 for channel estimation. The noise variance on is assumed to be 1. P. Singh and E. Sharma FBMC-ZF OFDM-ZF I-CSI P-CSI OFDM-MRC FBMC-MRC OFDM-ZF FBMC-ZF FBMC-LB Number of BS Antennas, L Normalized CFO Fig. 3.5 Uplink performance comparison of FBMC- and OFDM-based massive MIMO tech- nologies: (a) Uplink sum rate as a function of the number of BS antennas in the existence of imperfect and perfect knowledge of channel with power per user 2Pd = 10 dB and (b) SER versus normalized CFO with perfect knowledge of channel, power per user 2Pd = dB, L = 64, Lh = 2 and Bu = 1 for 1 With this assumption, the transmit power per user can be interpreted as normalized transmit SNR, and hence it is dimensionless. There are U = 8 single antenna users in the cell. Figure 3.5a shows that the uplink sum rates achieved by the ZF and MRC receivers agree to their respective lower bounds in the existence of imperfect and perfect channel knowledge. It can be seen that that the uplink sum rates of the FBMC-based MU massive MIMO system coincide with their OFDM counterparts. It is also observed that the ZF receiver outperforms the MRC receiver, especially in the high SNR regime. However, in comparison to the latter, the former costs more in terms of computational complexity. The results in Fig. 3.5a are plotted in the existence of perfect synchronization. However, in the existence of synchronization errors due to practical impairments such as CFO, as shown in Fig. 3.5b, the symbol error rate (SER) of an OFDM-based massive MIMO system degrades severely in comparison to its FBMC counterpart. This happens because the OFDM waveform experiences significant ICI due to the sinc-shaped frequency localization of the rectangular pulse shaping, whereas FBMC waveform, due to associated well time- frequency localized pulse-shaping filter, experience significantly lower ICI, which makes FBMC waveform robust against practical impairments. Conclusions and Future Directions This chapter designed and analysed FBMC-OQAM-based MIMO and massive MIMO systems. For i