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

TL;DR

AnciNet is a deep learning model designed to efficiently compress and feedback noisy CSI in massive MIMO systems, reducing bandwidth requirements while maintaining accuracy.

Contribution

The paper introduces AnciNet, a novel neural network architecture that extracts noise-free features from noisy CSI for improved compression in massive MIMO feedback.

Findings

AnciNet outperforms existing CSI feedback methods.

Effective noise reduction from noisy CSI samples.

Improved feedback bandwidth efficiency.

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.