Demo: Testing AI-driven MAC Learning in Autonomic Networks
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912073451044864 |
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| author | Paeleke, Leonard Keshtiarast, Navid Seehofer, Paul Bless, Roland Karl, Holger Petrova, Marina Zitterbart, Martina |
| author_facet | Paeleke, Leonard Keshtiarast, Navid Seehofer, Paul Bless, Roland Karl, Holger Petrova, Marina Zitterbart, Martina |
| contents | 6G networks will be highly dynamic, re-configurable, and resilient. To enable and support such features, employing AI has been suggested. Integrating AIin networks will likely require distributed AI deployments with resilient connectivity, e.g., for communication between RL agents and environment. Such approaches need to be validated in realistic network environments. In this demo, we use ContainerNet to emulate AI-capable and autonomic networks that employ the routing protocol KIRA to provide resilient connectivity and service discovery. As an example AI application, we train and infer deep RL agents learning medium access control (MAC) policies for a wireless network environment in the emulated network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11565 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Demo: Testing AI-driven MAC Learning in Autonomic Networks Paeleke, Leonard Keshtiarast, Navid Seehofer, Paul Bless, Roland Karl, Holger Petrova, Marina Zitterbart, Martina Networking and Internet Architecture Systems and Control 6G networks will be highly dynamic, re-configurable, and resilient. To enable and support such features, employing AI has been suggested. Integrating AIin networks will likely require distributed AI deployments with resilient connectivity, e.g., for communication between RL agents and environment. Such approaches need to be validated in realistic network environments. In this demo, we use ContainerNet to emulate AI-capable and autonomic networks that employ the routing protocol KIRA to provide resilient connectivity and service discovery. As an example AI application, we train and infer deep RL agents learning medium access control (MAC) policies for a wireless network environment in the emulated network. |
| title | Demo: Testing AI-driven MAC Learning in Autonomic Networks |
| topic | Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2410.11565 |