nology by exploiting the pilot symbols along with statistical properties of the information symbols. This results in a significant reduction in the mean square error (MSE) in comparison to its conventional pilot-based counterpart. 3.4.1 Review of Existing Works There has been a significant research progress in the area of channel estimation for MIMO-FBMC systems. Reference [28] extended the concept of IAM model- based CFR estimation for FBMC-aided MIMO systems and proposed least squares (LS) estimator. Rottenberg et al. in [29] investigated a linear minimum mean square error (MMSE) CFR estimation approach for the downlink of a distributed MIMO-FBMC technology. Reference [30] described a method for pilot sequence design for the IAM model-based CSI estimation by employing zero-correlation zone sequences in MIMO-FBMC systems. Javaudin et al. designed a scattered pilot-based CSI estimator for MIMO-FBMC systems in [31]. The authors in [32] analysed the performance of the IAM model-based MMSE and LS CSI estimations for MIMO-FBMC waveform in the existence of imperfect channel correlations. Reference [24] provided a comprehensive review on training-based approaches for CSI estimation in FBMC-based SISO and MIMO systems. References [16, 33-35] designed training-based time-domain CSI estimation algorithms for MIMO-FBMC systems over a high frequency selective channel. Lin et al. in [36] designed a pilot- based compressive sensing technique in the time domain for MIMO-FBMC CSI estimation by utilising the generalized approximate message passing algorithm. A key drawback of the above treatises is to employ only pilot symbols for CSI estimation, which leads to a decrease in the spectral efficiency. To avoid this shortcoming, the FBMC literature has developed blind CSI estimation schemes, which do not need pilot symbols for estimation [37, Chapter 11]. Blind schemes, 3 FBMC: A Waveform Candidate for Beyond 5G however, are usually computationally expansive and affected by poor convergence. For example, the authors in [38] investi