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Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs.

Wan Nur Suryani Firuz Wan Ariffin1, Xinruo Zhang2, Mohammad Reza Nakhai3

  • 1Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Arau 02600, Malaysia.

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Summary
This summary is machine-generated.

This study introduces an online learning algorithm for Cloud Radio Access Networks (Cloud-RANs) to manage electricity demand variability. The approach minimizes energy costs by prescheduling energy packages through learning from past energy trading and exploring new strategies.

Keywords:
cloud radio access networkcombinatorial multi-armed banditenergy tradingonline learning

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Area of Science:

  • Telecommunications Engineering
  • Energy Systems Management
  • Machine Learning Applications

Background:

  • Electricity demand variability and uncertainty impact both power generation and cellular communication systems.
  • Cloud Radio Access Networks (Cloud-RANs) face challenges in maintaining cost-aware and reliable operations due to these uncertainties.

Purpose of the Study:

  • To develop an online learning algorithm for prescheduling energy procurement in Cloud-RANs.
  • To manage variability and uncertainty for cost-efficient and reliable network operation.

Main Methods:

  • Development of a combinatorial multi-armed bandit model for energy scheduling.
  • Implementation of an algorithm that learns from past cooperative energy trading and explores new scheduling strategies.
  • Minimization of long-term energy costs at remote radio heads.

Main Results:

  • The proposed algorithm effectively controls available power budgets.
  • Significant reduction in overall energy costs demonstrated through simulations.
  • Outperforms recently proposed approaches for real-time energy resources and trading in Cloud-RANs.

Conclusions:

  • The developed online learning algorithm offers a robust solution for energy management in Cloud-RANs.
  • The approach successfully balances cost minimization with reliable network operation.
  • Enables efficient utilization of ancillary energy markets for future time slots.