XAI-on-RAN: Explainable, AI-native, and GPU-Accelerated RAN Towards 6G
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| Format: | Preprint |
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2025
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| _version_ | 1866908669303586816 |
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| author | Basaran, Osman Tugay Dressler, Falko |
| author_facet | Basaran, Osman Tugay Dressler, Falko |
| contents | Artificial intelligence (AI)-native radio access networks (RANs) will serve vertical industries with stringent requirements: smart grids, autonomous vehicles, remote healthcare, industrial automation, etc. To achieve these requirements, modern 5G/6G design increasingly leverage AI for network optimization, but the opacity of AI decisions poses risks in mission-critical domains. These use cases are often delivered via non-public networks (NPNs) or dedicated network slices, where reliability and safety are vital. In this paper, we motivate the need for transparent and trustworthy AI in high-stakes communications (e.g., healthcare, industrial automation, and robotics) by drawing on 3rd generation partnership project (3GPP)'s vision for non-public networks. We design a mathematical framework to model the trade-offs between transparency (explanation fidelity and fairness), latency, and graphics processing unit (GPU) utilization in deploying explainable AI (XAI) models. Empirical evaluations demonstrate that our proposed hybrid XAI model xAI-Native, consistently surpasses conventional baseline models in performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_17514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | XAI-on-RAN: Explainable, AI-native, and GPU-Accelerated RAN Towards 6G Basaran, Osman Tugay Dressler, Falko Networking and Internet Architecture Artificial Intelligence Artificial intelligence (AI)-native radio access networks (RANs) will serve vertical industries with stringent requirements: smart grids, autonomous vehicles, remote healthcare, industrial automation, etc. To achieve these requirements, modern 5G/6G design increasingly leverage AI for network optimization, but the opacity of AI decisions poses risks in mission-critical domains. These use cases are often delivered via non-public networks (NPNs) or dedicated network slices, where reliability and safety are vital. In this paper, we motivate the need for transparent and trustworthy AI in high-stakes communications (e.g., healthcare, industrial automation, and robotics) by drawing on 3rd generation partnership project (3GPP)'s vision for non-public networks. We design a mathematical framework to model the trade-offs between transparency (explanation fidelity and fairness), latency, and graphics processing unit (GPU) utilization in deploying explainable AI (XAI) models. Empirical evaluations demonstrate that our proposed hybrid XAI model xAI-Native, consistently surpasses conventional baseline models in performance. |
| title | XAI-on-RAN: Explainable, AI-native, and GPU-Accelerated RAN Towards 6G |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2511.17514 |