On Green Energy Powered Cognitive Radio Networks

TL;DR

This paper reviews techniques for energy-efficient cognitive radio networks powered by renewable energy, focusing on spectrum sensing, management, and optimization of green energy usage amidst environmental variability.

Contribution

It provides a comprehensive survey of current methods and discusses challenges in designing green energy powered cognitive radio networks with dynamic spectrum and energy management.

Findings

Energy harvesting introduces randomness in green energy availability.

Optimizing spectrum access and green energy utilization is crucial for network performance.

Existing techniques focus on energy-aware spectrum sensing and cooperative radio strategies.

Abstract

Green energy powered cognitive radio (CR) network is capable of liberating the wireless access networks from spectral and energy constraints. The limitation of the spectrum is alleviated by exploiting cognitive networking in which wireless nodes sense and utilize the spare spectrum for data communications, while dependence on the traditional unsustainable energy is assuaged by adopting energy harvesting (EH) through which green energy can be harnessed to power wireless networks. Green energy powered CR increases the network availability and thus extends emerging network applications. Designing green CR networks is challenging. It requires not only the optimization of dynamic spectrum access but also the optimal utilization of green energy. This paper surveys the energy efficient cognitive radio techniques and the optimization of green energy powered wireless networks. Existing works on energy aware spectrum sensing, management, and sharing are investigated in detail. The state of the art of the energy efficient CR based wireless access network is discussed in various aspects such as relay and cooperative radio and small cells. Envisioning green energy as an important energy resource in the future, network performance highly depends on the dynamics of the available spectrum and green energy. As compared with the traditional energy source, the arrival rate of green energy, which highly depends on the environment of the energy harvesters, is rather random and intermittent. To optimize and adapt the usage of green energy according to the opportunistic spectrum availability, we discuss research challenges in designing cognitive radio networks which are powered by energy harvesters.