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.

Leveraging Online Learning for CSS in Frugal IoT Network

“…Our problem setting can be formulated as perceptron with known bias term γ p j computed according to (25). Hence the objective of finding hyperplane (21) can be written as,…”

“…At FC the state of channel c j at n th time step, f j (n) is predicted as busy or free by comparing the combined soft informationf j (n) with the threshold γ p j according to (25). Depending on the final prediction f j (n) by FC, it instructs SUs (either to transmit or not) and the AGT g(n) is known.…”

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

“…To this end, the time overhead should be minimized. The overhead of transmission can be resulted due to several factors such as propagation delay (τ Pr ), the MAC layer latency (τ MAC−SS ) due to the internal competition of secondary nodes, the delay caused by the spectrum sensing (τ SpectrumSensing ) [74][75][76][77][78], the delay caused by the retransmission (τ Retransmission ), and the delay resulting from Negotiation/Agreement process between two cognitive nodes (τ N ). Therefore, the total delay can be written as follows:…”

Hybrid Sliding-Window Based Detector for Spectrum Sensing in Radar Bands

“…It can implement a dynamic spectrum allocation policy in which idle frequency bands can be opportunistically occupied by secondary user (SU) terminals. For the successful access to a vacant frequency band, cognitive SU terminals must be able to detect the presence of the primary user (PU) signal in the band of interest, by means of a process known as spectrum sensing [2], [3].…”