Improving Wideband Massive MIMO Channel Estimation With UAV State-Space Information

Abstract

In this paper, we focus on the estimation of massive Multiple-Input Multiple-Output (mMIMO) channels in high-frequency point-to-point wireless communications between a terrestrial multi-antenna base station, realizing fully digital beamforming, and a multi-antenna Unmanned Aerial Vehicle (UA V), capable of only analog combining via a single reception Radio-Frequency (RF) chain. A wideband channel model, incorporating selectivity which is usually referred to as beam squint and is particularly relevant in high frequency bands, such as millimeter waves and terahertz, is considered. To account for the UA V’s limited capability to collect long training sequences, due to its single-RF reception architecture, and the beam-squint effect at both communication ends, we present a novel channel estimation approach that exploits information provided by the control module of the UA V , namely its state-space vector. The proposed approach comprises a hybrid parametric and non-parametric estimation op- timization framework that is efficiently solved through an iterative algorithm using the alternating direction method of multipliers. Our extensive performance investigations, including comparisons with benchmark schemes, showcase that considerable gains can be achieved if state-space information from the UA V’s control module is appropriately incorporated in the channel estimation process.