Sparse Estimation in mmWave MIMO-OFDM Joint Radar and Communication (JRC) Systems

Abstract

This paper considers a joint radar and communication (JRC) system towards radar cross-section (RCS) parameter and channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithms are based on the hybrid mmWave MIMO architecture. First, the orthogonal matching pursuit (OMP)-based framework is conceived for radar target parameter estimation. Next, a novel multiple measurement vector (MMV)-based Bayesian learning (MBL) algorithm is proposed for mmWave MIMO channel estimation in JRC systems. Subsequently, these quantities are employed at the dual-functional radar-communication (DFRC) base station (BS) and at the user equipment (UE) toward successful data transmission and detection, respectively. The proposed techniques exploit the sparsity inherent in the radar scattering environment and the simultaneous sparsity of the wireless channel across all the subcarriers for improved performance. Numerical results demonstrate the efficacy of the proposed techniques and the improved performance in comparison to existing sparse recovery techniques as well as the conventional non-sparse parameter estimation algorithms.