Joint resource allocation for cognitive OFDM-NOMA systems with energy harvesting in green IoT

Abstract

In order to enhance the capacity and extend the lifetime of nodes for green Internet of Things (IoT), the non-orthogonal multiple access (NOMA) is applied to cognitive orthogonal frequency-division multiplexing (OFDM) systems, along with simultaneous wireless information and power transfer (SWIPT). Firstly, an uplink SWIPT-based cognitive OFDM-NOMA system model is proposed where the power splitting (PS) mode is applied to harvest energy from radio frequency signals. Then, we investigate the problem of maximizing the sum data rate of uplink transmission by jointly optimizing sensing duration, user matching, and power allocation constrained by the transmit power and harvested energy. In order to decouple the strong relations among variables, the proposed optimization problem is decomposed into three sub-problems, i.e., the optimization of sensing duration, the optimization of user matching based on the matching theory, and the optimization of power allocation. We propose an alternate iteration algorithm to jointly solve the three sub-problems. Furthermore, two cognitive modes are employed in this paper: overlay mode and underlay mode. The performance of sum rate as well as harvested energy is evaluated, while simulation results are shown to verify the convergence of the proposed algorithm. It is shown that the proposed algorithm can not only perform the subcarrier allocation efficiently, but also behave well in terms of sum data rate subject to the required energy.