Harvesting energy from natural (solar, wind, vibration, etc.) and synthesized (microwave power transfer) sources is envisioned as a key enabler for realizing green wireless networks. Energy efficient scheduling is one of the prime objectives in emerging cognitive radio platforms. To that end, in this article we present a comprehensive framework to characterize the performance of a cognitive metro-cellular network empowered by solar energy harvesting. The proposed model allows designers to capture both the spatial and temporal dynamics of the energy field and the mobile user traffic. A new definition for the “energy outage probability” metric, which characterizes the self-sustainable operation of the base stations under energy harvesting, is proposed, and the process for quantifying is described with the help of a case study for various UK cities. It is shown that the energy outage probability is strongly coupled with the path-loss exponent, required quality of service, and base station and user density. Moreover, the energy outage probability varies both on a daily and yearly basis depending on the solar geometry. It is observed that even in winter, BSs can run for three to six hours without any purchase of energy from the power grid by harvesting instantaneous energy.
- Department of Engineering and Technology - Senior Lecturer in Electronic Engineering and Embedded Systems
- School of Computing and Engineering
- Centre for Efficiency and Performance Engineering - Member
- CVIC - Centre for Visual and Immersive Computing - Associate Member