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Improved MIMO Signal Detection Based on DNN in MIMO-OFDM System

Jae-Hyun Ro1, Jong-Gyu Ha2, Woon-Sang Lee2, Young-Hwan You3, Hyoung-Kyu Song2,*
1 Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
2 Department of Information and Communication Engineering, Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
3 Department of Computer Engineering, Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
* Corresponding Author: Hyoung-Kyu Song. Email:

Computers, Materials & Continua 2022, 70(2), 3625-3636. https://doi.org/10.32604/cmc.2022.020596

Received 30 May 2021; Accepted 12 July 2021; Issue published 27 September 2021

Abstract

This paper proposes the multiple-input multiple-output (MIMO) detection scheme by using the deep neural network (DNN) based ensemble machine learning for higher error performance in wireless communication systems. For the MIMO detection based on the ensemble machine learning, all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models. In the offline learning, the received signals and channel coefficients are set to input data, and the labels which correspond to transmit symbols are set to output data. In the online learning, the perfectly learned models are used for signal detection where the models have fixed bias and weights. For performance improvement, the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver. The simulation results show that the proposed scheme has improved symbol error rate (SER) performance without additional receive antennas.

Keywords

MIMO; DNN; ensemble machine learning; ML

Cite This Article

J. Ro, J. Ha, W. Lee, Y. You and H. Song, "Improved mimo signal detection based on dnn in mimo-ofdm system," Computers, Materials & Continua, vol. 70, no.2, pp. 3625–3636, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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