A Survey of Cooperative Spectrum Sensing in Cognitive Radio Networks

The spectrum sensing is the base line on which the whole process of cognitive radio works. It has raised new facets for cognitive radio and many opportunistic spectrum access approaches. To avoid the interference with the licensed users and determining the accessible spectrum for increasing the spectrum’s usage is its pivotal task of cognitive radio. Generally detection performance is compromised with receiver uncertainty, multipath fading and shadowing issues and an effective method to alleviate the effect of these issues is the cooperative spectrum sensing. This paper provides the introduction to spectrum sensing, its techniques, cooperative spectrum sensing, and System requisites for cooperative sensing, its advantages and disadvantages, and elements of cooperative spectrum sensing and further describes various research area. Finally, this paper concludes that cooperative sensing becomes crucial if the network all together is to prevent interference with each primary user

Cooperative Sequential Compressed Spectrum Sensing over Wide Spectrum Band

Abstract-Cognitive radio (CR) techniques promise to significantly increase the available spectrum thus wireless bandwidth. With the increase of spectrum allowed for CR, it is critical and challenging to perform efficient wideband sensing. We propose an integrated sequential wideband sensing framework which concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower cost spectrum sensing. First, to ensure more timely spectrum detection while avoiding the high overhead involved in periodic recovery of CS signals, we design a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further identify the sub-channels occupied, we exploit joint sparsity of the signals among neighboring users to achieve efficient cooperative wideband sensing. Our performance evaluations demonstrate that our proposed scheme can outperform other peer schemes significantly in terms of the detection delay, detection accuracy, sensing overhead and sensing accuracy

Cooperative Spectrum Sensing in Cognitive Radio Networks Using Multidimensional Correlations

In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN)2, is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN)2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN)2 (S-(CORN)2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations