A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback

TL;DR

This paper introduces MarkovNet, a deep CNN framework leveraging channel coherence and a Markovian model to enhance CSI feedback efficiency and accuracy in massive MIMO systems, reducing computational complexity compared to RNNs.

Contribution

The paper proposes a novel Markovian model-driven deep CNN framework for CSI feedback that improves accuracy and reduces complexity in massive MIMO systems.

Findings

MarkovNet outperforms RNN-based methods in accuracy.

Significant reduction in computational complexity.

Effective CSI reconstruction with feedback quantization.

Abstract

Forward channel state information (CSI) often plays a vital role in scheduling and capacity-approaching transmission optimization for massive multiple-input multiple-output (MIMO) communication systems. In frequency division duplex (FDD) massive MIMO systems, forwardlink CSI reconstruction at the transmitter relies critically on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent studies on the use of recurrent neural networks (RNNs) have demonstrated strong promises, though the cost of computation and memory remains high, for massive MIMO deployment. In this work, we exploit channel coherence in time to substantially improve the feedback efficiency. Using a Markovian model, we develop a deep convolutional neural network (CNN)-based framework MarkovNet to differentially encode forward CSI in time to effectively improve reconstruction accuracy. Furthermore, we explore important physical insights, including spherical normalization of input data and convolutional layers for feedback compression. We demonstrate substantial performance improvement and complexity reduction over the RNN-based work by our proposed MarkovNet to recover forward CSI estimates accurately. We explore additional practical consideration in feedback quantization, and show that MarkovNet outperforms RNN-based CSI estimation networks at a fraction of the computational cost.