A Deep Learning-Based Detector for IM-MIMO-OFDM

Deep learning (DL) is playing an increasingly important role in the design of next-generation communication systems. In this paper, we apply DL algorithms to enhance signal detection and performance of multiple-input-multiple-output (MIMO) based orthogonal frequency-division multiplexing (OFDM) systems with index modulation (IM). The proposed detector termed DLIM is used as fully connected layers of a deep neural network (DNN) and adopted to achieve minimum bit error rates in IM-MIMO-OFDM over Rayleigh wireless channels. To show the enhancement of the proposed algorithm, the DL model is trained initially offline using data generated from simulation based on common statistical wireless channel models. DLIM is then adopted to recover the online transmitted data. Simulation results confirm that the proposed DLIM can detect the transmitted symbols with a performance comparable to near-optimal BER in a shorter runtime than required by the existing classical detectors.