xDiff: Online Diffusion Model for Collaborative Inter-Cell Interference Management in 5G O-RAN

TL;DR

xDiff is a novel diffusion-based reinforcement learning framework designed for real-time inter-cell interference management in 5G O-RAN, effectively optimizing resource allocation and outperforming existing methods.

Contribution

The paper introduces a new diffusion model integrated with reinforcement learning for online ICIM in O-RAN, including a novel policy representation and real-world testbed validation.

Findings

xDiff outperforms state-of-the-art ICIM methods in experiments.

The framework enables near-real-time policy generation for resource management.

Experimental validation on a 5G testbed demonstrates practical effectiveness.

Abstract

Open Radio Access Network (O-RAN) is a key architectural paradigm for 5G and beyond cellular networks, enabling the adoption of intelligent and efficient resource management solutions. Meanwhile, diffusion models have demonstrated remarkable capabilities in image and video generation, making them attractive for network optimization tasks. In this paper, we propose xDiff, a diffusion-based reinforcement learning(RL) framework for inter-cell interference management (ICIM) in O-RAN. We first formulate ICIM as a resource allocation optimization problem aimed at maximizing a user-defined reward function and then develop an online learning solution by integrating a diffusion model into an RL framework for near-real-time policy generation. Particularly, we introduce a novel metric, preference values, as the policy representation to enable efficient policy-guided resource allocation within O-RAN distributed units (DUs). We implement xDiff on a 5G testbed consisting of three cells and a set of smartphones in two small-cell scenarios. Experimental results demonstrate that xDiff outperforms state-of-the-art ICIM approaches, highlighting the potential of diffusion models for online optimization of O-RAN. Source code is available on GitHub [1].