Wideband Spectrum Sensing for Cognitive Radio Networks: A Survey

TL;DR

This survey reviews wideband spectrum sensing algorithms for cognitive radio, emphasizing sub-Nyquist techniques like compressive sensing, to enhance spectrum detection across broad frequency ranges in future networks.

Contribution

It provides a comprehensive overview of existing wideband spectrum sensing methods, highlighting advantages, disadvantages, and challenges, especially focusing on sub-Nyquist sampling approaches.

Findings

Sub-Nyquist techniques enable efficient wideband sensing.

Compressive sensing reduces sampling requirements.

Trade-offs exist between detection accuracy and complexity.

Abstract

Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization in next generation cellular networks. A crucial requirement for future cognitive radio networks is wideband spectrum sensing: secondary users reliably detect spectral opportunities across a wide frequency range. In this article, various wideband spectrum sensing algorithms are presented, together with a discussion of the pros and cons of each algorithm and the challenging issues. Special attention is paid to the use of sub-Nyquist techniques, including compressive sensing and multi-channel sub-Nyquist sampling techniques.