.png){kind=icon}
返回
Joint Sparse Autoencoder Based Massive MIMO CSI Feedback
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2023.3250716.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In frequency division duplex (FDD) based massive multiple-input multiple-output (MIMO) systems, the channel state information (CSI) feedback overhead could degrade spectrum and energy efficiency. Many works have made great progress in efficient feedback. However, previous deep learning (DL)-based CSI feedback schemes considered downlink CSI only, or exploited uplink CSI in a simple way. In this letter, we propose a neural network to compress and accurately recover downlink CSI, based on FDD angle and delay reciprocity between bi-directional channels. We design a joint sparse autoencoder to learn sparse transform for compression, and introduce auxiliary uplink CSI in an explainable approach. Numerical results demonstrate that our structure can improve the reconstruction quality compared with downlink-only structure.
Keyword:
Uplink
Downlink
Transforms
Minimization
Sparse matrices
Neural networks
Massive MIMO
CSI feedback
deep learning
massive MIMO
partial reciprocity
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
Compressive Sampled CSI Feedback Method Based on Deep Learning for FDD Massive MIMO SystemsFDD大规模MIMO系统中基于深度学习的压缩采样CSI反馈方法
OAAI
.png){kind=icon}
OAAI分享收藏Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis基于卷积神经网络的大规模MIMO CSI反馈多速率压缩感知: 设计、仿真与分析OAAI
OAAI分享收藏分享收藏An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting一种用于低速率大规模MIMO CSI报告的高效深度学习框架OAAI
OAAI分享收藏Learnable Sparse Transformation-Based Massive MIMO CSI Recovery Network基于可学习稀疏变换的大规模MIMO CSI恢复网络
PREAI
分享收藏
查看更多内容