A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA with SWIPT

Jingci Luo; Jie  Tang; Daniel So; Gaojie  Chen; Kanapathippillai Cumanan; Jonathon  Chambers

A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA with SWIPT

Jingci Luo, Jie Tang, Daniel So, Gaojie Chen, Kanapathippillai Cumanan, Jonathon Chambers

Electronic Engineering

Research output: Contribution to journal › Article › peer-review

Original language	English
Number of pages	9
Journal	IEEE Access
Publication status	Accepted/In press - 22 Jan 2019

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This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

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This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.
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Cite this

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title = "A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA with SWIPT",

author = "Jingci Luo and Jie Tang and Daniel So and Gaojie Chen and Kanapathippillai Cumanan and Jonathon Chambers",

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AU - Luo, Jingci

AU - Tang, Jie

AU - So, Daniel

AU - Chen, Gaojie

AU - Cumanan, Kanapathippillai

AU - Chambers, Jonathon

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

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Y1 - 2019/1/22

M3 - Article

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JO - IEEE Access

JF - IEEE Access

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