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
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.
Topics & Concepts
Computer scienceChannel state informationMIMOOverhead (engineering)Bandwidth (computing)ImperfectFeedback loopControl theory (sociology)Channel (broadcasting)Artificial intelligenceTelecommunicationsWirelessComputer securityOperating systemPhilosophyControl (management)LinguisticsAdvanced MIMO Systems OptimizationMillimeter-Wave Propagation and ModelingAdvanced Wireless Communication Techniques