Sparse Channel Estimation in IRS-Assisted Massive MIMO Cognitive Radio Systems

This paper proposes novel Bayesian learning approaches for sparse channel estimation in a multi-user millimeter-wave massive multiple-input multiple-output underlay cognitive radio system. The intelligent reflecting surfaces (IRS)-aided secondary network adopts a two-phase transmission protocol comprising of silent and estimation phases. During the silent phase, the secondary base station(SBS) captures primary network pilot transmissions to estimate the cascaded channel between primary users and the SBS. Next, the estimation phase considers two pilot design policies with an inherent estimation accuracy and spectral efficiency trade-off, for cascaded channel estimation with respect to the secondary users, IRS, and SBS. Further, the associated hybrid and marginalized Cram & eacute;r-Rao bounds are developed to benchmark the efficacy of proposed estimation schemes. Simulation results demonstrate the superior performance of the proposed approaches in comparison to existing compressed sensing methods such as orthogonal matching pursuit and subspace multi-user joint channel estimation.