Transmit antenna Sselection for full-duplex spatial modulation based on machine learning
Journal Publication ResearchOnline@JCUAbstract
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.
Journal
IEEE Transactions on Vehicular Technology
Publication Name
IEEE Transactions on Vehicular Technology
Volume
70
ISBN/ISSN
1939-9359
Edition
N/A
Issue
10
Pages Count
14
Location
N/A
Publisher
Institute of Electrical and Electronics Engineers
Publisher Url
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Publisher Location
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Publish Date
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Url
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Date
N/A
EISSN
N/A
DOI
10.1109/TVT.2021.3111043