Abstract
The penetration of multitude smart devices and billions of Internet of Things (IoT) devices have demanded enhanced data speed and diversified network services. The 5th Generation cellular communication has already been rolled out in many countries. Naturally, 5G and Beyond 5G (B5G) networks have to accommodate variety of services with an exponentially large number of devices. 100% coverage is one of the goals of 5G network. To meet this demand dense small cells are proposed. This places excessive stress on the network service provider in terms of capital and operational expenditure. Moreover, as the coverage area is small, most Base Stations (BS) will be serving a fewer devices. Thus, the energy efficiency per BS will be reduced drastically. In this sense, location intelligence based on energy saving is an important research topic. In this paper, we present a Genetic Algorithm (GA) approach, and its application in estimating the best location for 5G base stations reducing overall energy consumption. Our simulation results show better energy efficiency than the legacy procedures.
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Sachan, R., Dash, S., Sahu, B.J.R. (2022). Energy Efficient Base Station Location Optimization for Green B5G Networks. In: Mallick, P.K., Bhoi, A.K., González-Briones, A., Pattnaik, P.K. (eds) Electronic Systems and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 860. Springer, Singapore. https://doi.org/10.1007/978-981-16-9488-2_41
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DOI: https://doi.org/10.1007/978-981-16-9488-2_41
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