Energy Efficient RIS-Assisted Wireless Powered D2D Communications in Cognitive Radio Networks

Abstract

We consider a passive reconfigurable intelligent surface (RIS)-empowered device-to-device (D2D) cognitive radio network (CRN). In this setup, a batteryless secondary transmitter (ST) harvests energy from radio frequency (RF) signals emitted by the primary transmitter (PT). Using this harvested energy, the ST transmits messages by accessing the spectrum licensed to a primary receiver. The RIS aids the D2D pair by enhancing the desired signal and suppressing interference to the primary network. We maximize the energy efficiency (EE) of the secondary network by jointly optimizing the transmit powers and passive beamforming at the RIS. As the formulated problem is non-convex, we decouple it into two sub-problems. We propose an efficient alternating optimization algorithm that jointly designs the optimal resource allocation and beamforming at the tuneable RIS elements. The convergence performance and complexity of the proposed algorithm are analyzed. The theoretical claims and convergence analysis are validated through numerical results, and various system design insights are derived. We compare our proposed scheme with two semi-adaptive schemes, namely, power-optimal, and phase-shift optimal. It is demonstrated that the proposed jointly optimal framework yields a substantial gain of 35%, while the power-optimal and phase-shift optimal schemes offer improvements of 20% and 12%, respectively over the fixed scheme.