Machine Learning-Based Beamforming in Two-User MISO Interference Channels 


Vol. 44,  No. 3, pp. 461-469, Mar.  2019
10.7840/kics.2019.44.3.461


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  Abstract

As the demand for data rate increases, interference management becomes more important especially in small cell environment of emerging wireless communication systems. In this paper, we investigate the machine learning-based beamforming design in two-user MISO interference channels. To see the possibilities of machine learning in beamforming design, we consider simple beamforming, where each user chooses one between two popular beamforming schemes, which are the maximum ratio transmission (MRT) beamforming and the zero-forcing (ZF) beamforming. We first propose a machine learning structure that takes transmit power and channel vectors as inputs and then recommends two users’ choices between MRT and ZF as an output. The numerical results show that our proposed machine learning-based beamforming design well finds the best beamforming combination and achieves the sum-rate more than 99.9% of the best beamforming combination.

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  Cite this article

[IEEE Style]

H. J. Kwon, J. H. Lee, W. Choi, "Machine Learning-Based Beamforming in Two-User MISO Interference Channels," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 3, pp. 461-469, 2019. DOI: 10.7840/kics.2019.44.3.461.

[ACM Style]

Hyung Jun Kwon, Jung Hoon Lee, and Wan Choi. 2019. Machine Learning-Based Beamforming in Two-User MISO Interference Channels. The Journal of Korean Institute of Communications and Information Sciences, 44, 3, (2019), 461-469. DOI: 10.7840/kics.2019.44.3.461.

[KICS Style]

Hyung Jun Kwon, Jung Hoon Lee, Wan Choi, "Machine Learning-Based Beamforming in Two-User MISO Interference Channels," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 3, pp. 461-469, 3. 2019. (https://doi.org/10.7840/kics.2019.44.3.461)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880