Implementation of NOMA with Machine Learning in MISO Broadcast Channels 


Vol. 47,  No. 6, pp. 801-808, Jun.  2022
10.7840/kics.2022.47.6.801


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  Abstract

Non-orthogonal multiple access (NOMA) requires successive interference cancellation at the receiver, but the optimal decoding order is not easy to find especially when the transmitter has multiple antennas. In this paper, we use machine learning to implement NOMA in downlink multiple input single output broadcast channels when data rate for each user is fixed. Our machine learning model finds the optimal decoding order for given channel states.

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  Cite this article

[IEEE Style]

M. J. Kang and J. H. Lee, "Implementation of NOMA with Machine Learning in MISO Broadcast Channels," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 6, pp. 801-808, 2022. DOI: 10.7840/kics.2022.47.6.801.

[ACM Style]

Min Jeong Kang and Jung Hoon Lee. 2022. Implementation of NOMA with Machine Learning in MISO Broadcast Channels. The Journal of Korean Institute of Communications and Information Sciences, 47, 6, (2022), 801-808. DOI: 10.7840/kics.2022.47.6.801.

[KICS Style]

Min Jeong Kang and Jung Hoon Lee, "Implementation of NOMA with Machine Learning in MISO Broadcast Channels," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 6, pp. 801-808, 6. 2022. (https://doi.org/10.7840/kics.2022.47.6.801)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880