Transmit antenna Sselection for full-duplex spatial modulation based on machine learning

Liu, Haoran, Xiao, Yue, Yang, Ping, Fu, Jialiang, Li, Shaoqian, and Xiang, Wei (2021) Transmit antenna Sselection for full-duplex spatial modulation based on machine learning. IEEE Transactions on Vehicular Technology, 70 (10). pp. 10695-10708.

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View at Publisher Website: https://doi.org/10.1109/TVT.2021.3111043
 
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Abstract

In this paper, we first derive the channel capacity of the full-duplex spatial modulation (FD-SM) system and its upper and lower bounds. Furthermore, different from the traditional optimization-driven decision, we use the data-driven prediction method to solve the transmit antenna selection (TAS) problem in the FD-SM system. Specifically, two novel TAS methods based on the support vector machine (SVM) and deep neural network (DNN) are proposed for reducing the effect of residual self-interference (RSI) on the FD-SM system performance. In our design, we propose a novel feature extraction method based on the principal component analysis (PCA) to help the proposed classifiers improve training. Our simulation results show that our data-driven TAS schemes can approach the optimal performance achieved by exhaustive search while significantly reducing complexity.

Item ID: 70066
Item Type: Article (Research - C1)
ISSN: 1939-9359
Keywords: Full-duplex spatial modulation (FD-SM), machine learning (ML), transmit antenna selection (TAS)
Copyright Information: © 2021 Institute of Electrical and Electronics Engineers.
Date Deposited: 19 Apr 2022 03:53
Downloads: Total: 3
Last 12 Months: 2
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