Optimal time sharing in underlay cognitive radio systems with RF energy harvesting

Due to the fundamental tradeoffs, achieving spectrum efficiency and energy efficiency are two contending design challenges for the future wireless networks. However, applying radio-frequency (RF) energy harvesting (EH) in a cognitive radio system could potentially circumvent this tradeoff, resulting in a secondary system with limitless power supply and meaningful achievable information rates. This paper proposes an online solution for the optimal time allocation (time sharing) between the EH phase and the information transmission (IT) phase in an underlay cognitive radio system, which harvests the RF energy originating from the primary system. The proposed online solution maximizes the average achievable rate of the cognitive radio system, subject to the $\varepsilon$-percentile protection criteria for the primary system. The optimal time sharing achieves significant gains compared to equal time allocation between the EH and IT phases.Comment: Proceedings of the 2015 IEEE International Conference on Communications (IEEE ICC 2015), 8-12 June 2015, London, U

Power Minimization Resource Allocation for Underlay MISO-NOMA SWIPT Systems

The combination of cognitive radio and non-orthogonal multiple access (NOMA) has tremendous potential to achieve high spectral efficiency in the IoT era. In this paper, we focus on the energy-efficient resource allocation of a cognitive multiple-input single-output NOMA system with the aid of simultaneous wireless information and power transfer. Specifically, a non-linear energy harvesting (EH) model is adopted to characterize the non-linear energy conversion property. In order to achieve the green design goal, we aim for the minimization of the system power consumption by jointly designing the transmit beamformer and the receive power splitter subject to the information transmission and EH harvesting requirements of second users (SUs), and the maximum tolerable interference constraints at primary users. However, the formulated optimization problem is non-convex and hard to tackle. By exploiting the classic semi-definite relaxation and successive convex approximation, we propose a penalty function-based algorithm to solve the non-convex problem. The convergence of the proposed algorithm is further proved. Finally, simulation results demonstrate that the non-linear EH model is able to strongly reflect the property of practical energy harvester and the performance gain of the proposed algorithm than the baseline scheme