A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 1; p. 126
• Main Authors: Arjoune, Youness, Kaabouch, Naima
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02-01-2019; MDPI
• Subjects: Artificial intelligence; Broadband; Cognitive radio; Communication; compressive sensing; International conferences; Machine learning; Narrowband; narrowband sensing; Radio networks; Radio spectra; Researchers; Review; Sparsity; spectrum sensing; wideband sensing; United States > US; narrowband sensing; wideband sensing; cognitive radio; machine learning; compressive sensing; spectrum sensing
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19010126

Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has been progressing at a rapid pace and significant advances have been made. To help researchers stay abreast of these advances, surveys and tutorial papers are strongly needed. Therefore, in this paper, we aimed to provide an in-depth survey on the most recent advances in spectrum sensing, covering its development from its inception to its current state and beyond. In addition, we highlight the efficiency and limitations of both narrowband and wideband spectrum sensing techniques as well as the challenges involved in their implementation. TV white spaces are also discussed in this paper as the first real application of cognitive radio. Last but by no means least, we discuss future research directions. This survey paper was designed in a way to help new researchers in the field to become familiar with the concepts of spectrum sensing, compressive sensing, and machine learning, all of which are the enabling technologies of the future networks, yet to help researchers further improve the efficiently of spectrum sensing.