Energy-Saving Small-Cell Wake Up Strategy for Ultra-Dense Networks Based on User Behavior Prediction
Authors: 
Peng Long, Jin Li, Nan Liu, Zhiwen Pan, Xiaohu YouAuthors Info & Claims
Peng Long, Jin Li, Nan Liu, Zhiwen Pan, Xiaohu YouAuthors Info & ClaimsPages 253 - 258
Abstract
Ultra-dense networks (UDNs) is one of the key technologies that can offer large capacities with numerous small base stations (BSs) deployed. However, the BSs consume a lot of energy when they are all turned on. To save energy, small cells with zero or low load should be in sleep mode. In this paper, we propose a small cell wake-up strategy based on the mobile application usage prediction of the users. First, LSTM neural network model is used to predict the users’ application usage in the next interval and the training data is from a real-world anonymous datasets. The small BS will be woken up when it is predicted that one or more users in its coverage area will use high data-rate applications in the next time period. Numerical results show that the LSTM method achieves higher prediction precision and recall compared with the other prediction algorithms. Employing our scheme, we can get about 14% gain in energy consumption compared to the energy efficient system where the small cell is woken up based on the predicted number of users in its coverage area.
References
[1]
2018. Predict what app to use next time only consider time and latest used app context. In the 2018 2nd International Conference.
[2]
Imran Ashraf, Federico Boccardi, and Lester Ho. 2011. SLEEP mode techniques for small cell deployments. IEEE Communications Magazine 49, 8 (2011), 72–79. https://doi.org/10.1109/MCOM.2011.5978418
[3]
Y. Bengio, P. Simard, and P. Frasconi. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks 5, 2 (1994), 157–166. https://doi.org/10.1109/72.279181
[4]
Tmt Do and D Gatica-Perez. 2014. Where and what: Using smartphones to predict next locations and applications in daily life. Pervasive & Mobile Computing 12 (2014), 79–91.
[5]
S. Hochreiter and J. Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735–1780.
[6]
Nandish P. Kuruvatti, Andreas Klein, and Hans D. Schotten. 2015. Prediction of Dynamic Crowd Formation in Cellular Networks for Activating Small Cells. In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring). 1–5. https://doi.org/10.1109/VTCSpring.2015.7146028
[7]
Haris Pervaiz, Oluwakayode Onireti, Abdelrahim Mohamed, Muhammad Ali Imran, Rahim Tafazolli, and Qiang Ni. 2018. Energy-Efficient and Load-Proportional eNodeB for 5G User-Centric Networks: A Multilevel Sleep Strategy Mechanism. IEEE Vehicular Technology Magazine 13, 4 (2018), 51–59. https://doi.org/10.1109/MVT.2018.2871740
[8]
G. Tsoumakas, I. Katakis, and I. Vlahavas. 2009. Mining Multi-label Data. Data Mining and Knowledge Discovery Handbook.
[9]
Hao Wu, Hui Tian, Zhaolong Huang, and Gaofeng Nie. 2018. User location prediction based cell discovery scheme for user-centric ultra-dense networks. In 2018 IEEE Wireless Communications and Networking Conference (WCNC). 1–6. https://doi.org/10.1109/WCNC.2018.8377120
[10]
Jingjin Wu, Yujing Zhang, Moshe Zukerman, and Edward Kai-Ning Yung. 2015. Energy-Efficient Base-Stations Sleep-Mode Techniques in Green Cellular Networks: A Survey. IEEE Communications Surveys Tutorials 17, 2 (2015), 803–826. https://doi.org/10.1109/COMST.2015.2403395
[11]
Fengli Xu, Yuyun Lin, Jiaxin Huang, Di Wu, Hongzhi Shi, Jeungeun Song, and Yong Li. 2016. Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach. IEEE Transactions on Services Computing 9, 5 (2016), 796–805. https://doi.org/10.1109/TSC.2016.2599878
[12]
Huan Yan, Tzu-Heng Lin, Ming Zeng, Jing Wu, Jiaxin Huang, Yong Li, and Depeng Jin. 2017. Characterizing the Usage of Mobile Video Service in Cellular Networks. In GLOBECOM 2017 - 2017 IEEE Global Communications Conference. 1–6. https://doi.org/10.1109/GLOCOM.2017.8254136
[13]
Wenjing Zhang, Yuan Liu, Tingting Liu, and Chenyang Yang. 2018. Trajectory Prediction with Recurrent Neural Networks for Predictive Resource Allocation. In 2018 14th IEEE International Conference on Signal Processing (ICSP). 634–639. https://doi.org/10.1109/ICSP.2018.8652460
Index Terms
- Energy-Saving Small-Cell Wake Up Strategy for Ultra-Dense Networks Based on User Behavior Prediction
Recommendations
Ambient RF energy harvesting in ultra-dense small cell networks: performance and trade-offs
In order to minimize electric grid power consumption, energy harvesting from ambient RF sources is considered as a promising technique for wireless charging of low-power devices. To illustrate the design considerations of RF-based ambient energy ...
Saving energy during channel contention in 802.11 WLANs
We focus on energy saving in 802.11-based WLANs. Previous work has shown that, on the one hand, 802.11 wireless interfaces consume a significant amount of energy, on the other hand the use of current power management schemes can severely degrade the QoS ...
Improving energy-efficient communications with a battery lifetime-aware mechanism in IEEE802.16e wireless networks
Green network communication has recently received attention because of its economic and environmentally friendly benefits. Energy consumption significantly affects mobile subscriber stations in wireless broadband access networks. Efficient energy saving ...
Comments
Information & Contributors
Information
Published In
May 2023
1025 pages
ISBN:9798400700705
DOI:10.1145/3603781
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 July 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CNIOT'23
CNIOT'23: 2023 4th International Conference on Computing, Networks and Internet of Things
May 26 - 28, 2023
Xiamen, China
Acceptance Rates
Overall Acceptance Rate 39 of 82 submissions, 48%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 24Total Downloads
- Downloads (Last 12 months)8
- Downloads (Last 6 weeks)1
Reflects downloads up to 25 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format