An Online Energy Allocation Strategy for Energy Harvesting Cognitive Radio Systems

Energy harvesting is gaining increasing importance for complementing the current battery-powered wireless communication systems, prolonging their lifetime and making them more environment friendly. Moreover, applying energy harvesting techniques to cognitive radio network (CRN) can improve both energy efficiency and spectral efficiency. In this paper, we consider a cognitive radio scenario where the secondary user is eqnipped with energy-harvesting transmitter and the system is slotted in time. According to the stored energy in rechargeable battery, the secondary user (SU) decides whether to detect the activity of primary user (PU) or to remain idle at the beginning of each slot. Based on the decision, the SU carries out energy allocation over a finite time horizon to maximize its throughput, taking channel condition and time-varying energy source into account. Employing the sliding window approach to optimize the energy allocation, a suboptimal algorithm is proposed. The simulation results show that the throughput obtained by the proposed scheme is much higher than that of myopic policy.