Wireless-Powered Intelligent Radio Environment With Nonlinear Energy Harvesting

• Published in: IEEE internet of things journal Vol. 9; no. 18; pp. 18130 - 18141
• Main Authors: Chu, Zheng, Xiao, Pei, Mi, De, Hao, Wanming, Lin, Zihuai, Chen, Qingchun, Tafazolli, Rahim
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmarks; Complex circle manifold (CCM); Downlink; Energy harvesting; Energy transfer; Information transfer; intelligent radio environment; intelligent reflecting surface (IRS); Intelligent sensors; Internet of Things; Iterative methods; Kuhn-Tucker method; nonlinear energy harvesting (NLEH); phase shifts; quadratic transformation (QT); Radio; Throughput; Uplink; Wireless communication; Wireless sensor networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2022.3162761

This article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.