Abstract
Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has been introduced as a new transmission method for 5G and beyond communications. On the other hand, deep neural networks (DNNs) have started to be effective in many fields, including wireless communication, due to their advantages such as low complexity, high performance, low processing times, etc. Since the optimum decoding for MIMO-OFDM-IM grows exponentially with higher modulation orders and the number of transmit and receive antennas, DNN-based decoding will be a potential choice for the next generation receiver architecture. In this work, a novel fully connected DNN based MIMO-OFDM-IM to jointly detect the transmitted symbols from each antenna is proposed and its performance is analyzed. As seen from the simulation results, the proposed DNN-based detector shows a close bit error rate performance to optimum detection with lower computational complexity.
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