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Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems

1
Telecommunication and Image Technology Laboratories, Sharp Corporation, Chiba 261-8520, Japan
2
Computational Science and Engineering, Yonsei University, Seoul 03722, Korea
3
School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(5), 471; https://doi.org/10.3390/e21050471
Received: 4 April 2019 / Revised: 1 May 2019 / Accepted: 3 May 2019 / Published: 6 May 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

In this paper, a support vector machine (SVM) technique has been applied to an antenna allocation system with multiple antennas in multiuser downlink communications. Here, only the channel magnitude information is available at the transmitter. Thus, a subset of transmit antennas that can reduce multiuser interference is selected based on such partial channel state information to support multiple users. For training, we generate the feature vectors by fully utilizing the characteristics of the interference-limited setup in the multiuser downlink system and determine the corresponding class label by evaluating a key performance indicator, i.e., sum rate in multiuser communications. Using test channels, we evaluate the performance of our antenna allocation system invoking the SVM-based allocation and optimization-based allocation, in terms of sum-rate performance and computational complexity. Rigorous testing allowed for a comparison of a SVM algorithm design between one-vs-one (OVO) and one-vs-all (OVA) strategies and a kernel function: (i) OVA is preferable to OVO since OVA can achieve almost the same sum rate as OVO with significantly reduced computational complexity, (ii) a Gaussian function is a good choice as the kernel function for the SVM, and (iii) the variance (kernel scale) and penalty parameter (box constraint) of an SVM kernel function are determined by 21.56 and 7.67, respectively. Further simulation results revealed that the designed SVM-based approach can remarkably reduce the time complexity compared to a traditional optimization-based approach, at the cost of marginal sum rate degradation. Our proposed framework offers some important insights for intelligently combining machine learning techniques and multiuser wireless communications. View Full-Text
Keywords: antenna allocation systems; multiuser communication systems; multiclass classification; supervised machine learning; support vector machine antenna allocation systems; multiuser communication systems; multiclass classification; supervised machine learning; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lin, H.; Shin, W.-Y.; Joung, J. Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems. Entropy 2019, 21, 471.

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