Large Generative AI Models meet Open Networks for 6G: Integration, Platform, and Monetization
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arXiv
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| Format: | Preprint |
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2024
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| author | Li, Peizheng Sánchez-Mompó, Adrián Farnham, Tim Khan, Aftab Aijaz, Adnan |
| author_facet | Li, Peizheng Sánchez-Mompó, Adrián Farnham, Tim Khan, Aftab Aijaz, Adnan |
| contents | Generative artificial intelligence (GAI) has emerged as a pivotal technology for content generation, reasoning, and decision-making, making it a promising solution on the 6G stage characterized by openness, connected intelligence, and service democratization. This article explores strategies for integrating and monetizing GAI within future open 6G networks, mainly from the perspectives of mobile network operators (MNOs). We propose a novel API-centric telecoms GAI marketplace platform, designed to serve as a central hub for deploying, managing, and monetizing diverse GAI services directly within the network. This platform underpins a flexible and interoperable ecosystem, enhances service delivery, and facilitates seamless integration of GAI capabilities across various network segments, thereby enabling new revenue streams through customer-centric generative services. Results from experimental evaluation in an end-to-end Open RAN testbed, show the latency benefits of this platform for local large language model (LLM) deployment, by comparing token timing for various generated lengths with cloud-based general-purpose LLMs. Lastly, the article discusses key considerations for implementing the GAI marketplace within 6G networks, including monetization strategy, regulatory, management, and service platform aspects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18790 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Generative AI Models meet Open Networks for 6G: Integration, Platform, and Monetization Li, Peizheng Sánchez-Mompó, Adrián Farnham, Tim Khan, Aftab Aijaz, Adnan Networking and Internet Architecture Generative artificial intelligence (GAI) has emerged as a pivotal technology for content generation, reasoning, and decision-making, making it a promising solution on the 6G stage characterized by openness, connected intelligence, and service democratization. This article explores strategies for integrating and monetizing GAI within future open 6G networks, mainly from the perspectives of mobile network operators (MNOs). We propose a novel API-centric telecoms GAI marketplace platform, designed to serve as a central hub for deploying, managing, and monetizing diverse GAI services directly within the network. This platform underpins a flexible and interoperable ecosystem, enhances service delivery, and facilitates seamless integration of GAI capabilities across various network segments, thereby enabling new revenue streams through customer-centric generative services. Results from experimental evaluation in an end-to-end Open RAN testbed, show the latency benefits of this platform for local large language model (LLM) deployment, by comparing token timing for various generated lengths with cloud-based general-purpose LLMs. Lastly, the article discusses key considerations for implementing the GAI marketplace within 6G networks, including monetization strategy, regulatory, management, and service platform aspects. |
| title | Large Generative AI Models meet Open Networks for 6G: Integration, Platform, and Monetization |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2410.18790 |