Spectrum sensing and spectrum sensing techniques
A way forward is to decrease the high number of samples acquired using compressive sensing [ 53545556 ]. Table 1. 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.
Non cooperative spectrum sensing
Abstract Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. These devices can use unlicensed bands as well as licensed bands when their licensed primary users are not active, preventing adverse interference. 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. Cognitive radio technology allows wireless devices to sense the radio spectrum, decide about the state of the frequency channels, and reconfigure their communication parameters to meet quality-of-service requirements while minimizing their energy consumption [ 4 ]. Overall, the investigations in this thesis provide novel spectrum sensing techniques for overcoming the challenge of noise uncertainty with reduced computational complexity. TV white spaces are also discussed in this paper as the first real application of cognitive radio. These authors also provided a performance comparison between these techniques in terms of accuracy and complexity. A way forward is to decrease the high number of samples acquired using compressive sensing [ 53 , 54 , 55 , 56 ]. Simultaneous sensing schemes require a large number of sensors and joint synchronized function, increasing the complexity of a given implementation [ 52 ]. Not sharing the radio spectrum among users can result in the creation of unwanted denial of service events. Sequential-sensing approaches are ineffective because they require longer times and higher energy due to the use of high-rate analog-to-digital converters ADC , which is both costly and impractical for timely communications. Compressive sensing recovers the original sparse signal from only a few measurements. Over 50 billion wireless devices will be connected by , all of which are likely going to demand access to the Internet [ 2 ]. To apply compressive sensing, signals are required to be sparse in a given domain and the sensing matrix has to satisfy the restrict isometry property RIP , or it must have a small mutual incoherence to guarantee the exact recovery of the original sparse signal.
These authors also provided a performance comparison between these techniques in terms of accuracy and complexity. Considering the limited radio spectrum, supporting the demand for higher capacity and higher data rates is a challenging task that requires innovative technologies capable of providing new ways of exploiting the available radio spectrum.
Abstract Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access.
Introduction The dramatic growth in the number of wireless devices alongside the static management of the radio spectrum have created a shortage of available radio spectrum [ 1 ].
Several survey papers that provide an overview of the wideband spectrum sensing and compressive sensing are shown in Table 1 [ 535456686970 ]. Simultaneous sensing schemes require a large number of sensors and joint synchronized function, increasing the complexity of a given implementation [ 52 ].
based on 101 review