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arXiv · 1807.11673

Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels

Tianqi Wang·Chao-Kai Wen·Shi Jin·Geoffrey Ye Li
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Abstract

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 (FDD) networks. However, the huge number of antennas poses a challenge to conventional CSI feedback reduction methods and leads to excessive feedback overhead. In this article, we develop a real-time CSI feedback architecture, called CsiNet-long 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 trade-off 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 DLbased methods and is remarkably robust to CR reduction.

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Tianqi Wang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li. 2018-07-31. Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels. https://arxiv.org/abs/1807.11673

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