The Role of Generative Artificial Intelligence in Testing and Automation of Open RAN Systems: GNG-OTA Framework

I. INTRODUCTION

• Diverse vendors and configurations make testing environments complex and limit visibility into potential interoperability use cases, while specification updates require parallel test adaptations.The paper also identifies standardized test methodologies and open, collaborative evaluation as pillars of a robust testing ecosystem.Source: §I · p. 1
• GNG-OTA is proposed as an integrated AI tester for accurate, reliable, efficient, and automatic O-RAN interoperability and performance testing.The framework uses generative AI, NLP, and graph neural networks to address interoperability and performance testing challenges.Source: §I · p. 2
• The paper moves from O-RAN testing requirements and AI techniques to the GNG-OTA framework, then concludes.Source: §I · p. 2

A. Near-RT and Non-RT RIC Testing

• Near-RT and Non-RT RIC interoperability tests check that multi-vendor implementations communicate and function together.Source: A. Near-RT and Non-RT RIC Testing · p. 2
• A1-P scenarios cover policy-type queries and lifecycle operations, while A1-EI scenarios cover EI-type queries, job management, and result delivery.These tests check that policy and EI-job information is exchanged, interpreted, and reported across RIC implementations.Source: A. Near-RT and Non-RT RIC Testing · p. 2
• Near-RT RIC conformance testing checks low-latency control functions and message contents, structure, and response accuracy; Non-RT RIC tests cover optimization and orchestration functions.Named functions include policy management, EI-job handling, and notification delivery.Source: A. Near-RT and Non-RT RIC Testing · p. 2

B. O-DU and O-RU Testing

• Fronthaul interoperability testing covers M-, S-, C-, and U-Planes, which may be tested independently or simultaneously during end-to-end activities.The source gives bringing devices into service and establishing calls as examples of activity across multiple planes.Source: B. O-DU and O-RU Testing · p. 2
• M-Plane tests check startup interaction with DHCP servers and NETCONF clients, while S-Plane tests check frequency and time synchronization under changing temperature and traffic loads.The tests assess whether O-DU/O-RU end-to-end functionality meets fronthaul specifications.Source: B. O-DU and O-RU Testing · p. 2
• C-Plane and U-Plane interoperability tests check UE connections and initial data transfers, measuring availability, accessibility, retainability, mobility, and data integrity.Customized configurations may be needed when equipment limits or licensing constraints prevent use of standard profiles; tests also assess packet timing, integrity, O-RU processing time, and fronthaul latency.Source: B. O-DU and O-RU Testing · p. 2
• O-DU/O-RU conformance checks include VLAN and DHCPv4 sessions, notifications, access control, energy settings, EPE measurements, uplink sensitivity, and synchronization.The passage describes these checks as confirming reliable and efficient operation within a multi-vendor O-RAN environment.Source: B. O-DU and O-RU Testing · p. 2

C. Other Performance Testing

• Tests cover cell setup and MIB/SIB broadcasts, UE attachment, registration, de-registration and PDU session establishment, plus O-DU/SMO link establishment and YANG capability exchange over O1.Source: C. Other Performance Testing · pp. 2–3
• Additional tests check SMO-configured cell activation, subscribed measurement counters, E2 establishment with the near-RT RIC, periodic O-DU reporting, and UE attachment under specified radio settings.The passage specifies SSB periodicity and sub-carrier spacing for the final UE attachment test.Source: C. Other Performance Testing · p. 3

• GAI, NLP, and GNNs are presented as applicable techniques for O-RAN testing and automation.Source: §III · p. 3
• Integrating these techniques is described as offering potential gains in testing efficiency, accuracy, and adaptability.Source: §III · p. 3
• The section examines the applicability and benefits of these AI techniques within the O-RAN ecosystem.Source: §III · p. 3

