Quantized channel state information (CSI) plays a critical role in precoding design which helps reap the merits of multiple-input multiple-output (MIMO) technology. In order to reduce the overhead of CSI feedback, we propose a deep learning based CSI quantization method by developing a joint convolutional residual network (JCResNet) which benefits MIMO channel feature extraction and recovery from the perspective of bit-level quantization performance. Experiments show that our proposed method substantially improves the performance.
@article{arxiv.1909.10730,
title = {Bit-level Optimized Neural Network for Multi-antenna Channel Quantization},
author = {Chao Lu and Wei Xu and Shi Jin and Kezhi Wang},
journal= {arXiv preprint arXiv:1909.10730},
year = {2019}
}