Stochastic approach for channel selection in cognitive radio networks using optimization techniques

Sumathi, D.; Manivannan, Siyamalan

doi:10.1007/s11235-020-00705-6

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…In [29], the invasive weed optimization algorithm is proposed for an increase in the spectrum handoff efficiency, which reflects the balancing of load and reduces the handoff delay. The problem is to raise the level of spectrum efficiency by assigning the DUs to the accessible channel as proposed in [30] based on the chaotic biography-based algorithm.…”

Section: Related Workmentioning

confidence: 99%

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

et al. 2023

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With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches.

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Section: Related Workmentioning

confidence: 99%

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

et al. 2023

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“…Authors in [23] suggested that the use of stochastic approach under the hybrid with Invasive weed optimization algorithm increases spectrum hand off efficiency in CR networks. The channel selection accuracy of this approach was found to be 97.8% effective than other conventional methods.…”

Section: Related Workmentioning

confidence: 99%

Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks

Tlouyamma

Velempini

2021

Wireless Pers Commun

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A major concern in the recent past was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks. In an attempt to solve this challenge, cognitive radios technology was proposed. It allows a spectrum to be accessed dynamically by Cognitive radio users or secondary users (SU). Dynamic access can efficiently be achieved by making necessary adjustment to some Medium access control (MAC) layer functionalities such as sensing and channel allocation. MAC protocols play a central role in scheduling sensing periods and channel allocation which ensure that the interference is reduced to a tolerable level. In order to improve the accuracy of sensing algorithm, necessary adjustments should be made at MAC layer. Sensing delays and errors are major challenges in the design of a more accurate spectrum sensing algorithm. This study focuses on designing a channel selection algorithm to efficiently utilize the spectrum. Channels are ordered and grouped to allow faster discovery of channel access opportunities. The ordering is based on descending order of channel’s idling probabilities. Grouping of channels ensured that channels are sensed simultaneously. These two techniques greatly reduce delays and maximized throughput of SU. Hence, Extended Generalized Predictive Channel Selection Algorithm, a proposed scheme has significantly performed better than its counterpart (Generalized Predictive Channel Selection Algorithm). Matlab simulation tool was used to simulate and plot the results of the proposed channel selection algorithm.

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Threshold Optimization in Maximum–Minimum Eigenvalue-Based Detection in Cognitive Radio Using Ant Colony Optimization

Vishwakarma

Kulkarni

2022

Mobile Computing and Sustainable Informatics

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Stochastic approach for channel selection in cognitive radio networks using optimization techniques

Cited by 6 publications

References 33 publications

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks

Threshold Optimization in Maximum–Minimum Eigenvalue-Based Detection in Cognitive Radio Using Ant Colony Optimization

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