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).
Highlights
Future wireless communication systems are expected to support a variety of use cases that are categorized into three categories: enhanced mobile broadband, enhanced type communications, and ultra-reliable low-latency communications (URLLC) (Renfors et al, 2017)
To show the power of the proposed approach to interference mitigation in multiple input multiple output (MIMO)-FBMC/OQAM, we have evaluated its performance using computer simulations
We have proposed a nonlinear approach to interference mitigation for MIMO-FBMC/OQAM systems
Summary
Future wireless communication systems are expected to support a variety of use cases that are categorized into three categories: enhanced mobile broadband (eMBB), enhanced type communications (eHTC), and ultra-reliable low-latency communications (URLLC) (Renfors et al, 2017). We propose a deep neural network-based approach to interference mitigation to enable MIMO techniques for FBMC/OQAM systems. Our approach combines an adaptive neuro fuzzy inference system (ANFIS) and a deep neural network (DNN), which are mounted in cascade to detect the transmitted data symbols in the presence of the intrinsic interference This latter is caused by the overlapping pattern of FBMC/OQAM signals. In order to faithfully detect the transmitted data symbols, one has to deal with the uncertain channel behavior, the interference, and the noise In this context, we are inspired by the idea of adaptive neuro-fuzzy inference systems (ANFIS), and propose a deep learning approach to cancel the intrinsic interference. We present the proposed approach for interference cancellation in MIMO-FBMC/OQAM systems
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