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

IEEE Access

Volumn 7, Issue , 2019, Pages 17450-17460

  Luo, Jingci ^a   Tang, Jie ^a,b   So, Daniel K C ^c   Chen, Gaojie ^d   Cumanan, Kanapathippillai ^e   Chambers, Jonathon A ^d  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b SOUTHEAST UNIVERSITY   (China)

^c UNIVERSITY OF MANCHESTER   (United Kingdom)

^d UNIVERSITY OF LEICESTER   (United Kingdom)

^e UNIVERSITY OF YORK   (United Kingdom)

Author keywords

machine learning; Non orthogonal multiple access (NOMA); simultaneous wireless information and power transfer (SWIPT)

Indexed keywords

ELECTRIC POWER UTILIZATION; ENERGY EFFICIENCY; ENERGY TRANSFER; FREQUENCY DIVISION MULTIPLE ACCESS; INTEGER PROGRAMMING; ITERATIVE METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS; QUALITY OF SERVICE; WIRELESS NETWORKS;

DEEP BELIEF NETWORK (DBN); DOWNLINK RESOURCE ALLOCATION; INFORMATION AND POWER TRANSFERS; LEARNING-BASED APPROACH; MIXED INTEGER PROGRAMMING; MULTIPLE ACCESS; OPTIMIZATION PROBLEMS; ORTHOGONAL FREQUENCY DIVISION MULTIPLE ACCESS;

DEEP LEARNING;

EID: 85061777363     PISSN: None     EISSN: 21693536     Source Type: Journal    
DOI: 10.1109/ACCESS.2019.2895201     Document Type: Article

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