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Wireless and Satellite Systems. 14th EAI International Conference, WiSATS 2024, Harbin, China, August 23–25, 2024, Proceedings, Part II

Research Article

On the Researches of Mixed User Grouping NOMA Systems

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-86203-8_21,
        author={Jinqiang Li and Jiaxing Zhang and Hsiao-Hwa Chen and Shuyi Chen and Qing Guo},
        title={On the Researches of Mixed User Grouping NOMA Systems},
        proceedings={Wireless and Satellite Systems. 14th EAI International Conference, WiSATS 2024, Harbin, China, August 23--25, 2024, Proceedings, Part II},
        proceedings_a={WISATS PART 2},
        year={2025},
        month={3},
        keywords={Non-orthogonal multiple access (NOMA) Mixed grouping strategies Genetic algorithms Simulated annealing algorithms},
        doi={10.1007/978-3-031-86203-8_21}
    }
    
  • Jinqiang Li
    Jiaxing Zhang
    Hsiao-Hwa Chen
    Shuyi Chen
    Qing Guo
    Year: 2025
    On the Researches of Mixed User Grouping NOMA Systems
    WISATS PART 2
    Springer
    DOI: 10.1007/978-3-031-86203-8_21
Jinqiang Li1, Jiaxing Zhang, Hsiao-Hwa Chen2, Shuyi Chen1,*, Qing Guo1
  • 1: Harbin Institute of Technology
  • 2: National Cheng Kung University
*Contact email: chenshuyitina@163.com

Abstract

This paper introduces the concept of a mixed grouping strategies for NOMA systems, analyzes its advantages over traditional OMA and fixed grouping strategies for NOMA systems, and proposes two feasible mixed grouping algorithms based on genetic algorithm and simulated annealing algorithm to improve system channel capacity. Through simulation verification, we find that the NOMA system with mixed grouping exhibits significant advantages in terms of system capacity and fairness. Furthermore, compared to brute force search algorithms, the proposed mixed grouping algorithms can significantly reduce computational complexity with only minor performance loss. Therefore, it can be concluded that the proposed mixed grouping algorithms strikes a good balance between performance and computational complexity. Additionally, this paper further explores the potential for implementing more complex NOMA system mixed grouping algorithms using machine learning methods in future scenarios.

Keywords
Non-orthogonal multiple access (NOMA) Mixed grouping strategies Genetic algorithms Simulated annealing algorithms
Published
2025-03-27
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-86203-8_21
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