Learning-Based MIMO Channel Estimation under Spectrum Efficient Pilot Allocation and Feedback

Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks networks. Precoding in Massive MIMO transmission requires accurate downlink channel state information (CSI). Many recent works have effectively applied deep learning (DL) to jointly train UE-side compression networks for delay domain CSI and a BS-side decoding scheme. Vitally, these works assume that the full delay domain CSI is available at the UE, but in reality, the UE must estimate the delay domain based on a limited number of frequency domain pilots. In this work, we propose a linear pilot-to-delay (P2D) estimator that transforms sparse frequency pilots to the truncated delay CSI. We show that the P2D estimator is accurate under frequency downsampling, and we demonstrate that the P2D estimate can be effectively utilized with existing autoencoder-based CSI estimation networks. In addition to accounting for pilot-based estimates of downlink CSI, we apply unrolled optimization networks to emulate iterative solutions to compressed sensing (CS), and we demonstrate better estimation performance than prior autoencoder-based DL networks. Finally, we investigate the efficacy of trainable CS networks for in a differential encoding network for time-varying CSI estimation, and we propose a new network, MarkovNet-ISTA-ENet, comprised of both a CS network for initial CSI estimation and multiple autoencoders to estimate the error terms. We demonstrate that this heterogeneous network has better asymptotic performance than networks comprised of only one type of network.

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

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.