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, Hairong Jia School of Electronic Information and Optical Engineer, Taiyuan University of Technology , 209, University Street, Yuci District, Jinzhong, Shanxi 030600 , China Corresponding author: jiahairong@tyut.edu.cn Search for other works by this author on: Oxford Academic Suying Wang School of Electronic Information and Optical Engineer, Taiyuan University of Technology , 209, University Street, Yuci District, Jinzhong, Shanxi 030600 , China Search for other works by this author on: Oxford Academic Zelong Ren School of Electronic Information and Optical Engineer, Taiyuan University of Technology , 209, University Street, Yuci District, Jinzhong, Shanxi 030600 , China Search for other works by this author on: Oxford Academic
https://orcid.org/0000-0001-6292-9171 The Computer Journal, Volume 67, Issue 6, June 2024, Pages 2246–2256, https://doi.org/10.1093/comjnl/bxae003
Published:
31 January 2024
Article history
Received:
27 August 2022
Revision received:
16 November 2023
Accepted:
28 December 2023
Published:
31 January 2024
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Hairong Jia, Suying Wang, Zelong Ren, CNN-LSTM Base Station Traffic Prediction Based On Dual Attention Mechanism and Timing Application, The Computer Journal, Volume 67, Issue 6, June 2024, Pages 2246–2256, https://doi.org/10.1093/comjnl/bxae003
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Abstract
Energy consumption in 5G base stations remains consistently high, even during periods of low traffic loads, thereby resulting in unnecessary inefficiencies. To address this problem, this paper presents a novel approach by proposing a convolutional neural network (CNN)-long short-term memory (LSTM) traffic prediction model with a dual attention mechanism, coupled with the particle swarm optimization k-means algorithm for intelligent switch timing. The proposed CNN-LSTM model leverages a dual channel attention mechanism to bolster key feature information for long-term traffic data predictions. Specifically, a temporal attention mechanism is added to the LSTM to enhance the importance of temporal information. Moreover, the particle swarm optimization K-Means algorithm is proposed in order to cluster the traffic prediction results, output the corresponding time points of the lower traffic value and to obtain the optimal switch-off periods of the base station. Extensive experiments across multiple base stations over an extended period of time have validated our approach. The results show that this method offers accurate traffic prediction with minimal average errors in traffic prediction and the on/off timings of the base stations are in line with the “tide effect” of traffic, thereby achieving the goal of energy savings.
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