity.com/paper/2004.09576/) (2020)
- [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://mlsanity.com/paper/2004.09602/) (2020)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [Pruning vs Quantization: Which is Better?](https://mlsanity.com/paper/2307.02973/) (2023)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Effective Interplay between Sparsity and Quantization: From Theory to Practice](https://mlsanity.com/paper/2405.20935/) (2024)
- [Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory](https://mlsanity.com/paper/2203.06271/) (2022)
- [Efficient Deep Neural Receiver with Post-Training Quantization](https://mlsanity.com/paper/2508.06275/) (2025)
- [Industrial Viewpoints on RAN Technologies for 6G](https://mlsanity.com/paper/2508.08225/) (2025)

## Abstract (arXiv)

As wireless communication systems advance toward Sixth Generation (6G) Radio Access Networks (RAN), Deep Learning (DL)-based neural receivers are emerging as transformative solutions for Physical Layer (PHY) processing, delivering superior Block Error Rate (BLER) performance compared to traditional model-based approaches. Practical deployment on resource-constrained hardware, however, requires efficient quantization to reduce latency, energy, and memory without sacrificing reliability. In this paper, we extend Post-Training Quantization (PTQ) by focusing on Quantization-Aware Training (QAT), which incorporates low-precision simulation during training for robustness at ultra-low bitwidths. In particular, we develop a QAT methodology for a neural receiver architecture and benchmark it against a PTQ approach across diverse 3GPP Clustered Delay Line (CDL) channel profiles under both Line-of-Sight (LoS) and Non-LoS (NLoS) conditions, with user velocities up to 40 m/s. Results show that 4-bit and 8-bit QAT models achieve BLERs comparable to FP32 models at a 10% target BLER. Moreover, QAT models succeed in NLoS scenarios where PTQ models fail to reach the 10% BLER target, while also yielding an 8x compression. These results with respect to full-precision demonstrate that QAT is a key enabler of low-complexity and latency-constrained inference at the PHY layer, facilitating real-time processing in 6G edge devices.