Wireless Internet. 9th International Conference, WICON 2016, Haikou, China, December 19-20, 2016, Proceedings

Research Article

A Low-Complexity Power Allocation Method in Ultra-dense Network

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  • @INPROCEEDINGS{10.1007/978-3-319-72998-5_17,
        author={Xin Su and Bei Liu and Jie Zeng and Jing Wang and Xibin Xu},
        title={A Low-Complexity Power Allocation Method in Ultra-dense Network},
        proceedings={Wireless Internet. 9th International Conference, WICON 2016, Haikou, China, December 19-20, 2016, Proceedings},
        proceedings_a={WICON},
        year={2018},
        month={1},
        keywords={Ultra-dense network Water filling algorithm Power allocation},
        doi={10.1007/978-3-319-72998-5_17}
    }
    
  • Xin Su
    Bei Liu
    Jie Zeng
    Jing Wang
    Xibin Xu
    Year: 2018
    A Low-Complexity Power Allocation Method in Ultra-dense Network
    WICON
    Springer
    DOI: 10.1007/978-3-319-72998-5_17
Xin Su1, Bei Liu2, Jie Zeng1,*, Jing Wang1, Xibin Xu1
  • 1: Tsinghua University
  • 2: Chongqing University of Posts and Telecommunications
*Contact email: zengjie@tsinghua.edu.cn

Abstract

This paper considers the downlink power allocation in the ultra-dense network. To acquire the maximum sum rate of all the users, we first make the appropriate approximate hypothesis on the interference, and then adopt the Lagrangian Multiplier method and Karush-Kuhn-Tucker condition to obtain the expression of the optimum power allocation. Finally, the iteratively searching water filling algorithm is used to allocate power for each access node, when the total power is limited. Due to the consideration of the computation complexity of the iteratively searching algorithm, we applied the low-complexity water filling algorithm into the power allocation to reduce the iteration times. The simulation results have shown that the performance of the both two water filling algorithms are close, and can improve the sum rate of the users in the ultra-dense network, and the low-complexity water filling algorithm can converge to the optimum solution more quickly.