Implementation of Enhanced Chimp Optimization Algorithm in Cognitive Radio Networks for Vehicular Mobile Communication

Q3 Engineering
S. Vidhya, Hamza Mohammed Ridha Al-Khafaji, M. Sathya Priya, Bolganay Kaldarova, C.M. Velu, K. Bhavana Raj, Zhanar Togzhanova
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引用次数: 0

Abstract

In recent years, 6G technology has been extended to many different applications, especially mobile communications. As a result, the volume of mobile data increases, which poses a problem with the load on the plane of control (IoE, IoT). This problem is solved by efficient use of resources and reduced power consumption in cognitive radio networks (CRNs). In the literature, many methods have been developed by researchers to control spectrum sensing as well as energy -efficient operation, but they still need to be improved to improve system efficiency and processing power. Therefore, in this paper, an energy efficient method for Opposition Function -based Chimpanzee Optimization Algorithm (OFCOA) is developed in CRN for energy management as well as resource allocation. The proposed method is a combination of Opposition Function (OF) and Chimpanzee Optimization Algorithm (COA). In COAs, the optimal decision process is enhanced by the use of OF. The proposed method provides energy efficient operation in CRN through energy management taking into account spectrum measurements. The proposed method was tested under four Primary User (PU) and Secondary User (SU) conditions with channel occupation and CRN findings. The proposed methodology is implemented in MATLAB and performance is measured based on performance metrics such as processing power, network life, transmission rate, delay, flush, power consumption and overhead. The performance of the proposed methodology is compared with traditional methods such as Chimpanzee Optimization Algorithm (COA), Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO).
增强黑猩猩优化算法在车载移动通信认知无线网络中的实现
近年来,6G技术已经扩展到许多不同的应用,特别是移动通信。因此,移动数据量增加,这给控制平面(IoE, IoT)的负载带来了问题。认知无线网络(crn)通过有效利用资源和降低功耗来解决这一问题。在文献中,研究人员已经开发了许多方法来控制频谱传感和节能运行,但仍需要改进,以提高系统效率和处理能力。因此,本文提出了一种基于对立函数的黑猩猩优化算法(OFCOA)在CRN中的节能方法,用于能量管理和资源分配。该方法结合了反对函数(of)和黑猩猩优化算法(COA)。在coa中,利用of增强了最优决策过程。该方法通过考虑频谱测量的能量管理,实现了CRN的节能运行。在四种主用户(PU)和次用户(SU)条件下对该方法进行了信道占用和CRN结果的测试。提出的方法在MATLAB中实现,并根据处理能力、网络寿命、传输速率、延迟、刷新、功耗和开销等性能指标对性能进行了测量。将该方法与传统的黑猩猩优化算法(COA)、鲸鱼优化算法(WOA)和粒子群优化算法(PSO)进行了性能比较。
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来源期刊
International Journal of Vehicle Structures and Systems
International Journal of Vehicle Structures and Systems Engineering-Mechanical Engineering
CiteScore
0.90
自引率
0.00%
发文量
78
期刊介绍: The International Journal of Vehicle Structures and Systems (IJVSS) is a quarterly journal and is published by MechAero Foundation for Technical Research and Education Excellence (MAFTREE), based in Chennai, India. MAFTREE is engaged in promoting the advancement of technical research and education in the field of mechanical, aerospace, automotive and its related branches of engineering, science, and technology. IJVSS disseminates high quality original research and review papers, case studies, technical notes and book reviews. All published papers in this journal will have undergone rigorous peer review. IJVSS was founded in 2009. IJVSS is available in Print (ISSN 0975-3060) and Online (ISSN 0975-3540) versions. The prime focus of the IJVSS is given to the subjects of modelling, analysis, design, simulation, optimization and testing of structures and systems of the following: 1. Automotive vehicle including scooter, auto, car, motor sport and racing vehicles, 2. Truck, trailer and heavy vehicles for road transport, 3. Rail, bus, tram, emerging transit and hybrid vehicle, 4. Terrain vehicle, armoured vehicle, construction vehicle and Unmanned Ground Vehicle, 5. Aircraft, launch vehicle, missile, airship, spacecraft, space exploration vehicle, 6. Unmanned Aerial Vehicle, Micro Aerial Vehicle, 7. Marine vehicle, ship and yachts and under water vehicles.
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