Deep learning-based detector for downlink IM-NOMA systems Online publication date: Fri, 09-Aug-2024
by Issa Chihaoui; Mohamed Lassaad Ammari
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 46, No. 4, 2024
Abstract: This work proposes a deep learning (DL)-based detector for downlink index modulation non-orthogonal multiple access (IM-NOMA) systems, which we call DL-IM-NOMA. The proposed detector involves a multilayer fully connected deep neural network (DNN) block. The received signal is preprocessed using zero-forcing (ZF) and channel knowledge at the receiver before being fed into the DNN block. Architecture complexity and bit error rate (BER) performance of DL-IM-NOMA have been presented for several configurations, and compared to these of the suboptimal log-likelihood ratio (LLR) detector. Simulation results show that DL-IM-NOMA outperforms LLR detector for high signal-to-noise ratios (SNRs) with lower complexity. It has been shown that DL-IM-NOMA avoids BER floor occurred at strong users using LLR detector. In contrast, for low SNRs, DL-IM-NOMA causes an insignificant performance loss, especially for systems using large constellation size.
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