Optimal spectrum sensing and transmission power allocation in energy‐efficiency multichannel cognitive radio with energy harvesting

Abstract

SUMMARYWe consider an energy-efficiency multichannel cognitive radio (CR) where the secondary user (SU) can harvest the signal energy from the primary user (PU) to supply the power transmission. The goal of this paper is to determine an optimal joint spectrum sensing and transmission power allocation that maximizes the average throughput of the multichannel SU over all the subchannels subject to the constraints of subchannel sensing probabilities, interference power, and total transmission power. The SU harvests the PU energy, which is then converted into electrical energy and stored in the battery if the presence of the PU is detected, while consuming the stored energy to transmit data if the absence of the PU is detected. The allocation scheme is formulated as a joint optimization problem of spectrum sensing time and subchannel transmission power vector, whose optimal solution is obtained through the joint optimization algorithm based on alternating direction optimization. Analytical and numerical results show that the proposed energy-efficiency CR outperforms the traditional CR without energy harvesting, and the transmission power of the energy-efficiency CR improves both with the increasing of the spectrum sensing time and the presence probability of the PU.

Optimal energy-efficient multichannel cognitive radio with primary energy harvesting

“…By substituting (18) into (17), the simplified optimization problem is given as follows [14] ( ) Hence, the optimal solution to (19) is given as follows h obey the Rayleigh distributions with the mean -10dB. Fig.…”

A joint sensing and transmission power control policy for RF energy harvesting cognitive radio networks

“…On the other hand, the energy harvesting technique can efficiently reduce the energy consumption and CO 2 emission . The SU can harvest energy from natural sources (solar, wind, thermal, and so on) and RF sources (wireless power, electromagnetic wave, PU signal, and so on) . However, energy harvesting from natural sources requires additional components to be placed on the user equipment.…”

Efficient resource allocation for network‐assisted multi‐link device‐to‐device communication

“…But in this article, we consider the mode selection problem with respect to each UE to establish either a cellular link or D2D link for communication with other UEs. Similar to the spectrum sensing approach in , the mode selection problem is implemented by comparing the SINR with a respecified threshold.…”