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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A ML-Based Resource Allocation Scheme for Energy Optimization in 5G NR.

Xiao Yao1, Antonio Pérez Yuste1

  • 1ETSI Sistemas de Telecomunicación, Universidad Politécnica de Madrid, 28031 Madrid, Spain.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning framework for optimizing 5G New Radio energy consumption. It reduces energy use by over 40% while ensuring quality of service through predictive resource management.

Keywords:
5G RANRRCenergy efficiencymachine learningresource allocation

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Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • 5G New Radio (5G NR) networks face increasing energy demands.
  • Efficient energy management is crucial for sustainable network operation.
  • Maintaining Quality of Service (QoS) alongside energy savings presents a significant challenge.

Purpose of the Study:

  • To propose and validate a machine learning-based energy optimization framework for 5G NR.
  • To reduce energy consumption in 5G NR base stations.
  • To ensure that energy reduction strategies do not compromise essential QoS parameters.

Main Methods:

  • Utilizing a Classification and Regression Tree (CART) algorithm for predictive load forecasting.
  • Implementing dynamic cell resource reconfiguration based on predicted network load.
  • Simulating an inter-band NR-NR Dual Connectivity (DC) network layout for validation.

Main Results:

  • Achieved a 42.3% reduction in energy consumption.
  • Maintained Quality of Service (QoS) parameters within 3rd Generation Partnership Project (3GPP) specified thresholds.
  • Quantitatively validated the proposed model through a simulated network case study.

Conclusions:

  • The proposed machine learning framework effectively optimizes energy consumption in 5G NR networks.
  • Dynamic cell resource reconfiguration driven by predictive load forecasting is a viable strategy for energy savings.
  • The CART algorithm provides a robust method for achieving significant energy reductions without sacrificing network performance.