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arXiv · 2008.07112

AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems

Yuyao Sun·Wei Xu·Lisheng Fan·Geoffrey Ye Li·George K. Karagiannidis
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Abstract

Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith. By considering the noisy CSI due to imperfect channel estimation, we propose a novel deep neural network architecture, namely AnciNet, to conduct the CSI feedback with limited bandwidth. AnciNet extracts noise-free features from the noisy CSI samples to achieve effective CSI compression for the feedback. Experimental results verify that the proposed AnciNet approach outperforms the existing techniques under various conditions.

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Yuyao Sun, Wei Xu, Lisheng Fan, Geoffrey Ye Li, George K. Karagiannidis. 2020-08-17. AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems. https://arxiv.org/abs/2008.07112

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