In frequency division duplexing (FDD) mode, it is necessary to send the channel state information (CSI) from user equipment to base station. The downlink CSI is essential for the massive multiple-input multiple-output (MIMO) system to acquire the potential gain. Recently, deep learning is widely adopted to massive MIMO CSI feedback task and proved to be effective compared with traditional compressed sensing methods. In this paper, a novel network named ACRNet is designed to boost the feedback performance with network aggregation and parametric RuLU activation. Moreover, valid approach to expand the network architecture in exchange of better performance is first discussed in CSI feedback task. Experiments show that ACRNet outperforms loads of previous state-of-the-art feedback networks without any extra information.
@article{arxiv.2101.06618,
title = {Aggregated Network for Massive MIMO CSI Feedback},
author = {Zhilin Lu and Hongyi He and Zhengyang Duan and Jintao Wang and Jian Song},
journal= {arXiv preprint arXiv:2101.06618},
year = {2021}
}
Comments
This version is only a draft of the final paper `Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System`, which has been uploaded as arXiv:2105.00354. This incomplete version has some performance error, therefore it should be withdrawn