Stochastic geometry analysis of reconfigurable intelligent surface-assisted millimeter-wave energy harvesting networks - Muhammad, Nor Aishah; Seman, Norhudah; Saleh, Mohammed Mehdi; Anwar Apandi, Nur Ilyana (Institute Of Electrical And Electronics Engineers Inc., 2025)
Energy harvesting (EH) in millimeter-wave (mm-wave) cellular networks has gained significant attention due to the widespread use of large antenna arrays and dense base station (BS) deployments. However, mm-wave signals are highly susceptible to path attenuation caused by significant atmospheric and obstacle-induced absorption, which can limit coverage and degrade the performance of EH systems due to high path losses. This paper considers the use of reconfigurable intelligent surface (RIS)-assisted mm-wave networks as a solution to enhance EH performance. We propose an analytical framework based on stochastic geometry to evaluate the energy coverage probability (ECP) performance of user equipment (UE) in these networks, deriving a closed-form expression for the ECP. The analytical formula for average harvested energy (AHE) is also provided to help characterize system performance. The findings show that deploying RISs can significantly improve EH performance in mm-wave networks, even in challenging urban areas with significant path loss. The findings also show that dense deployment of passive RISs significantly improves EH comparable to active BS deployment. Furthermore, the findings indicate that adding passive reflectors is as effective as equipping the BS with additional active antenna elements to enhance ECP and AHE. This study provides valuable insights for designing future EH strategies tailored to end-UEs in RIS-assisted mm-wave networks, emphasizing the efficacy of RISs in improving network performance and EH capabilities.