ZODIAC: Zero-shot Offline Diffusion for Inferring Multi-xApps Conflicts in Open Radio Access Networks
Zeyu Fang, Shu Hong, Huu Trung Thieu, Nakjung Choi, Tian Lan

TL;DR
ZODIAC is a novel zero-shot framework that infers conflicts in open radio networks using only marginal offline data, outperforming existing methods in accuracy and diversity.
Contribution
It introduces the first conflict reasoning framework for O-RAN that operates without joint-execution data, leveraging diffusion models and uncertainty quantification.
Findings
ZODIAC achieves over 20% higher True Positive Rate at Top-20.
It demonstrates stronger correlation and greater scenario diversity.
Ablation confirms the importance of guidance components and uncertainty penalties.
Abstract
Open Radio Access Network (O-RAN) enables network control through multi-vendor xApps operating both within and across layers, subnets, and domains, whose concurrent execution can trigger conflicts that are latent during the development phase. Existing conflict management approaches rely heavily on joint-execution data, which is often unavailable in practice. To address this limitation, we formalize a novel problem termed conflict reasoning, which involves identifying conflict-inducing conditions given only marginal datasets from each individual xApp. We propose ZODIAC, a three-stage framework for zero-shot conflict condition inference that comprises uncertainty-aware surrogate model training, trajectory-level diffusion training, and compositional guided denoising for efficient, physics-constrained, and reliable condition search. We derive a theoretical lower confidence bound showing…
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