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Revenue maximization based joint optimization in mmWave cell-free network: an equivalent decomposition and alternative iteration combined approach

Author

Listed:
  • Zhongyu Ma

    (Northwest Normal University)

  • Liang Ran

    (Northwest Normal University)

  • Jianbing Pu

    (Northwest Normal University)

  • Qun Guo

    (Lanzhou University of Technology)

  • Xianghong Lin

    (Northwest Normal University)

Abstract

Recently, the ever-increasing demands including higher rate and connection stability have bottlenecked the user experience of the traditional cellular networks. To this end, the Cell-Free (CF) network, which is characterized as a user-centric architecture, is viewed as a promising paradigm to enhance the user experience. Aimed at this point, this paper investigates the joint optimization problem including user matching, sub-channel allocation and power controlling for the mmWave CF network to maximize the revenue of the operators. Firstly, the revenue maximization oriented joint optimization problem of the mmWave CF network is formulated as mixed integer non-convex and non-linear programming, which is NP-hard problem and is intractable to search an optimal solution in within polynomial time. Secondly, the original problem is decomposed into three sub-problems, i.e., user association sub-problem, the sub-channel allocation sub-problem and the power controlling sub-problem under the consideration of the matching quotas, rate demand and transmission power, etc. Thirdly, a many-to-many matching based user association algorithm and an alternating iterative joint resource management algorithm, which is composed of the harmony search based sub-channel allocation sub-algorithm and the interior-point method based power controlling sub-algorithm, are proposed to obtain a sub-optimal solution, and the computational complexity of the proposed algorithms are also analyzed. Finally, the performance superiorities of the proposed algorithms are demonstrated through extensive simulations, and it is demonstrated that the proposed algorithms can outperform the proposed algorithm outperforms the PUAA algorithm by 5.73% and the SRAA algorithm by 11.25% in terms of operator revenue.

Suggested Citation

  • Zhongyu Ma & Liang Ran & Jianbing Pu & Qun Guo & Xianghong Lin, 2024. "Revenue maximization based joint optimization in mmWave cell-free network: an equivalent decomposition and alternative iteration combined approach," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 86(2), pages 333-349, June.
    • Handle: RePEc:spr:telsys:v:86:y:2024:i:2:d:10.1007_s11235-024-01171-0
    DOI: 10.1007/s11235-024-01171-0
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    References listed on IDEAS

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    1. Fei, Mingda & Zhang, Zhenyu & Zhao, Wenbo & Zhang, Peng & Xing, Zhaolin, 2024. "Optimal power distribution control in modular power architecture using hydraulic free piston engines," Applied Energy, Elsevier, vol. 358(C).
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