Energy-Efficient Sub-Carrier and Power Allocation in Cloud-Based Cellular Network With Ambient RF Energy Harvesting

• Main Authors: Yisheng Zhao, Victor C. M. Leung, Chunsheng Zhu, Hui Gao, Zhonghui Chen, Hong Ji
• Format: Article
• Language: English
• Published: IEEE 2017-01-01
• Series: IEEE Access
• Subjects: Ambient RF energy harvesting; cloud-based cellular network; energy efficiency; resource allocation
• Online Access: https://ieeexplore.ieee.org/document/7850971/

Due to the limited battery energy of mobile devices, the issue of energy-efficient resource allocation has drawn significant interest in the mobile cloud computing area. Simultaneous wireless information and power transfer (SWIPT) is an innovative way to provide electrical energy for mobile devices. Extensive research on the resource allocation problem is conducted in SWIPT systems. However, most previous works mainly focus on energy harvesting over a relatively narrow frequency range. Due to small amounts of energy harvested by the users, the practical implementations are usually limited to low power devices. In this paper, an energy-efficient uplink resource allocation problem is investigated in a cloud-based cellular network with ambient radio frequency (RF) energy harvesting. In order to obtain sufficient energy, a broadband rectenna is equipped at the user device to harvest ambient RF energy over six frequency bands at the same time. From the viewpoint of service arrival in the ambient transmitter, a new energy arrival model is presented. The joint problem of sub-carrier and power allocation is formulated as a mixed-integer nonlinear programming problem. The objective is to maximize the energy efficiency while satisfying the energy consumption constraint and the total data rate requirement. In order to reduce the computational complexity, a suboptimal solution to the optimization problem is derived by employing a quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that more energy can be harvested by the user devices compared with narrow band SWIFT systems, and the QPSO method achieves higher energy efficiency than a conventional particle swarm optimization approach.