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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor-Critic Learning.

Shahram Mollahasani1, Turgay Pamuklu1, Rodney Wilson2

  • 1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada.

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Summary

This study introduces a new algorithm for Open Radio Access Networks (O-RAN) to optimize network function placement. The Soft Actor-Critic Energy-Aware Dynamic DU Selection (SA2C-EADDUS) algorithm significantly improves energy efficiency and reduces network delay.

Keywords:
O-RANRAN optimizationactor–critic learningenergy-efficiency

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Area of Science:

  • Telecommunications Engineering
  • Network Optimization
  • Artificial Intelligence in Networking

Background:

  • Open Radio Access Network (O-RAN) architecture enables flexibility and cost-effectiveness through openness and intelligence.
  • Virtualized network functions (NFs) in O-RAN, such as the Central Unit (O-CU) and Distributed Unit (O-DU), pose challenges in optimal placement due to propagation delay and computational capacity constraints.
  • Efficient resource allocation is critical for O-RAN performance, balancing latency and energy consumption.

Purpose of the Study:

  • To propose a novel algorithm, Soft Actor-Critic Energy-Aware Dynamic DU Selection (SA2C-EADDUS), for dynamic selection and placement of Distributed Units (DUs) in O-RAN.
  • To minimize both network delay and energy consumption in the O-RAN architecture.
  • To evaluate the performance of the proposed algorithm against optimization models and existing resource allocation techniques.

Main Methods:

  • Development of the SA2C-EADDUS algorithm, integrating two nested actor-critic agents for dynamic DU selection.
  • Formulation of an optimization model to minimize delay and energy consumption, solved using an MILP solver for a lower bound comparison.
  • Comparative analysis of SA2C-EADDUS against Reinforcement Learning (RL), Deep Reinforcement Learning (DRL) based algorithms, and a heuristic method.

Main Results:

  • The SA2C-EADDUS algorithm demonstrates significant improvements in energy efficiency, achieving a 50% enhancement compared to other schemes.
  • The proposed approach effectively reduces mean network delay through dynamic relocation of NFs based on service requirements.
  • Collaborative actor-critic agents across different layers contribute to enhanced performance and resource management.

Conclusions:

  • The SA2C-EADDUS algorithm offers a superior solution for dynamic DU selection in O-RAN, effectively addressing the trade-offs between delay and energy consumption.
  • The integration of nested actor-critic agents and dynamic NF relocation provides a novel and efficient approach to O-RAN resource management.
  • This research contributes to the advancement of intelligent and energy-efficient O-RAN deployments.