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

Abstract

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.

Keywords: cognitive radio; compressive sensing; machine learning; narrowband sensing; spectrum sensing; wideband sensing.

Figure 1

Classification of spectrum sensing techniques.

Figure 2

Block diagram of Energy Detection [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20].

Figure 3

Block diagram of cyclostationary features based techniques [21,22,23,24,25,26,27].

Figure 4

Block diagram of matched filter based spectrum sensing.

Figure 5

Block diagram of covariance-based based techniques.

Figure 6

Block diagram of wavelet based spectrum sensing [71,72,73,74,75,76].

Figure 7

Block diagram of multiband joint detection [77,78].

Figure 8

Block diagram of filter bank based spectrum sensing [79,80,81,82].

Figure 9

Block diagram of compressive sensing [53,54,56,67].

Figure 10

Theoretical framework of one-bit compressive sensing [92,99,100,101,102,103,104,105].

Figure 11

Concept of compressive sensing as introduced in [64].