Energy-efficient Deep Reinforcement Learning-based Network Function Disaggregation in Hybrid Non-terrestrial Open Radio Access Networks

TL;DR

This paper presents a deep reinforcement learning framework to optimize network function disaggregation in non-terrestrial 5G networks, enhancing energy efficiency and adapting to dynamic conditions.

Contribution

It introduces a novel DQN-based approach for dynamic RAN split optimization tailored for NTN platforms, addressing energy efficiency and real-time adaptation.

Findings

The proposed method improves energy efficiency in NTN-based RANs.

It dynamically adapts to traffic and network conditions for optimal resource allocation.

Numerical results show effective real-time functional split selection.

Abstract

This paper explores the integration of Open Radio Access Network (O-RAN) principles with non-terrestrial networks (NTN) and investigates the optimization of the functional split between Centralized Units (CU) and Distributed Units (DU) to improve energy efficiency in dynamic network environments. Given the inherent constraints of NTN platforms, such as Low Earth Orbit (LEO) satellites and high-altitude platform stations (HAPS), we propose a reinforcement learning-based framework utilizing Deep Q-Network (DQN) to intelligently determine the optimal RAN functional split. The proposed approach dynamically adapts to real-time fluctuations in traffic demand, network conditions, and power limitations, ensuring efficient resource allocation and enhanced system performance.The numerical results demonstrate that the proposed policy effectively adapts to network traffic flow by selecting an efficient network disaggregation strategy and corresponding functional split option based on data rate and latency requirements.