Demo: Testing AI-driven MAC Learning in Autonomic Networks

TL;DR

This paper demonstrates a realistic emulation platform for testing AI-driven MAC learning in dynamic 6G networks, focusing on resilient connectivity and autonomous decision-making.

Contribution

It introduces ContainerNet for emulating AI-capable networks and showcases deep RL agents learning MAC policies in a realistic environment.

Findings

Successful emulation of AI-enabled networks with resilient connectivity

Deep RL agents effectively learn MAC policies in the emulated environment

Validation of AI deployment strategies for future 6G networks

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.