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<p>With the accelerated rollout of 5G networks comes a dramatic explosion of data rates for numerous applications, accompanied by explosive energy consumption in User Equipment (UE). This paper proposes AI-DRX: an artificial intelligence (AI) powered Dynamic Discontinuous Reception (DRX) mechanism for energy saving in 5G networks. We present a data-driven approach that utilizes Convolutional Neural Networks (CNNs) specifically optimized for real-time operation on mobile devices to model the complex relationship between DRX parameters, network conditions, and user behavior patterns. The proposed AI-DRX mechanism optimizes DRX parameters according to real-time conditions, enabling significant energy savings with minimal QoS degradation.</p><p>Using comprehensive evaluation with real wireless traffic traces from the MONROE dataset, the AI-DRX mechanism demonstrates energy savings of 69.2% and 55.8% compared to traditional LTE-DRX across different network scenarios, and 70% improvement over Poisson packet arrival models. The CNN model achieves 91.8% prediction accuracy with inference latency of only 2.3ms on modern mobile hardware, making real-time deployment feasible. These results emphasize the promising impact of AI-DRX on dramatically increasing energy efficiency in 5G-powered devices, marking it as an integral component for ensuring sustainable performance of next-generation mobile networks.</p>

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Enhancing energy efficiency in 5G networks through AI-driven dynamic discontinuous reception

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月30日

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  • Hammad Lazrek,
  • Hassan El Ferindi,
  • Mohammed Zouiten,
  • Aniss Moumen

摘要

With the accelerated rollout of 5G networks comes a dramatic explosion of data rates for numerous applications, accompanied by explosive energy consumption in User Equipment (UE). This paper proposes AI-DRX: an artificial intelligence (AI) powered Dynamic Discontinuous Reception (DRX) mechanism for energy saving in 5G networks. We present a data-driven approach that utilizes Convolutional Neural Networks (CNNs) specifically optimized for real-time operation on mobile devices to model the complex relationship between DRX parameters, network conditions, and user behavior patterns. The proposed AI-DRX mechanism optimizes DRX parameters according to real-time conditions, enabling significant energy savings with minimal QoS degradation.

Using comprehensive evaluation with real wireless traffic traces from the MONROE dataset, the AI-DRX mechanism demonstrates energy savings of 69.2% and 55.8% compared to traditional LTE-DRX across different network scenarios, and 70% improvement over Poisson packet arrival models. The CNN model achieves 91.8% prediction accuracy with inference latency of only 2.3ms on modern mobile hardware, making real-time deployment feasible. These results emphasize the promising impact of AI-DRX on dramatically increasing energy efficiency in 5G-powered devices, marking it as an integral component for ensuring sustainable performance of next-generation mobile networks.