Spectrum Sensing Methods for Cognitive Radio Networks: A Review

Claudino, Lucas; Abrão, Taufik

doi:10.1007/s11277-017-4143-1

Cited by 18 publications

(15 citation statements)

References 35 publications

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“…Moreover, Yadav et al 20 have presented that for AP ¼ 5, the clusterbased network shows a performance improvement of $24.48%, over the non-clustered network. From the figure, it can be observed that the highest detection probability has been achieved for the multislot with dynamic clustering (8,4,3).…”

Section: Resultsmentioning

confidence: 94%

“…Therefore, the first cluster has been formed as (8,5,2). Likewise, in the second iteration, five CRs have reported to AP-1, six CRs to AP-2, and four CRs to AP-3, which results in the formation of a second cluster as (5,6,4). Figure 6 illustrates the performance of both the dynamic cluster networks and non-clustered networks for the target values of false alarm probability (P f ) and SNR of 0:01 and À25 dB, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…From the figure, an increase in The figure depicts that for a fixed sensing time, the detection probability of a cluster-based network is higher than that of a non-clustered network. For example, at t ¼ 2:3 ms, P d ¼$ 0:727, $ 0:5062, and $ 0:3288, respectively, for cluster (5,6,4), cluster (8, 5, 2), and without cluster. However, from the extensive simulation analysis, it has been noticed that the obtained variations in the detection probability are not optimal for a particular cluster, determined in each observation.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, spectrum sensing (SS) has turned out to be a vital function in the CR. [1][2][3][4] The use of the spectrum is determined by two factors: the spectrum sensing accuracy and the rapidity with which a vacant/unused spectrum of PU can be found. As the PU must be detected precisely in quick time, when PU signal is vague to CR, the spectrum sensing scheme based on the energy detection (ED) approach is an appropriate choice.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐slot‐based dynamic clustering in cognitive radio network

Singh

Ranjan

2022

Int J Communication

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Summary The current work investigates the performance of multi‐slot‐based dynamic clustering in a cognitive radio network. For the spectrum sensing, every secondary user utilizes the multi‐slots of sensing time frame and combines the decisions of every multi‐slot, using the OR logic scheme, to get the sub‐local decision. The channel state information of the connections among the secondary users and access points present on the network's access points has been used to cluster the secondary users. To form the clusters, the secondary users with higher quality reporting channels for a specific access point have been chosen. Therefore, the clusters formed, in each iteration, with their respective access points, are dynamic in nature. Every secondary user in a cluster detects the availability of a primary user, makes a decision, and forwards it to its corresponding access point, via the reporting‐channel. Each access point employs the OR rule to merge the decisions of secondary users in the respective clusters, to make a local decision. Finally, the local decisions of all access points have been directed to fusion center, making the final decision about primary user's accessibility. The performance of multi‐slot‐based dynamic clustering has been studied relative to the different parameters, like detection probability, number of clusters, number of multi‐slots, secondary users, and access points. Further, the performance evaluation and comparisons of a multi‐slot‐based cognitive radio network with a single slot, cluster‐based cognitive radio network with non‐clustered, and multi‐slot‐based dynamic cluster with individual multi‐slots as well as dynamic clustering, have been done.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi‐slot‐based dynamic clustering in cognitive radio network

Singh

Ranjan

2022

Int J Communication

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“…Spectrum sensing is a mandatory component at IEEE 802.22 WRAN standardization to enable self-coexistence for CR and it could be achieved by two methods [9] [10]. In the first method, the radio itself becomes spectrum awareness by detecting and estimating the spectrum to identify which channels are occupied and which are unoccupied [11].…”

Section: Introductionmentioning

confidence: 99%

Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey

Abo-Zahhad¹,

Ahmed²,

Farrag³

et al. 2018

JSIP

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Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole. The task of sensing is becoming more challenging especially at wideband spectrum scenario. The difficulty is due to conventional sampling rate theory which makes it infeasible to sample such very wide range of frequencies and the technical requirements are very costly. Recently, compressive sensing introduced itself as a pioneer solution that relaxed the wideband sampling rate requirements. It showed the ability to sample a signal below the Nyquist sampling rate and reconstructed it using very few measurements. In this paper, we discuss the approaches used for solving compressed spectrum sensing problem for wideband cognitive radio networks and how the problem is formulated and rendered to improve the detection performance.

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