Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks

TL;DR

This paper introduces a distributed deep reinforcement learning framework for energy-efficient resource allocation in cloud-native O-RAN small cell networks, effectively balancing eMBB and URLLC requirements.

Contribution

It presents a novel transfer learning-based DRL approach for online, adaptive, and energy-efficient resource management in 5G small cell networks.

Findings

Rapid adaptation to dynamic network conditions.

Improved energy efficiency in resource allocation.

Effective handling of diverse user requirements.

Abstract

In the rapidly evolving landscape of 5G and beyond, cloud-native Open Radio Access Networks (O-RAN) present a paradigm shift towards intelligent, flexible, and sustainable network operations. This study addresses the intricate challenge of energy efficient (EE) resource allocation that services both enhanced Mobile Broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users. We propose a novel distributed learning framework leveraging on-policy and off-policy transfer learning strategies within a deep reinforcement learning (DRL)--based model to facilitate online resource allocation decisions under different channel conditions. The simulation results explain the efficacy of the proposed method, which rapidly adapts to dynamic network states, thereby achieving a green resource allocation.