Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

TL;DR

This paper reviews how cognitive radio and machine learning techniques can enhance wireless communication systems by improving spectrum sensing, access, and energy efficiency through intelligent adaptation and reconfiguration.

Contribution

It provides a comprehensive overview of recent developments in cognitive radio and machine learning for wireless communications, highlighting their roles in system adaptation and efficiency.

Findings

Enhanced spectrum sensing and access methods

Machine learning algorithms for dynamic environment adaptation

Practical applications demonstrating improved efficiency

Abstract

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.