Attention-based Open RAN Slice Management using Deep Reinforcement Learning

TL;DR

This paper presents an attention-based deep reinforcement learning approach for managing network slices in Open RAN, improving decision reliability and network performance in dynamic 5G environments.

Contribution

It introduces a novel attention-based deep RL method with distributed agents and value-attention networks for enhanced slice management in O-RAN.

Findings

Significant performance improvements over baseline DRL methods.

Effective information extraction via attention mechanisms.

Enhanced decision-making reliability in dynamic network environments.

Abstract

As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing. Network slicing has emerged as a potential solution to address the different service requirements. However, managing network slices while maintaining quality of services (QoS) in dynamic environments is a challenging task. Utilizing machine learning (ML) approaches for optimal control of dynamic networks can enhance network performance by preventing Service Level Agreement (SLA) violations. This is critical for dependable decision-making and satisfying the needs of emerging networks. Although RL-based control methods are effective for real-time monitoring and controlling network QoS, generalization is necessary to improve decision-making reliability. This paper introduces an innovative attention-based deep RL (ADRL) technique that leverages the O-RAN disaggregated modules and distributed agent cooperation to achieve better performance through effective information extraction and implementing generalization. The proposed method introduces a value-attention network between distributed agents to enable reliable and optimal decision-making. Simulation results demonstrate significant improvements in network performance compared to other DRL baseline methods.