Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks
Seunghyun Lee, Rui Zhang, Kaibin Huang

TL;DR
This paper introduces a novel model for cognitive radio networks where secondary devices harvest RF energy from primary transmitters while opportunistically accessing the spectrum, optimizing network throughput under interference constraints.
Contribution
It presents a stochastic-geometry-based analysis of RF energy harvesting in cognitive radio networks, deriving optimal transmission power and density for secondary users.
Findings
Optimal secondary transmitter density for maximum throughput.
Derived transmission power levels under outage constraints.
Analytical model applicable to non-CR wireless power transfer scenarios.
Abstract
Wireless networks can be self-sustaining by harvesting energy from ambient radio-frequency (RF) signals. Recently, researchers have made progress on designing efficient circuits and devices for RF energy harvesting suitable for low-power wireless applications. Motivated by this and building upon the classic cognitive radio (CR) network model, this paper proposes a novel method for wireless networks coexisting where low-power mobiles in a secondary network, called secondary transmitters (STs), harvest ambient RF energy from transmissions by nearby active transmitters in a primary network, called primary transmitters (PTs), while opportunistically accessing the spectrum licensed to the primary network. We consider a stochastic-geometry model in which PTs and STs are distributed as independent homogeneous Poisson point processes (HPPPs) and communicate with their intended receivers at…
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