Resource and Power Allocation in SWIPT-Enabled Device-to-Device Communications Based on a Nonlinear Energy Harvesting Model
Haohang Yang, Yinghui Ye, Xiaoli Chu, Mianxiong Dong
Abstract
Due to the limited battery capacity in mobile devices, simultaneous wireless information and power transfer (SWIPT) has been proposed as a promising solution to improve the energy efficiency (EE) in Internet-of-Things (IoT) networks, i.e., device-to-device (D2D) networks, by allowing mobile devices to harvest energy from ambient radio-frequency (RF) signals. However, the nonlinear behavior of RF energy harvesters has largely been ignored in the existing works on SWIPT. In this article, we propose to maximize the sum EE of all D2D links in a D2D underlaid cellular network by optimizing the resource and power allocation based on a nonlinear energy harvesting (EH) model. Toward this end, we first propose a prematching algorithm to divide the D2D links into a SWIPT-enabled group and a non-EH group that cannot meet the EH circuit sensitivity. We then develop a two-layer iterative algorithm to jointly optimize the D2D transmission power and the power splitting ratio to maximize the EE for each SWIPT-enabled D2D link. On this basis, we build the preference lists for both SWIPT-enabled D2D links and cellular user equipment (CUE), and propose a one-to-one constraint stable matching algorithm to maximize the sum EE of all SWIPT-enabled D2D links by optimizing the spectrum resource sharing between D2D links and CUEs. The sum EE of non-EH D2D links is maximized through an iterative power control algorithm and a one-to-one stable matching algorithm. Simulation results show that our proposed algorithms achieve a much higher sum EE than the existing matching-based energy-efficient resource allocation scheme for SWIPT-enabled D2D networks.