AI-empowered Networking

The WMNL is investigating AI/ML-empowered approaches to various networking problems. For example, we are re-visiting the WLAN-5G coexistence scenario to adopt AI techniques for autonomous network reconfiguration. In the WLAN-5G heterogeneous network (HetNet), an asymmetry can arise such that Wi-Fi is exposed to 5G NR-U, but NR-U is hidden from Wi-Fi, since Wi-Fi’s CCA threshold is -62 dBm whereas NR-U’s is -72 dBm, as directed by 3GPP. Although we have already proposed a series of analytical frameworks to model such asymmetry (in our TMC’18 and Access’19 papers), we are trying to develop a more effective mechanism that can be applied to more diverse coexistence scenarios. For this, we consider to utilize AI-based techniques likes Deep Reinforcement Learning (DRL) and Generative adversarial network (GAN) as illustrated below.



We are also looking at various technology enablers of 6G, including RAN agnostic transceivers, extreme URLLC, AI inspired air interfaces, etc. Among them, we currently focus on the next-generation RAN agnostic transceiver design, powered by AI-based signal examination, so as to make transceivers less restricted by the given structure of wireless standards.