Cooperative Spectrum Prescience for Advanced Performance of Cognitive Radio Networks (CRNs)
Chowdhury Sajadul Islam, Mohammad Sarwar Hossain Mollah · 2018
Notice of Violation of IEEE Publication Principles“Cooperative Spectrum Prescience for Advanced Performance of Cognitive Radio Networks (CRNs)”by Chowdhury Sajadul Islam and Md. Sarwar Hossain Mollahin the Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), September 2018After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.This paper duplicates large portion of text and figures from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:“Spectrum and Energy Efficiency of Cooperative Spectrum Prediction in Cognitive Radio Networks”by Nagwa Shaghluf and T. Aaron Gulliver,in Wireless Networks 25, March 2018, pp. 3265–3274In this paper, the spectrum and energy efficiency (EE) of cooperative spectrum prescience (CSP) in cognitive radio networks (CRNs) are investigated. In integration, the performance of CSP is evaluated utilizing hidden Markov model (HMM) and a multilayer perceptron (MLP) neural network. The cooperation between secondary users in presaging the next channel status employs AND, OR and majority rule fusion schemes. Simulation results show a paramount rule in the spectrum efficiency utilizing CSP with the majority rule at the cost of a diminutive degradation in energy efficiency compared to single spectrum prescience (SSP) and traditional spectrum sensing (TSS). The HMM prognosticator provides better performance than the MLP presage. Moreover, the total probability of prescience error with the majority rule provides the best performance compared to SSP and the other fusion rules. On the other hand, the AND and OR rules have the worst performance in the high and low traffic cases, respectively. The majority rule provides a good tradeoff between diligent and redundant state prescience errors compared with the AND and OR rules and SSP. Further, a reduction in the diligent state prescience error increases the spectrum efficiency (SE) more compared to a reduction in the redundant state prescience error.