A bio-inspired swarm intelligence technique for social aware cognitive radio handovers

Haldorai, Anandakumar; Umamaheswari, K.

doi:10.1016/j.compeleceng.2017.09.016

Cited by 321 publications

(49 citation statements)

References 22 publications

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“…PSO techniques have been applied on different aspects of spectrum allocation. Tang and Xin [16] deal with maximizing energy efficiency using a co-evolution chaotic PSO, Anandakumar and Umamaheswari [17] focus on a social approach to cognitive handover using SpecPSO, Xu et al [18] talk about maximizing average weighted sum rate of throughput, with the help of a hybrid PSO model. Xu et al [19] minimize total power consumption using a RBFN based QPSO.…”

Section: Results and Analysismentioning

confidence: 99%

“…Tang and Xin [16] have applied co-evolution chaotic PSO to maximize energy efficiency, under the constraints of interference power and total transmit power. Anandakumar and Umamaheswari [17] have performed efficient social cognitive handover using socially intelligent secondary users and integration of primary and secondary holes, by applying a SpecPSO technique.…”

Section: Literature Surveymentioning

confidence: 99%

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Spectrum Allocation in Cognitive Radio: A PSO-based Approach

Mishra

Sagnika

Singh

et al. 2019

Period. Polytech. Elec. Eng. Comp. Sci.

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Cognitive radio systems have taken a fore-running position in the wireless communication technology. With most of the communication taking place through multi-carrier systems, the allocation of available spectrum to various carriers is a prominent issue. Since cognitive systems provide an environment of dynamic spectrum allocation, it becomes necessary to perform dynamic spectrum allocation swiftly with due consideration of parameters, like power consumption, fair distribution and minimal error. This paper considers a Particle Swarm Optimization-based approach, popularly used for solving large problems involving complex solution spaces to reach an optimal solution within feasible time. The mentioned spectrum allocation problem has been solved using PSO with a view to maximize the total transfer rate of the system, within specified constraints of maximum error rate, maximum power consumption and minimum transfer rate per user. The results have been compared with the existing Genetic Algorithm-based approach and have proved to be more effective.

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Section: Results and Analysismentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Spectrum Allocation in Cognitive Radio: A PSO-based Approach

Mishra

Sagnika

Singh

et al. 2019

Period. Polytech. Elec. Eng. Comp. Sci.

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“…Yamada and Zukhruf (2015) proposed a new variant of PSO to deal with the multimodal supply chain transport supernetwork equilibrium problem. Other applications of swarm-based algorithms include exploring social aware cognitive radio handovers (Anandakumar and Umamaheswari, 2018), wind farm decision systems (Zhao et al, 2019), load balancing for long-term evolution-advanced heterogeneous networks (Summakieh et al, 2019), and molten pool detection (Baskoro et al, 2011). As can be seen, swarm-based algorithms have been substantially exploited to solve numerous engineering problems, yet few such initiatives have been directed toward transportation challenges, specifically in the CT domain, as was done in the current work.…”

Section: Solution Techniquesmentioning

confidence: 99%

A Comparative Study on Swarm-based Algorithms to Solve the Stochastic Optimization Problem in Container Terminal Design

Zukhruf¹,

Frazila²,

Widhiarso³

2020

IJTech

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This study compared swarm-based algorithms in terms of their effectiveness in improving the design of facilities in container terminals (CTs). The design was conducted within the framework of stochastic discrete optimization and involved determining the number of equipment needed in CTs by considering variations in demand and the productivity of facilities-issues that are rarely elaborated in CT design. Variations were identified via Monte Carlo simulation characterized by a particular distribution. The conflicting issue due to increments in equipment investment that possibly cause the distribution delays was also modeled, specifically in relation to the increasing number of trucks used in terminals. Given that the optimization problem is typified by numerous combinations of actions, the swarm-based algorithms were deployed to develop a feasible solution. A new variant of glowworm swarm optimization (GSO) was then proposed and compared with particle swarm optimization (PSO) algorithms. The numerical results showed that the performance of the proposed GSO is superior to that of PSO algorithms.

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“…• Supervised learning for spectrum sensing [262], channel estimation, channel selection [264], MAC protocol selection [265], learning and classification of PU behaviors [266], spectrum sharing [267], optimal resource allocation [268], PU boundary detection [269], etc.…”

Section: B2)mentioning

confidence: 99%

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Piran¹

2020

Preprint

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The stringent requirements of wireless multimedia transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the issue with spectrum availability is not scarcity, but the inefficient utilization. Unique characteristics of cognitive radio (CR) such as flexibility, adaptability, and interoperability, particularly have contributed to it being the optimum technological candidate to alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and loss-tolerant service, exposes several severe challenges specially to guarantee quality of service (QoS) and quality of experience (QoE). As a result, to date, different schemes based on source and channel coding, multicast, and distributed streaming, have been examined to improve the QoS/QoE in MCRNs. In this paper, we survey QoS/QoE provisioning schemes in MCRNs. We first discuss the basic concepts of multimedia communication, CRNs, QoS and QoE. Then, we present the advantages of utilizing CR for multimedia services and outline the stringent QoS and QoE requirements in MCRNs. Next, we classify the critical challenges for QoS/QoE provisioning in MCRNs including spectrum sensing, resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for each challenge highlighting performance issues, strengths, and weaknesses. Furthermore, we discuss several important open research problems and provide some avenues for future research.

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A bio-inspired swarm intelligence technique for social aware cognitive radio handovers

Cited by 321 publications

References 22 publications

Spectrum Allocation in Cognitive Radio: A PSO-based Approach

Spectrum Allocation in Cognitive Radio: A PSO-based Approach

A Comparative Study on Swarm-based Algorithms to Solve the Stochastic Optimization Problem in Container Terminal Design

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

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