Iterative Variational Bayesian Inference Based Channel Estimation for TWR mmWave Systems

Abstract

Millimeter wave (mmWave) with two way relaying (TWR) is an emerging paradigm towards cellular fifth/sixth generation (5G/6G) technology that can support various data hungry applications with improve coverage, network throughput, and reliability. The foreseen potential of mmWave TWR system can be further alleviated by using large-scale multiple input multiple output (MIMO) architecture. However, high power consumption, hardware complexity and cost of fully digital large-scale MIMO architecture restricts its application. As a countermeasure, hybrid precoding structures with reduced RF chains are utilized. Therefore, the amalgamation of TWR MIMO mmWave system with hybrid precoding structure demands an accurate channel state information (CSI) to avail the maximum gain of two way MIMO communications. Furthermore, the inherent self-interference in TWR systems in collusion with sparse mmWave channels imposes severe challenges in acquiring accurate CSI. The challenge is further aggravated in case of frequency-selective mmWave channel. To solve this problem, we propose a novel iterative variational Bayesian inference (IVBI)-based channel estimation (CE) scheme in the time domain for TWR system with reduced RF chain. In the proposed method, we follow the alternative minimization method involving variational Bayesian inference (VBI) to estimate all the channels of mmWave TWR system. The performance of the proposed algorithm are evaluated over both flat fading and frequency-selective channel. Extensive simulation results for symmetric and non-symmetric MIMO structure validate the proposed algorithm. The Bayesian Cramer-Rao bound (BCRB) of the proposed estimator is also derived. This novel scheme converges fast and achieves significant improvement in terms of normalized mean square error (NMSE) and bit error rate (BER) as compared to state-of-art.