Enhancing 5G O-RAN Communication Efficiency Through AI-Based Latency Forecasting

• URL: http://arxiv.org/abs/2502.18046v1
• Date: Tue, 25 Feb 2025 10:11:48 GMT
• Title: Enhancing 5G O-RAN Communication Efficiency Through AI-Based Latency Forecasting
• Authors: Raúl Parada, Ebrahim Abu-Helalah, Jordi Serra, Anton Aguilar, Paolo Dini,
• Abstract summary: This paper presents an artificial intelligence-driven latency forecasting system integrated into a functional O-RAN prototype.<n>The system uses a bidirectional long short-term memory model to predict latency in real time within a scalable, open-source framework built with FlexRIC.<n> Experimental results demonstrate the model's efficacy, achieving a loss metric below 0.04, thus validating its applicability in dynamic 5G environments.
• Score: 2.3784320672898227
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The increasing complexity and dynamic nature of 5G open radio access networks (O-RAN) pose significant challenges to maintaining low latency, high throughput, and resource efficiency. While existing methods leverage machine learning for latency prediction and resource management, they often lack real-world scalability and hardware validation. This paper addresses these limitations by presenting an artificial intelligence-driven latency forecasting system integrated into a functional O-RAN prototype. The system uses a bidirectional long short-term memory model to predict latency in real time within a scalable, open-source framework built with FlexRIC. Experimental results demonstrate the model's efficacy, achieving a loss metric below 0.04, thus validating its applicability in dynamic 5G environments.

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