Achievable Throughput of Energy Harvesting Cognitive Radio Networks

Abstract

We consider energy harvesting cognitive radio networks to improve both energy efficiency and spectral efficiency. The goal of this paper is to analyze the theoretically achievable throughput of the secondary transmitter, which harvests energy from ambient sources or wireless power transfer systems while opportunistically accessing the spectrum licensed to the primary network. By modeling the temporal correlation of the primary traffic according to a time-homogeneous discrete Markov process, we derive the upper bound on the achievable throughput as a function of the energy arrival rate, the temporal correlation of the primary traffic, and the detection threshold for a spectrum sensor. The optimal detection threshold is then derived to maximize the upper bound on the achievable throughput under an energy causality constraint and a collision constraint. The energy causality constraint mandates that the total consumed energy should not exceed the total harvested energy, while the collision constraint is required to protect the primary network from secondary transmission. Analytical results show the temporal correlation of the primary traffic to enable efficient usage of the harvested energy by preventing the secondary transmitter from accessing the spectrum that may be occupied by the primary network.