Constraint Based Cooperative Spectrum Sensing for Cognitive Radio Network

Deka, Sanjib Kumar; Chauhan, Prakash; Sarma, Nityananda

doi:10.1109/icit.2014.12

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…FC performs spectrum sensing using energy detection (ED) [22]. To synchronize SUs with PU, a time-slotted system is considered as in [23], [24] and SUs access the channel in a time division multiple access (TDMA) fashion. Each time slot (T ) is further divided into three sub-slots, named as prediction time (t p ), sensing time (t s ), and transmission time (t t ) respectively.…”

Section: System Model and Assumptionsmentioning

confidence: 99%

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Cooperative Spectrum Prediction-Driven Sensing for Energy Constrained Cognitive Radio Networks

et al. 2021

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Spectrum prediction based sensing schemes minimize the overall energy consumption of the sensing module in cognitive radio networks (CRNs) by predicting the status of spectrum before performing actual physical sensing. But, the performance of independent or local prediction models suffer from inaccuracies. Cooperative mode of spectrum prediction is found to be suitable to overcome the issues of local prediction models. In this work, we propose a cooperative spectrum prediction-driven sensing scheme for energy constrained cognitive radio networks to reduce the energy consumption while maintaining the spectral efficiency. The proposed scheme first employs a long short term memory network technique to perform local spectrum prediction, which identifies the status of a channel before actual sensing to improve energy efficiency. Thereafter, a parallel fusion based cooperative spectrum prediction model is applied to minimize the errors induced in local prediction model. Finally, the resultant cooperative prediction model is combined with a spectrum sensing framework to perform sensing operation when the cooperative spectrum prediction results to an indeterminate state in order to enhance the spectral efficiency. Simulation results show the efficacy of the proposed scheme in terms of spectral efficiency and energy efficiency compared to similar schemes from literature.

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Section: System Model and Assumptionsmentioning

confidence: 99%

“…where, e s be the slot wise energy spent by FC during sensing. Again, using ( 21) and ( 23), the ratio of average data transfer per slot to the sensing energy consumption is given by (24).…”

Section: Joint Cooperative Spectrum Prediction and Sensingmentioning

confidence: 99%

Cooperative Spectrum Prediction-Driven Sensing for Energy Constrained Cognitive Radio Networks

et al. 2021

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“…The decision of local spectrum sensing is taken by comparing the value Y (t) with sensing decision threshold denoted by λ s . For a SU i, the decision of local spectrum sensing at time slot t denoted by d i,t is given by (2).…”

Section: System Model and Assumptionsmentioning

confidence: 99%

“…Once P d is entertained, the algorithm terminates and computes the cooperative probability of false alarm using (4). The average throughput C i of SU i for a group G can be computed by (10) [2].…”

Section: Selection Of Secondary Users (Sus) For Decision Fusionmentioning

confidence: 99%

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An Efficient Decision Fusion Scheme for Cooperative Spectrum Sensing for Cognitive Radio Networks

Chauhan

Deka

Sarma

2020

Applications of Internet of Things

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In this work, we propose an efficient decision fusion scheme for CSS, which achieves target cooperative probability of detection while maintaining a lower cooperative false alarm probability which eventually enhances the network throughput. The proposed scheme enables the fusion center (FC) to select secondary users (SUs) for decision fusion according to their reliability weights. For every SU, the reliability weight is computed by jointly considering the reporting information of SUs for past K time slots along with their detection and false alarm probabilities. The value of K is determined by computing the entropy on primary user (PU) activities observed on a designated channel over a certain period of time. Simulation-based study shows the efficacy of the proposed scheme in terms of reducing in cooperative probability of false alarm and improving the network throughput compared to conventional approach. Keywords Cooperative spectrum sensing • Reliability • Efficient decision fusion •

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