Connection Management xAPP for O-RAN RIC: A Graph Neural Network and Reinforcement Learning Approach

TL;DR

This paper presents a novel graph neural network and reinforcement learning based approach for connection management in O-RAN 5G networks, significantly improving user association and load balancing over traditional methods.

Contribution

It introduces a deep reinforcement learning framework utilizing graph neural networks for optimized user-cell association in O-RAN networks, addressing load balancing and coverage.

Findings

Up to 10% increase in throughput

45-140% improvement in cell coverage

20-45% enhancement in load balancing

Abstract

Connection management is an important problem for any wireless network to ensure smooth and well-balanced operation throughout. Traditional methods for connection management (specifically user-cell association) consider sub-optimal and greedy solutions such as connection of each user to a cell with maximum receive power. However, network performance can be improved by leveraging machine learning (ML) and artificial intelligence (AI) based solutions. The next generation software defined 5G networks defined by the Open Radio Access Network (O-RAN) alliance facilitates the inclusion of ML/AI based solutions for various network problems. In this paper, we consider intelligent connection management based on the O-RAN network architecture to optimize user association and load balancing in the network. We formulate connection management as a combinatorial graph optimization problem. We propose a deep reinforcement learning (DRL) solution that uses the underlying graph to learn the weights of the graph neural networks (GNN) for optimal user-cell association. We consider three candidate objective functions: sum user throughput, cell coverage, and load balancing. Our results show up to 10% gain in throughput, 45-140% gain cell coverage, 20-45% gain in load balancing depending on network deployment configurations compared to baseline greedy techniques.