Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then we further provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently, in which the established machine learning-based model is exploited to explore the relative relationships of channel gains of users and obtain several fixed grouping patterns via Merge operation. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed GuG scheme achieves significant performance gains in terms of system sum rate.
Bibliographical noteFunding Information:
Manuscript received January 16, 2020; revised June 10, 2020 and September 30, 2020; accepted November 30, 2020. Date of publication December 23, 2020; date of current version May 10, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61936014; in part by the National Key Research and Development Project under Grant 2019YFB2102300, Grant 2019YFB2102301, and Grant 2020YFB2103900; in part by the National Natural Science Foundation of China under Grant 61901302; in part by the Natural Science Foundation of Shanghai under Grant 20ZR1462400; in part by the Open Research Fund from Shandong Provincial Key Laboratory of Wireless Communication Technologies under Grant SDKLWCT-2019-02; in part by the Fundamental Research Funds for the Central Universities; and in part by the National Science Foundation under Grant CPS-1932413 and Grant ECCS-1935915. The associate editor coordinating the review of this article and approving it for publication was Y. Chen. (Corresponding authors: Shengjie Zhao; Rongqing Zhang.) Weichao Chen is with the College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, and also with the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China (e-mail: email@example.com).
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- generalized user grouping
- machine learning
- power control