Achievable Throughput of Energy Harvesting Cognitive Radio Networks

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.

I. Introduction

The energy harvesting cognitive radio network has received substantial attention as a promising approach to increasing both energy efficiency and spectral efficiency [1]–[6]. Powering cognitive radio network by harvesting energy from ambient sources (e.g., solar, wind, thermal, vibration, and even ambient radio power) or other energy sources (e.g., wireless power transfer) enables potentially perpetual operation without the need for external power cables or periodic battery replacements [7]. In addition, the opportunistic spectrum access allows the secondary network to dynamically access underutilized spectrum licensed to the primary network [8]. This system therefore represents an attractive alternative for various types of future wireless networks, including heterogeneous cognitive radio networks and cognitive sensor networks. In heterogeneous cognitive radio networks, a self-powered small cell base station will be realistic in the near future due to its rapid miniaturization, dense deployment, increasingly bursty nature of user traffic, recent advancement of cost-effective energy harvesting techniques, and increased availability of high-speed wireless backhaul [9]. Therefore, combining energy harvesting technique with cognitive radio capability would open up entirely new categories of low-cost “drop and play” small cell deployments in the heterogeneous cognitive radio networks. Similarly, energy harvesting is emerging as a promising technique for cognitive sensor networks since battery installed on sensors are limited due to physical constraints, such as difficulties in replacing the batteries due to inaccessibility of the sensors.