Development of Enhanced Chimp Optimization Algorithm (OFCOA) in Cognitive Radio Networks for Energy Management and Resource Allocation

Transmit time and power optimisation increase secondary network energy efficiency (EE). The optimum resource allocation strategy in cognitive radio networks is the enhanced chimp optimisation algorithm (OFCOA) since the EE maximising problem is a nonlinear fractional programming problem. To control...

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Bibliographic Details
Published in:International journal of software science and computational intelligence Vol. 15; no. 1; pp. 1 - 20
Main Authors: Chui, Kwok Tai, Saini, Dilip Kumar Jang Bahadur, Mishra, Anupama, Siddharth, Dhirendra, Joshi, Pooja, Bansal, Ritika, Bansal, Shavi
Format: Journal Article
Language:English
Published: Hershey IGI Global 10.01.2024
Subjects:
Algorithms
Chimpanzees
Cognitive radio
Communication
Communications networks
Data transmission
Energy efficiency
Energy management
Mathematical programming
Measurement techniques
Monkeys & apes
Network latency
Optimization algorithms
Particle swarm optimization
Power consumption
Radio networks
Resource allocation
Sensors
Software
Telecommunications systems
ISSN:1942-9045, 1942-9037
Online Access:Get full text
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Summary:Transmit time and power optimisation increase secondary network energy efficiency (EE). The optimum resource allocation strategy in cognitive radio networks is the enhanced chimp optimisation algorithm (OFCOA) since the EE maximising problem is a nonlinear fractional programming problem. To control resources and energy, this research offers an energy-efficient CRN opposition function-based chimpanzee optimisation algorithm (OFCOA) solution. Combining the opposition function (OF) with the chimpanzee optimisation technique is recommended. OF in COAs improves decision-making. Spectrum measurements in energy management provide energy-efficient CRN operation. The suggested technique was evaluated using channel occupancy, CRN data, and four major and secondary user scenarios. CPU power, network life, transmission rate, latency, flush, power consumption, and overhead are utilized to evaluate the proposed approach in MATLAB. The proposed method is compared to existing approaches like Particle Swarm Optimisation (PSO), Chimpanzee Optimisation Algorithm (COA), and Whale Optimisation Algorithm.
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ISSN:1942-9045
1942-9037
DOI:10.4018/IJSSCI.335898