TY - GEN
T1 - On the Researches of Mixed User Grouping NOMA Systems
AU - Li, Jinqiang
AU - Zhang, Jiaxing
AU - Chen, Hsiao Hwa
AU - Chen, Shuyi
AU - Guo, Qing
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Genetic algorithms
KW - Mixed grouping strategies
KW - Non-orthogonal multiple access (NOMA)
KW - Simulated annealing algorithms
UR - https://www.scopus.com/pages/publications/105002133224
U2 - 10.1007/978-3-031-86203-8_21
DO - 10.1007/978-3-031-86203-8_21
M3 - 会议稿件
AN - SCOPUS:105002133224
SN - 9783031862021
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 263
EP - 276
BT - Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings
A2 - Chen, Hsiao-Hwa
A2 - Meng, Weixiao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024
Y2 - 23 August 2024 through 25 August 2024
ER -