Abstract
The 5G network services require Quality of Service (QoS) features such as user mobility, higher data rates, highly reliable communication, and lower latency. To achieve these features integration of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) is generally required. To achieve similar performance in a MIMO-based system a better and energy-efficient algorithm is required, which can avoid the use of NFV and SDN. This work focuses on a novel approach of converging moth search and dragonfly algorithms to obtain a highly efficient 5G network. The signal-to-noise ratio (SNR) obtained was about -25dB, which was better than most techniques reported in the literature. We expect this algorithm to be implemented in QoS-driven green power allocation system.
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Authors would like to thank colleagues from Pacific Academy of Higher Education and Research University.
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Authors S. Yadav, S. Nanivadekar, and B. Vyas declare that they have no conflict of interest.
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Conceptualization was done by S. Yadav (SY), S. NaniVadekar (SN), and B. Vyas (BV). All the simulations were performed by SY. Manuscript writing—original draft preparation SY and SN. Review and editing were SY and SN. Visualization work carried out by SY.
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Yadav, S., Nanivadekar, S., Vyas, B.M. (2023). Hybrid Moth Search and Dragonfly Algorithm for Energy-Efficient 5G Networks. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_19
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DOI: https://doi.org/10.1007/978-981-99-0047-3_19
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