AUTHOR=Bedoui Abla , Et-tolba Mohamed TITLE=A Deep Neural Network-Based Interference Mitigation for MIMO-FBMC/OQAM Systems JOURNAL=Frontiers in Communications and Networks VOLUME=2 YEAR=2021 URL=https://www.frontiersin.org/journals/communications-and-networks/articles/10.3389/frcmn.2021.728982 DOI=10.3389/frcmn.2021.728982 ISSN=2673-530X ABSTRACT=

Offset quadrature amplitude modulation-based filter bank multicarrier (FBMC/OQAM) is among the promising waveforms for future wireless communication systems. This is due to its flexible spectrum usage and high spectral efficiency compared with the conventional multicarrier schemes. However, with OQAM modulation, the FBMC/OQAM signals are not orthogonal in the imaginary field. This causes a significant intrinsic interference, which is an obstacle to apply multiple input multiple output (MIMO) technology with FBMC/OQAM. In this paper, we propose a deep neural network (DNN)-based approach to deal with the imaginary interference, and enable the application of MIMO technique with FBMC/OQAM. We show, by simulations, that the proposed approach provides good performance in terms of bit error rate (BER).