On Scaling Latency-Aware MAC Communication Protocols with a Hierarchical Network Topology

Future 6G wireless networks of connected intelligence will require ultra-high throughput, ultra-low latency, extreme reliability, high levels of scalability and decentralized heterogeneous AI networking. To meet such stringent requirements, high-performing communication protocols become the foundation for the upcoming 6G use-cases. Recently, multi-agent reinforcement learning (MARL) has demonstrated its potential to provide viable alternatives to classical networking for such scenarios. However, while highly competitive, such emerged protocols are not fully explainable and fail to scale to large numbers of users. To bridge this gap, we leverage the advantages of the contention-free MAC protocol, optimize it for two users and integrate it in a hierarchical network architecture, to ensure its high scalability and decreased latency.