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In this paper, we propose a deep learning-based signal detector calledDuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D)orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D-OFDM is a subcarrier index modulation scheme which conveys data bits via bothdual-mode 3D constellation symbols and indices of active subcarriers. Thus,this scheme obtains better error performance than the existing IM schemes whenusing the conventional maximum likelihood (ML) detector, which, however,suffers from high computational complexity, especially when the systemparameters increase. In order to address this fundamental issue, we propose theusage of a deep neural network (DNN) at the receiver to jointly and reliablydetect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fadingchannels in a data-driven manner. Simulation results demonstrate that ourproposed DNN detector achieves near-optimal performance at significantly lowerruntime complexity compared to the ML detector.

Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM