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

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Document pages: 13 pages

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

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