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Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)

Created by
  • Haebom

Author

Sarat Ahmad, Zeinab Nezami, Maryam Hafeez, Syed Ali Raza Zaidi

Outline

This paper addresses the role of generative AI (GenAI) for autonomous optimization of next-generation wireless networks and the generation of xApps and rApps using large-scale language models (LLMs) within the Open RAN (ORAN) architecture. To address the high cost and resource consumption of conventional LLM fine-tuning, this paper proposes a Retrieval-Augmented Generation (RAG)-based approach. Specifically, we compare and evaluate three approaches: vector-based RAG, GraphRAG, and Hybrid GraphRAG, using the ORAN specification, and analyze their performance in terms of fidelity, answer relevance, contextual relevance, and factual accuracy depending on question complexity. The results show that GraphRAG and Hybrid GraphRAG outperform conventional vector-based RAG, and in particular, Hybrid GraphRAG improves factual accuracy by 8% and GraphRAG improves contextual relevance by 11%.

Takeaways, Limitations

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Takeaways:
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Experimentally demonstrating that GraphRAG and Hybrid GraphRAG are effective in generating LLM-based xApps and rApps in an ORAN environment.
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Hybrid GraphRAG improves factual accuracy, while GraphRAG improves contextual relevance, increasing the applicability of RAG in high-risk domains such as ORAN.
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Provides guidance on selecting a RAG model suitable for the ORAN environment by comparing the performance of various RAG methodologies.
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Limitations:
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This study evaluated a specific ORAN specification. Further research is needed to determine its generalizability to other ORAN specifications or wireless network environments.
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Consideration should be given to metrics other than those used in the evaluation (e.g., generation speed, computational cost).
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Lack of experimental validation in a real ORAN environment. Further research is needed to address potential issues that may arise during actual implementation and deployment.
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