R&D on AI-Synthesized Deterministic Networking for 6G Communications

Ultra-Reliable and Low-Latency Communications (URLLC) were considered one of the key application scenarios in 5G with extremely low latency and reliability requirements (~1ms latency, up to 99.99999% reliability). 5G URLLC does not fulfill all KPIs of diverse mission-critical applications like industrial automation, intelligent transportation, telemedicine, Tactile Internet, VR/AR, and Meta-Universe. Deterministic Networking (DetNet) is gaining momentum in 6G to meet these stricter requirements: deterministic wireless throughput with high spectrum efficiency, deterministic QoS (bounded Age of Information, round-trip delay/jitter, zero packet loss), ultra-reliable/secure data delivery, high network availability/sustainability, and E2E orchestration across multiple technologies/domains.

Highly-motivated students or postdoc researchers able to acquire new knowledge, master new technologies, and conduct high-quality research leveraging DetNet, AI/ML, and SDN/NFV for deterministic flow scheduling. Good analytical skills and experience in network switching/routing, SDN, mathematical optimization, or machine learning preferred. Programming (Python, C/C++, Java), ML frameworks (PyTorch), network simulators (Omnet, NS3) a plus.