Bayesian Learning-Based Doubly-Selective Sparse Channel Estimation for Millimeter Wave Hybrid MIMO-FBMC-OQAM Systems

We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF) circuitry during transmission of zero symbols. Subsequently, Bayesian learning (BL) technique proposed sparse estimation, which relies on multiple measurement vectors combined with selective subcarrier grouping enhanced estimation. Additionally, an online BL based Kalman (OBL-KF) designed tracking in doubly-selective mmH-MFO Then Cramér-Rao lower bounds (BCRLBs) are derived characterizing performance frequency-selective techniques. Finally, limited feedback algorithm relying beamspace estimates precoder/combiner design. The accuracy our analytical results confirmed by simulation results.