Deep Recurrent Neural Network Based Detector for OFDM With Index Modulation

Abstract

Index modulation (IM) leads to a decrease in power consumption and transmitter complexity compared to classical orthogonal frequency division multiplexing (OFDM) systems. The overall bit-error rate (BER) performance of the OFDM with IM (OFDM-IM) system is significantly influenced by the accuracy of index-bit detection. To take advantage of IM, in this paper, we propose a recurrent neural network-based signal detection scheme for OFDM-IM. In addition, we introduce a more effective long-short-term memory (LSTM)-based detection technique to improve the BER performance of the OFDM-IM system. The Adam optimization algorithm is utilized to reduce the total system loss. Before entering the network, the received signal and channel matrix are pre-processed based on domain knowledge to enhance the performance of the proposed system. At first, the model is trained in offline to minimize BER using the simulation dataset, and then the trained model is employed in the online phase to detect the OFDM-IM signal. We compare the performance of the proposed LSTM-based detector with traditional detectors and other deep learning (DL) detectors. The simulation outcomes show that our proposed detector outperforms conventional detectors and other DL detectors under perfect and imperfect channel conditions.

Keywords

• OFDM-index modulation
• long-short-term memory (LSTM)-IM
• BER
• channel state information
• deep learning IM