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Joint resource allocation for QoE optimization in large-scale NOMA-enabled multi-cell networks

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Abstract

The non-orthogonal multiple access (NOMA) technology has been considered as a promising technology for the upcoming six-generation mobile communication networks (6G). However, with the increasing complexity of this access technology, how to allocate the limited network resources effectively for massive number of connectivity becomes more and more challenging. The QoE optimization problem including both the sub-channel (SC) assignment and power allocation is formulated. In order to cope with it, we first decouple it into two sub-problems: the UE-BS association, SC assignment sub-problem and the power allocation sub-problem. We thus propose genetic algorithm (GA) for UE-BS association, SC assignment and artificial bee colony (ABC) algorithm for power allocation. The corresponding constraints satisfying mechanisms are also proposed to ensure the feasibility of individual bee colony in each iteration, thus to accelerate the convergence. The simulation results show that the proposed power allocation scheme can converge to the optimal solution quickly, and the MOS can be increased by increasing the number of users (UE), and SCs.

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Acknowledgements

This work was supported in part by the Major Scientific and Technological Innovation Project of Shandong Province under Grant No. 2019JZZY010128, in part by the National Natural Science Foundation of China under Grants No. 61772126, 61972079, and 62172084, in part by the Fundamental Research Funds for the Central Universities under Grants N2016004, N2016002, N2116004, N2116009, and N2024005-1, in part by the Central Government Guided Local Science and Technology Development Fund Project under Grant 2020ZY0003, in part by the Science and Technology Plan Project of Inner Mongolia Autonomous Region of China under Grant 2020GG0189, in part by the LiaoNing Revitalization Talents Program under Grant No. XLYC2007162, in part by the Young and Middle-aged Scientific and Technological Innovation Talent Support Program of Shenyang under Grant RC200548. 

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Jie Jia, Yao Xu, Jian Chen, Qinghu Wang & Xingwei Wang

  2. Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, 110819, China

    Jie Jia, Qinghu Wang & Xingwei Wang

  3. SIASUN Robot & Automation CO.,Ltd., Shenyang, China

    Zhenjun Du

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  1. Jie Jia
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  2. Yao Xu
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  3. Zhenjun Du
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  4. Jian Chen
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  5. Qinghu Wang
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  6. Xingwei Wang
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Correspondence to Jie Jia.

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Jia, J., Xu, Y., Du, Z. et al. Joint resource allocation for QoE optimization in large-scale NOMA-enabled multi-cell networks. Peer-to-Peer Netw. Appl. 15, 689–702 (2022). https://doi.org/10.1007/s12083-021-01270-8

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