Bit-level Optimized Neural Network for Multi-antenna Channel Quantization
arXiv:1909.10730
Abstract
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.
References in corpus (6)
- LSTM: A Search Space Odyssey
- Xception: Deep Learning with Depthwise Separable Convolutions
- Lossy Image Compression with Compressive Autoencoders
- Deep Learning Based MIMO Communications
- Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems
- An Introduction to Deep Learning for the Physical Layer