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

D. K. Saini, Anupama Mishra, Dhirendra Siddharth, Pooja Joshi, Ritika Bansal, Shavi Bansal, Kwok Tai Chui
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Abstract

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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在认知无线电网络中开发用于能源管理和资源分配的增强型 Chimp 优化算法 (OFCOA)
传输时间和功率优化可提高二次网络能源效率(EE)。认知无线电网络中的最佳资源分配策略是增强黑猩猩优化算法(OFCOA),因为 EE 最大化问题是一个非线性分数编程问题。为了控制资源和能源,本研究提供了一种基于对立函数的高能效认知无线电网络黑猩猩优化算法(OFCOA)解决方案。建议将反对函数(OF)与黑猩猩优化技术相结合。COA 中的反对函数可改善决策。能源管理中的频谱测量可提供高能效的 CRN 运行。利用信道占用率、CRN 数据以及四个主要和次要用户场景对建议的技术进行了评估。在 MATLAB 中利用 CPU 功耗、网络寿命、传输速率、延迟、冲洗、功耗和开销对建议的方法进行了评估。提议的方法与粒子群优化(PSO)、黑猩猩优化算法(COA)和鲸鱼优化算法等现有方法进行了比较。
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