gated a blind CSI estimation technique for SISO-FBMC systems by utilising the cyclostationarity induced by the overlapping nature of pulse-shaping filters. The proposed technique therein demands a large number of data symbols to provide a reliable CSI estimate, and its performance deteriorates with an increase in the pulse-shaping filters' length. Savaux et al. in [39] designed blind equalization for FBMC-based SISO waveform by utilizing the concept of constant norm. The technique therein is restricted for square QAM constellations, and similar to [38], its convergence needs a large data length. In presence of the above observations, semi-blind schemes, which significantly improve CSI estimation accuracy by utilising both statistical characteristics of the underlying system and a small training overhead, present a viable alternative to both pilot-based and blind CSI estimation methods. Authors in [40] conceived a semi-blind CSI estimation scheme for FBMC-based SISO systems, wherein the CSI magnitude and phase are estimated blindly by exploiting the subcarrier power and the spatial-sign covariance matrix, respectively, and pilot symbols are utilised to mitigate the sign ambiguity arising from the blind CSI estimation algorithm. The MSE of the blind estimation in [40] is sensitive to frame length, and therefore, its performance deteriorates as the frame length decreases [41]. Kofidis et al. in [42] proposed a tensor-based scheme for semi-blind CSI estimation and data detection for FBMC-based MIMO systems by using canonical polyadic decomposition (CPD). The results presented therein are restricted to a single-input multiple-output (SIMO)-FBMC system, because the CPD model is not always identifiable. Due to the effectiveness and improved performance of semi-blind schemes, this section develops a different semi-blind technique for CSI estimation in FBMC-based MIMO systems by exploiting both pilot and the statistical properties of data symbols. Contrary to the pilot-based CSI estimation schemes in [24, 26, 28- 31, 33, 