Demo: Testing AI-driven MAC Learning in Autonomic Networks

arXiv (Cornell University)

Abstract

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.

Authors 7

1. Leonard Paeleke

   Hasso Plattner Institute

   Affiliation as printed

   Hasso-Plattner-Institute (HPI) and University of Potsdam (UP) -

2. Navid Keshtiarast Aachen

   RWTH Aachen University

   Affiliation as printed

   MCC , RWTH Aachen University -

3. Paul Seehofer

   Karlsruhe Institute of Technology

   Affiliation as printed

   Karlsruhe Institute of Technology (KIT) -

4. Roland Bless

   Karlsruhe Institute of Technology

   Affiliation as printed

   Karlsruhe Institute of Technology (KIT) -

5. Holger Karl

   Hasso Plattner Institute

   Affiliation as printed

   Hasso-Plattner-Institute (HPI) and University of Potsdam (UP) -

6. Marina Petrova Aachen

   RWTH Aachen University

   Affiliation as printed

   MCC , RWTH Aachen University -

7. Martina Zitterbart

   Karlsruhe Institute of Technology

   Affiliation as printed

   Karlsruhe Institute of Technology (KIT) -