Multi-band RF Energy and Spectrum Harvesting in Cognitive Radio Networks

TL;DR

This paper presents a multi-band RF energy harvesting framework in cognitive radio networks, optimizing sensing and harvesting strategies using machine learning to enhance energy efficiency and spectrum utilization.

Contribution

It introduces a novel joint optimization of sensing parameters and energy harvesting, employing geometric programming and SVM for efficient spectrum and energy management.

Findings

Optimized sensing and harvesting improve energy collection.

Machine learning reduces sensing energy by selective sensing.

Near-optimal solutions enhance spectrum and energy efficiency.

Abstract

This paper investigates a multi-band harvesting (EH) schemes under cognitive radio interweave framework. All secondary users are considered as EH nodes that are allowed to harvest energy from multiple bands of Radio Frequency (RF) sources. A win-win framework is proposed, where SUs can sense the spectrum to determine whether the spectrum is busy, and hence they may harvest from RF energy, or if it is idle, and hence they can use it for transmission. Only a subset of the SUs can sense in order to reduce sensing energy, and then machine learning is used to characterize areas of harvesting and spectrum usage. We formulate an optimization problem that jointly optimize number of sensing samples and sensing threshold in order to minimize the sensing time and hence maximize the amount of energy harvested. A near optimal solution is proposed using Geometric Programming (GP) to optimally solve the problem in a time-slotted period. Finally, an energy efficient approach based on multi-class Support Vector Machine (SVM) is proposed by involving only training SUs instead of all SUs.