Abstract
At present, the demand for natural energy has been ever increasing, so energy has become a major concern for everyone. As Long Term Evolution Base Stations consume a large amount of the total energy expenditure in a cellular network, it is of keen interest to researchers to reduce the energy consumed by BSs when considering network planning. In this paper, we consider the green radio network planning problem for the LTE cellular networks. Our aim is to reduce energy consumption by reducing the number of active BSs, which will also reduce the production of carbon dioxide. Now BSs are currently operated and deployed for the worst traffic peak estimates. However, traffic fluctuates with time depending on the mobile stations behavior and their data needs. From our point of view, in order to investigate more realistic cases, we consider the situation where the traffic information is taken as imprecise and uncertain value. So, we introduce a model of problem where each traffic is a fuzzy variable, and then, we present a decision-making model based on possibility theory. To solve the problem, we propose a solution method using genetic algorithms and a dynamic Evolved Node B switching on/off strategy. The obtained results showed the efficiency of our approach and demonstrated considerable energy saving, through dynamic adaptation of the number of active BSs.
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Dahmani, S., Gabli, M., Mermri, E.B. et al. Optimization of green RNP problem for LTE networks using possibility theory. Neural Comput & Applic 32, 3825–3838 (2020). https://doi.org/10.1007/s00521-018-3943-x
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DOI: https://doi.org/10.1007/s00521-018-3943-x