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

TL;DR

This paper presents a reinforcement learning framework for dynamically optimizing the functional split in non-terrestrial open radio access networks to improve energy efficiency and adaptability across various NTN platforms.

Contribution

It introduces a DQN-based method for real-time optimization of RAN functional split and platform selection in NTN, enhancing energy efficiency and network adaptability.

Findings

Significant energy efficiency improvements demonstrated in simulations.

Effective adaptation to diverse NTN scenarios and platforms.

Enhanced network sustainability and service quality.

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.