Deep Learning-Based Detector for OFDM-IM

• Published in: IEEE wireless communications letters Vol. 8; no. 4; pp. 1159 - 1162
• Main Authors: Luong, Thien Van, Ko, Youngwook, Vien, Ngo Anh, Nguyen, Duy H. N., Matthaiou, Michail
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Bit error rate; Complexity theory; Computer simulation; Deep learning; deep neural network; DeepIM; Detectors; index modulation; Indexes; Knowledge management; low-complexity detector; Machine learning; OFDM; OFDM-IM; Orthogonal Frequency Division Multiplexing; Signal detection; Signal processing; Simulation; Training
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2019.2909893

This letter presents the first attempt of exploiting deep learning (DL) in the signal detection of orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. Particularly, we propose a novel DL-based detector termed as DeepIM, which employs a deep neural network with fully connected layers to recover data bits in an OFDM-IM system. To enhance the performance of DeepIM, the received signal and channel vectors are pre-processed based on the domain knowledge before entering the network. Using datasets collected by simulations, DeepIM is first trained offline to minimize the bit error rate (BER) and then the trained model is deployed for the online signal detection of OFDM-IM. Simulation results show that DeepIM can achieve a near-optimal BER with a lower runtime than existing hand-crafted detectors.