IoT as a Service. 4th EAI International Conference, IoTaaS 2018, Xi’an, China, November 17–18, 2018, Proceedings

Research Article

Joint Power and Splitting Factor Allocation Algorithms for Energy Harvesting Enabled Hybrid Cellular Networks

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  • @INPROCEEDINGS{10.1007/978-3-030-14657-3_23,
        author={Jianjun Yang and Zhiren Yao and Jie Hu and Longjiang Li and Yuming Mao},
        title={Joint Power and Splitting Factor Allocation Algorithms for Energy Harvesting Enabled Hybrid Cellular Networks},
        proceedings={IoT as a Service. 4th EAI International Conference, IoTaaS 2018, Xi’an, China, November 17--18, 2018, Proceedings},
        proceedings_a={IOTAAS},
        year={2019},
        month={3},
        keywords={Hybrid cellular networks Resource allocation Wireless information and power transfer},
        doi={10.1007/978-3-030-14657-3_23}
    }
    
  • Jianjun Yang
    Zhiren Yao
    Jie Hu
    Longjiang Li
    Yuming Mao
    Year: 2019
    Joint Power and Splitting Factor Allocation Algorithms for Energy Harvesting Enabled Hybrid Cellular Networks
    IOTAAS
    Springer
    DOI: 10.1007/978-3-030-14657-3_23
Jianjun Yang1,*, Zhiren Yao1,*, Jie Hu1,*, Longjiang Li1,*, Yuming Mao1,*
  • 1: University of Electronic Science and Technology of China
*Contact email: jjyang@uestc.edu.cn, zhirenyao@uestc.edu.cn, hujie@uestc.edu.cn, longjiangli@uestc.edu.cn, ymmao@uestc.edu.cn

Abstract

In the hybrid cellular network with Simultaneous Wireless Information and Power Transfer (SWIPT), interference signal is a source of energy. In this paper, we develop a resource allocation scheme, which jointly optimizes transmit powers of base station (BS) and received power splitting ratios for energy harvesting and information processing at the users. Meeting the user’s minimum throughput and energy harvesting rate, we perform with two different objectives to maximize the downlink information rate of small cell users and max-min their throughput. To solve the non-convex optimization problem, we propose to solve a series of geometric programming through the approach of successive convex approximation and devising iterative algorithms based on geometric programming. Numerical results are provided to demonstrate the effectiveness of proposed algorithm and its ability to improve network performance.