Improved Connected-Mode Discontinuous Reception (C-DRX) Power Saving and Delay Reduction Using Ensemble-Based Traffic Prediction

Ji-Hee Yu, Yoon-Ju Choi, Seung-Hwan Seo, Seong-Gyun Choi, Hye-Yoon Jeong, Ja-Eun Kim, Myung-Sun Baek, Young-Hwan You and Hyoung-Kyu Song ()
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Ji-Hee Yu: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Yoon-Ju Choi: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Seung-Hwan Seo: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Seong-Gyun Choi: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Hye-Yoon Jeong: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Ja-Eun Kim: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Myung-Sun Baek: Department of Electrical Engineering, Sejong University, Seoul 05006, Republic of Korea
Young-Hwan You: Department of Computer Engineering, Sejong University, Seoul 05006, Republic of Korea
Hyoung-Kyu Song: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2025, vol. 13, issue 6, 1-18

Abstract: This paper proposes a traffic prediction-based connected-mode discontinuous reception (C-DRX) approach to enhance energy efficiency and reduce data transmission delay in mobile communication systems. Traditional C-DRX determines user equipment (UE) activation based on a fixed timer cycle, which may not align with actual traffic occurrences, leading to unnecessary activation and increased energy consumption or delays in data reception. To address this issue, this paper presents an ensemble model combining random forest (RF) and a temporal convolutional network (TCN) to predict traffic occurrences and adjust C-DRX activation timing. RF extracts traffic features, while TCN captures temporal dependencies in traffic data. The predictions from both models are combined to determine C-DRX activation timing. Additionally, the extended activation approach is introduced to refine activation timing by extending the activation window around predicted traffic occurrences. The proposed method is evaluated using real-world Netflix traffic data, achieving a 20.9% decrease in unnecessary active time and a 70.7% reduction in mean delay compared to the conventional periodic C-DRX approach. Overall, the proposed method significantly enhances energy efficiency and quality of service (QoS) in LTE and 5G networks, making it a viable solution for future mobile communication systems.

Keywords: C-DRX (connected-mode discontinuous reception); deep learning; energy efficiency; ensemble learning; machine learning; mobile communication; random forest; temporal convolutional network; traffic prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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