  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.
