Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN

TL;DR

This paper presents an evolutionary deep reinforcement learning framework to enhance dynamic slice management in O-RAN, significantly improving resource allocation efficiency and service quality in next-generation wireless networks.

Contribution

It introduces a novel EDRL-based method for optimizing O-RAN slice management, accelerating learning and outperforming traditional DRL approaches.

Findings

Outperforms DRL baseline by 62.2% in simulation

Efficiently manages network slices in dynamic environments

Enhances QoS and SLA adherence in O-RAN networks

Abstract

The next-generation wireless networks are required to satisfy a variety of services and criteria concurrently. To address upcoming strict criteria, a new open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is being investigated as a critical strategy for ensuring network quality of service (QoS) in the face of changing circumstances. However, distinct network slices must be dynamically controlled to avoid service level agreement (SLA) variation caused by rapid changes in the environment. Therefore, this paper introduces a novel framework able to manage the network slices through provisioned resources intelligently. Due to diverse heterogeneous environments, intelligent machine learning approaches require sufficient exploration to handle the harshest situations in a wireless network and accelerate convergence. To solve this problem, a new solution is proposed based on evolutionary-based deep reinforcement learning (EDRL) to accelerate and optimize the slice management learning process in the radio access network's (RAN) intelligent controller (RIC) modules. To this end, the O-RAN slicing is represented as a Markov decision process (MDP) which is then solved optimally for resource allocation to meet service demand using the EDRL approach. In terms of reaching service demands, simulation results show that the proposed approach outperforms the DRL baseline by 62.2%.