@inproceedings{a91a4ac2cd7a44829b7b5699a6f2607d,
title = "Deep Neural Network Based Parallel Signal Detection in SM-OFDM System",
abstract = "A novel deep neural network based parallel signal detection (DNN-PSD) is proposed for the spatial modulation based orthogonal frequency division multiplexing (SM-OFDM) system. With the purpose to reduce the complexity of the conventional DNN, a uniform small-scale DNN with fewer parameters and less training time is exploited to detect the signals for each subcarrier parallelly. Apart from maximum likelihood (ML) and maximal ratio combining (MRC) detection schemes, the detailed DNN-PSD algorithm and its complexity analysis are presented. Simulation results confirm that the bit error rate (BER) performance of the proposed DNN-PSD is far superior to the MRC detection and similar to the optimal ML detection but with much lower complexity under different scenarios. It has more robustness and achieves a finer compromise between BER performance and complexity.",
keywords = "Deep neural network (DNN), bit error rate (BER), signal detection, spatial modulation based orthogonal frequency division multiplexing (SM-OFDM)",
author = "Jinmei Zhang and Zhiquan Bai and Kaiyue Yang and Abeer Mohamed and Kyungsup Kwak and Xinhong Hao",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022 ; Conference date: 05-07-2022 Through 08-07-2022",
year = "2022",
doi = "10.1109/ICUFN55119.2022.9829700",
language = "English",
series = "International Conference on Ubiquitous and Future Networks, ICUFN",
publisher = "IEEE Computer Society",
pages = "125--129",
booktitle = "ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks",
address = "United States",
}