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

by   Mason del Rosario, et al.

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.


page 1

page 2

page 3

page 4

page 6

page 7


Training Enhancement of Deep Learning Models for Massive MIMO CSI Feedback with Small Datasets

Accurate downlink channel state information (CSI) is vital to achieving ...

Coordinated Pilot Transmissions for Detecting the Signal Sparsity Level in a Massive IoT Network under Rayleigh Fading

Grant-free protocols exploiting compressed sensing (CS) multi-user detec...

Exploiting Partial FDD Reciprocity for Beam Based Pilot Precoding and CSI Feedback in Deep Learning

Massive MIMO systems can achieve high spectrum and energy efficiency in ...

Deep Learning for 1-Bit Compressed Sensing-based Superimposed CSI Feedback

In frequency-division duplexing (FDD) massive multiple-input multiple-ou...

Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction

A major obstacle for widespread deployment of frequency division duplex ...

Beyond 5G: Leveraging Cell Free TDD Massive MIMO using Cascaded Deep learning

Cell Free Massive MIMO is a solution for improving the spectral efficien...

2D Beam Domain Statistical CSI Estimation for Massive MIMO Uplink

In this paper, we investigate the beam domain statistical channel state ...