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http://dx.doi.org/10.6109/jkiice.2022.26.4.633

Adaptive Antenna Muting using RNN-based Traffic Load Prediction  

Ahmadzai, Fazel Haq (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Lee, Woongsup (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.4, 2022 , pp. 633-636 More about this Journal
Abstract
The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.
Keywords
Power consumption; traffic load; prediction; recurrent neural network; antenna muting;
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