Swarm intelligence based optimization of energy consumption in cognitive radio network

Gogoi, Ashim Jyoti; Choudhury, Hussain Ahmed; Baishnab, Krishna Lal

doi:10.3233/jifs-169951

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…After comparing the two algorithms, it was determined that the max feeding approach provides the best routes with the fewest handoffs. Gogoi et al [ 18 ] projected optimizing energy usage by swarm-based intelligence algorithms such as the whale optimization algorithm, human behavior-based particle swarm optimization, particle swarm optimization with an aging leader and challengers, and particle swarm optimization (PSO). Swarm intelligence deals with combined information about artificial and natural systems.…”

Section: Literature Survey and Problem Statementmentioning

confidence: 99%

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Srivastava

Singh

Malik

et al. 2023

Sensors

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A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed “support vector machine-based red deer algorithm” (SVM-RDA) is resilient and has low complexity. The suggested algorithm’s experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three.

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Section: Literature Survey and Problem Statementmentioning

confidence: 99%

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Srivastava

Singh

Malik

et al. 2023

Sensors

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“…This algorithm can detect spectrum holes with optimal transmission power, sensing bandwidth and power spectral density, thus improving the energy efficiency of spectrum sensing. References [14][15][16][17][18][19][20][21][22][23] have conducted various improvement studies on the PSO algorithm to enhance the PSO algorithm's capacity for optimization and convergence.…”

Section: Previous Studiesmentioning

confidence: 99%

A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO

Zhao

Zhou

2022

Sensors

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The use of a cognitive radio power allocation algorithm is an effective method to improve spectral utilization. However, there are three problems with traditional cognitive radio power allocation algorithms: (1) based on the ideal channel model analysis, channel fluctuation is not considered; (2) they do not consider fairness among cognitive users; and (3) some algorithms are complex and locating the optimal power allocation scheme is not an easy task. For the above problems, this study establishes a robust model which adds the cognitive user transmission rate variance constraint to solve the maximum channel capacity time power allocation scheme by considering the worst-case channel transmission model, and finally solves this complex non-convex optimization problem by using the hybrid particle swarm algorithm. Simulation results show that the algorithm has good robustness, improves the fairness among the cognitive users, makes full use of the channel resources under the constraints, and has a simple algorithm, fast convergence, and good optimization results.

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“…A number of papers address optimization in Vehicular Ad hoc Network (VANET) [45], cognitive radio networks [46], wireless sensor network [47], and Edge Computing [48]. Paper [45] surveys routing algorithms among vehicles and presents a mobicast routing version using a genetic algorithm to establish effective communication among nodes, with great improvement in the performance.…”

mentioning

confidence: 99%

“…Paper [45] surveys routing algorithms among vehicles and presents a mobicast routing version using a genetic algorithm to establish effective communication among nodes, with great improvement in the performance. The work in [46] describes an energy efficient multi-relay cognitive radio network with focus on optimization of energy consumed during data transmission using techniques like Particle Swarm Optimization (PSO), Particle Swarm Optimization with Aging Leader and Challengers (ALCPSO), Human behavior based Particle Swarm Optimization (HPSO) and Whale Optimization Algorithm (WOA). Another intelligent clustering technique is proposed in [47] to choose the optimal position of multiple base stations in wireless sensor network to increase the lifetime with limited energy resources.…”

mentioning

confidence: 99%

Soft computing and intelligent systems: techniques and applications

Thampi

El-Alfy

2019

IFS

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Swarm intelligence based optimization of energy consumption in cognitive radio network

Cited by 4 publications

References 15 publications

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO

Soft computing and intelligent systems: techniques and applications

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