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Related Experiment Video

Updated: Jul 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Optimal Power Procurement for Green Cellular Wireless Networks Under Uncertainty and Chance Constraints.

Nadhir Ben Rached1, Shyam Mohan Subbiah Pillai2, Raúl Tempone2,3

  • 1Department of Statistics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK.

Entropy (Basel, Switzerland)
|March 28, 2025
PubMed
Summary

This study presents a new method for cellular networks to optimize energy procurement, reducing costs and carbon emissions while ensuring service quality. The approach effectively handles unpredictable renewable energy sources and network demands.

Keywords:
Lagrangian relaxationchance constraintsdynamic programmingstochastic optimal controlwireless networks

Related Experiment Videos

Last Updated: Jul 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • * Energy Systems Engineering
  • * Wireless Communications
  • * Operations Research

Background:

  • * Increasing global demand for sustainable energy and rising energy needs of cellular networks.
  • * Challenges in managing hybrid energy systems with uncertain renewable sources and maintaining Quality of Service (QoS).
  • * Limitations of traditional dynamic programming for stochastic optimal control problems with probabilistic constraints.

Purpose of the Study:

  • * To develop an optimal short-term, continuous-time power procurement schedule for cellular networks.
  • * To minimize operating expenditure and carbon footprint.
  • * To ensure a high probability of meeting Quality of Service (QoS) constraints.

Main Methods:

  • * Introduction of a novel time-continuous Lagrangian relaxation approach.
  • * Utilizing an efficient upwind finite-difference solver for the Hamilton-Jacobi-Bellman equation.
  • * Employing Limited Memory Bundle Method (LMBM) and Stochastic Subgradient Method (SSM) for optimization.

Main Results:

  • * Demonstrated computational efficiency of the proposed numerical approach.
  • * Achieved a near-optimal energy procurement policy within a practical timeframe.
  • * Validated using German power system and cellular traffic data.

Conclusions:

  • * The proposed Lagrangian relaxation method effectively addresses tractability issues with probabilistic QoS constraints.
  • * The numerical solution provides an efficient and practical means for real-time energy procurement in cellular networks.
  • * The approach contributes to sustainable energy management in telecommunications.