Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex networks. However, the huge number of antennas poses a challenge to the conventional CSI feedback reduction methods and leads to excessive feedback overhead. In this letter, we develop a real-time CSI feedback architecture, called CsiNetlong short-term memory (LSTM), by extending a novel deep learning (DL)-based CSI sensing and recovery network. CsiNet-LSTM considerably enhances recovery quality and improves tradeoff between compression ratio (CR) and complexity by directly learning spatial structures combined with time correlation from training samples of time-varying massive MIMO channels. Simulation results demonstrate that CsiNet-LSTM outperforms existing compressive sensing-based and DL-based methods and is remarkably robust to CR reduction.
To remedy this defect, a CsiNetlong short-term memory (CsiNet-LSTM) has been proposed in REF to enhance recovery quality of CSI by learning spatial structures and time correlation of time-varying massive MIMO channels.
Deep Learning-Based CSI Feedback Approach for Time-Varying Massive MIMO Channels
IEEE Wireless Communications Letters
IEEE Wireless Communications Letters
Computer Science Mathematics