6G Infrastructures for Edge AI: An Analytical Perspective
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914165187149824 |
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| author | Horvath, Kurt Tuda, Shpresa Idrizi, Blerta Kitanov, Stojan Doko, Fisnik Kimovski, Dragi |
| author_facet | Horvath, Kurt Tuda, Shpresa Idrizi, Blerta Kitanov, Stojan Doko, Fisnik Kimovski, Dragi |
| contents | The convergence of Artificial Intelligence (AI) and the Internet of Things has accelerated the development of distributed, network-sensitive applications, necessitating ultra-low latency, high throughput, and real-time processing capabilities. While 5G networks represent a significant technological milestone, their ability to support AI-driven edge applications remains constrained by performance gaps observed in real-world deployments. This paper addresses these limitations and highlights critical advancements needed to realize a robust and scalable 6G ecosystem optimized for AI applications. Furthermore, we conduct an empirical evaluation of 5G network infrastructure in central Europe, with latency measurements ranging from 61 ms to 110 ms across different close geographical areas. These values exceed the requirements of latency-critical AI applications by approximately 270%, revealing significant shortcomings in current deployments. Building on these findings, we propose a set of recommendations to bridge the gap between existing 5G performance and the requirements of next-generation AI applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10570 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 6G Infrastructures for Edge AI: An Analytical Perspective Horvath, Kurt Tuda, Shpresa Idrizi, Blerta Kitanov, Stojan Doko, Fisnik Kimovski, Dragi Distributed, Parallel, and Cluster Computing The convergence of Artificial Intelligence (AI) and the Internet of Things has accelerated the development of distributed, network-sensitive applications, necessitating ultra-low latency, high throughput, and real-time processing capabilities. While 5G networks represent a significant technological milestone, their ability to support AI-driven edge applications remains constrained by performance gaps observed in real-world deployments. This paper addresses these limitations and highlights critical advancements needed to realize a robust and scalable 6G ecosystem optimized for AI applications. Furthermore, we conduct an empirical evaluation of 5G network infrastructure in central Europe, with latency measurements ranging from 61 ms to 110 ms across different close geographical areas. These values exceed the requirements of latency-critical AI applications by approximately 270%, revealing significant shortcomings in current deployments. Building on these findings, we propose a set of recommendations to bridge the gap between existing 5G performance and the requirements of next-generation AI applications. |
| title | 6G Infrastructures for Edge AI: An Analytical Perspective |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2506.10570 |