• GANs and VAEs can generate synthetic data to test rare and extreme network conditions that are difficult or time-consuming to reproduce in reality.They can scale diverse traffic patterns, user behaviors, and interference scenarios for large, complex networks.Source: A. Generative AI (GAI) · p. 3
• GAN generators create samples that discriminators evaluate, while VAEs encode data into a latent space and decode it to generate plausible network variations.Manipulating VAE latent variables can produce scenarios ranging from typical to extreme.Source: A. Generative AI (GAI) · p. 3
• GANs and VAEs can emulate high policy-query loads and dynamic updates to validate policy exchange across Near-RT and Non-RT RICs.They can also simulate O-RU startup sequences and user traffic for interface and end-to-end O-DU/O-RU tests.Source: A. Generative AI (GAI) · p. 3
• Synthetic interference and synchronization challenges can test whether O-RU and O-DU maintain frequency and time synchronization under varying conditions.Source: A. Generative AI (GAI) · p. 3

B. Natural Language Processing (NLP)

• NLP processes configurations, logs, and test descriptions using parsing, information extraction, pattern recognition, anomaly detection, and semantic analysis.Source: B. Natural Language Processing (NLP) · p. 3
• NLP can generate test cases from standards and vendor documents, analyze logs for anomalies, and map test cases to requirements for traceability and compliance checks [12] [13].BERT-based models may parse technical documents, while GPT models may generate test reports [12].Source: B. Natural Language Processing (NLP) · p. 3

C. Graph Neural Networks (GNNs)

• GNNs represent O-RAN components as nodes and their interactions as edges, learning from historical test outputs to predict behavior and flag deviations.The described use cases include policy propagation between RICs and UE attachment-pattern checks for possible registration or authentication anomalies.Source: C. Graph Neural Networks (GNNs) · pp. 3–4

• The proposed GNG-OTA framework combines GANs, NLP, and GNNs for O-RAN network testing.Source: §IV · p. 4
• The framework is tailored to build a robust and adaptive testing environment for O-RAN interoperability and performance challenges.Source: §IV · p. 4
• The framework is illustrated in Fig. 1.Source: §IV · p. 4

A. GNG-OTA Training Phase

• The NLP phase preprocesses O-RAN specifications and 3GPP reports into context-preserving segments, then fine-tunes a model to convert high-level descriptions into detailed test requirements.The model is trained on domain-specific terminology and context, including requirements concerning interoperability, latency, throughput, and synchronization.Source: A. GNG-OTA Training Phase · p. 4
• The GAN phase trains on historical O-RAN interactions, traffic patterns, test events, messages, timings, and outcomes to generate realistic test data.DCGAN or WGAN may be selected according to the complexity and nature of the collected dataset.Source: A. GNG-OTA Training Phase · p. 4
• The GNN models O-RAN components as nodes and their interactions as edges, learning node, edge, and graph-level features from real and GAN-generated test data.Edge embeddings can capture latency, error rates, achieved rate, and configuration parameters; supervised learning helps identify deviations from successful test patterns.Source: A. GNG-OTA Training Phase · p. 4

B. Inference Phase

• Natural-language requirements are converted into action-oriented test cases specifying what to test, procedures, and parameters, then structured as machine-readable configuration files.The configurations set up the O-RAN test environment for the selected units, interfaces, or functions.Source: B. Inference Phase · pp. 4–5
• The test configurations are streamed to the GAN to generate synthetic traffic patterns for evaluating performance with new or unseen data.Custom scripts execute the test, and collected logs are used to construct GNN graph representations.Source: B. Inference Phase · p. 5
• The GNN compares current test graphs with historical outputs for the components, functions, or interfaces under test to predict potential interoperability issues and performance bottlenecks.The framework is depicted in Fig. 1.Source: B. Inference Phase · p. 5, Figure 1 · p. 5

V. CONCLUSIONS

• GNG-OTA combines generative AI, NLP, and GNNs to automate O-RAN testing and address complex testing environments and evolving specifications.The conclusion says the framework preprocesses protocol specifications, generates test cases, predicts potential issues through graph analysis, and reports results, improving testing efficiency, reliability, and accuracy.Source: §V · p. 5