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Energy-efficient Functional Split in Non-terrestrial Open Radio Access Networks

S. M. Mahdi Shahabi, Xiaonan Deng, Ahmad Qidan, Taisir Elgorashi, Jaafar Elmirghani

TL;DR

A novel framework utilizing a Deep Q-Network (DQN)-based reinforcement learning approach to dynamically find the optimal RAN functional split option and the best NTN-based RAN network out of the available NTN-platforms according to real-time conditions, traffic demands, and limited energy resources in NTN platforms is introduced.

Abstract

This paper investigates the integration of Open Radio Access Network (O-RAN) within non-terrestrial networks (NTN), and optimizing the dynamic functional split between Centralized Units (CU) and Distributed Units (DU) for enhanced energy efficiency in the network. We introduce a novel framework utilizing a Deep Q-Network (DQN)-based reinforcement learning approach to dynamically find the optimal RAN functional split option and the best NTN-based RAN network out of the available NTN-platforms according to real-time conditions, traffic demands, and limited energy resources in NTN platforms. This approach supports capability of adapting to various NTN-based RANs across different platforms such as LEO satellites and high-altitude platform stations (HAPS), enabling adaptive network reconfiguration to ensure optimal service quality and energy utilization. Simulation results validate the effectiveness of our method, offering significant improvements in energy efficiency and sustainability under diverse NTN scenarios.

Energy-efficient Functional Split in Non-terrestrial Open Radio Access Networks

TL;DR

A novel framework utilizing a Deep Q-Network (DQN)-based reinforcement learning approach to dynamically find the optimal RAN functional split option and the best NTN-based RAN network out of the available NTN-platforms according to real-time conditions, traffic demands, and limited energy resources in NTN platforms is introduced.

Abstract

This paper investigates the integration of Open Radio Access Network (O-RAN) within non-terrestrial networks (NTN), and optimizing the dynamic functional split between Centralized Units (CU) and Distributed Units (DU) for enhanced energy efficiency in the network. We introduce a novel framework utilizing a Deep Q-Network (DQN)-based reinforcement learning approach to dynamically find the optimal RAN functional split option and the best NTN-based RAN network out of the available NTN-platforms according to real-time conditions, traffic demands, and limited energy resources in NTN platforms. This approach supports capability of adapting to various NTN-based RANs across different platforms such as LEO satellites and high-altitude platform stations (HAPS), enabling adaptive network reconfiguration to ensure optimal service quality and energy utilization. Simulation results validate the effectiveness of our method, offering significant improvements in energy efficiency and sustainability under diverse NTN scenarios.
Paper Structure (12 sections, 16 equations, 7 figures, 3 tables)

This paper contains 12 sections, 16 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Formulation
  4. Power Consumption Calculation
  5. Latency Calculation
  6. RAN functional Split Optimization Problem
  7. DQN-based method
  8. State Set
  9. Action Set
  10. Reward
  11. Numerical Analysis
  12. Conclusion

Figures (7)

  • Figure 1: The overall NTN architecture
  • Figure 2: The trade-off between the data rate and the latency for RAN functional split options introduced by murti2020optimal
  • Figure 3: Daily traffic pattern in a residential area and a business area for weekdays based on measurements in marsan2013towards.
  • Figure 4: ResNet-aided QNN structure
  • Figure 5: Normalized power vs time step for a business area
  • ...and 2 more figures