Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I

Research Article

Subcarrier Allocation-Based Simultaneous Wireless Information and Power Transfer for Multiuser OFDM Systems

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  • @INPROCEEDINGS{10.1007/978-3-319-73564-1_52,
        author={Xin Liu and Xiaotong Li and Zhenyu Na and Qiuyi Cao},
        title={Subcarrier Allocation-Based Simultaneous Wireless Information and Power Transfer for Multiuser OFDM Systems},
        proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I},
        proceedings_a={MLICOM},
        year={2018},
        month={2},
        keywords={SWIPT OFDM Subcarrier allocation Power allocation},
        doi={10.1007/978-3-319-73564-1_52}
    }
    
  • Xin Liu
    Xiaotong Li
    Zhenyu Na
    Qiuyi Cao
    Year: 2018
    Subcarrier Allocation-Based Simultaneous Wireless Information and Power Transfer for Multiuser OFDM Systems
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73564-1_52
Xin Liu1,*, Xiaotong Li2,*, Zhenyu Na2,*, Qiuyi Cao1,*
  • 1: Dalian University of Technology
  • 2: Dalian Maritime University
*Contact email: liuxinstar1984@dlut.edu.cn, 565856998@qq.com, nazhenyu@dlmu.edu.cn, cccqiu_yi@163.com

Abstract

Most of existing works on simultaneous wireless information and power transfer (SWIPT) for OFDM systems are studied based on power splitting or time splitting, which may lead to the time delay and the decreasing of sub-carrier utilization. In this paper, a multiuser orthogonal frequency division multiplexing (OFDM) system is proposed, which divides the sub-carriers into two parts, one for information decoding and the other one for energy harvesting. We investigate the optimization problem for maximizing the sum rate of users under the constraint of energy harvesting through optimizing the channel allocation and power allocation. By using the iterative algorithm, the optimal solution to the optimization problem can be achieved. The simulation results show that the proposed algorithm converges fast and outperforms the conventional algorithm.