Joint Channel Estimation and Prediction for Massive MIMO with Frequency Hopping Sounding

TL;DR

This paper introduces a joint channel estimation and prediction method for massive MIMO systems with frequency hopping sounding, using a novel off-grid model and message passing algorithm to improve CSI accuracy and mitigate energy leakage.

Contribution

It proposes a new off-grid delay-angle-Doppler channel model and an adaptive message passing algorithm for joint estimation and prediction in massive MIMO with frequency hopping.

Findings

The proposed algorithm effectively reduces energy leakage.

It exploits common CSI across subbands for improved accuracy.

It outperforms existing methods in numerical simulations.

Abstract

In massive multiple-input multiple-output (MIMO) systems, the downlink transmission performance heavily relies on accurate channel state information (CSI). Constrained by the transmitted power, user equipment always transmits sounding reference signals (SRSs) to the base station through frequency hopping, which will be leveraged to estimate uplink CSI and subsequently predict downlink CSI. This paper aims to investigate joint channel estimation and prediction (JCEP) for massive MIMO with frequency hopping sounding (FHS). Specifically, we present a multiple-subband (MS) delay-angle-Doppler (DAD) domain channel model with off-grid basis to tackle the energy leakage problem. Furthermore, we formulate the JCEP problem with FHS as a multiple measurement vector (MMV) problem, facilitating the sharing of common CSI across different subbands. To solve this problem, we propose an efficient Off-Grid-MS hybrid message passing (HMP) algorithm under the constrained Bethe free energy (BFE) framework. Aiming to address the lack of prior CSI in practical scenarios, the proposed algorithm can adaptively learn the hyper-parameters of the channel by minimizing the corresponding terms in the BFE expression. To alleviate the complexity of channel hyper-parameter learning, we leverage the approximations of the off-grid matrices to simplify the off-grid hyper-parameter estimation. Numerical results illustrate that the proposed algorithm can effectively mitigate the energy leakage issue and exploit the common CSI across different subbands, acquiring more accurate CSI compared to state-of-the-art counterparts.