Traffic Load Prediction and Power Consumption Reduction for Multi-band Networks
Energy is a major expense issue for mobile operators. In the case of wireless networks, base stations have been identified as the main source of energy consumption. In this paper, we study the energy consumption reduction problem based on real measurements for a commercial multi-band LTE network. Sp...
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Zusammenfassung: | Energy is a major expense issue for mobile operators. In the case of wireless
networks, base stations have been identified as the main source of energy
consumption. In this paper, we study the energy consumption reduction problem
based on real measurements for a commercial multi-band LTE network.
Specifically, we are interested in sleep modes to turn off certain frequency
bands during low traffic periods and consequently reduce power consumption. We
determine the number of frequency bands really needed at each time period. The
frequency bands that are not needed can be disabled to reduce energy
consumption. In order to allow the operator to predict how many bands can be
switched off without major impact on the quality of service, we propose to use
a deep learning algorithm, such as Long-Short Term Memory (LSTM). Based on the
captured data traces, we have shown that the proposed LSTM model can save an
average of 8% to 21% of the energy consumption during working days. |
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DOI: | 10.48550/arxiv.2312.11958 |