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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.18046 |
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| _version_ | 1866912245388148736 |
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| author | Parada, Raúl Abu-Helalah, Ebrahim Serra, Jordi Aguilar, Anton Dini, Paolo |
| author_facet | Parada, Raúl Abu-Helalah, Ebrahim Serra, Jordi Aguilar, Anton Dini, Paolo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing 5G O-RAN Communication Efficiency Through AI-Based Latency Forecasting Parada, Raúl Abu-Helalah, Ebrahim Serra, Jordi Aguilar, Anton Dini, Paolo Machine Learning 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. |
| title | Enhancing 5G O-RAN Communication Efficiency Through AI-Based Latency Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.18046 |