Scaling WLANs in Spectrum, User Density, and Robustness
The objective of this NSF-funded project is to design the next generation of Wireless Local Area Networks (WLANs) that will meet the ever-growing demands for more spectrum, higher densities, and higher degrees of robustness, moving closer towards the vision of
multi-Gigabit-per-second (Gbps) connectivity everywhere. Via a combination of physical (PHY) and link layer innovations, the project will design the first S-T (Sixty Gigahertz to Terahertz) WLAN offering multi-Gbps and Terabit-per-second (Tbps) data rates,
supporting both downlink and uplink multi-user multi-stream communication, and providing robust always-on connectivity. The proposed work is divided in three integrated research thrusts:
Thrust 1: The first thrust will develop new PHY layer techniques that maximize the utilization of the multi-Gigahertz (GHz) wide channels available in S-T communication systems. In the downlink, novel bandwidth hierarchical modulations are proposed as a way to enable simultaneous transmissions from users within the same transmit antenna beam. In the uplink, novel multi-beam codebooks will be designed to increase the path diversity and enable simultaneous directional transmission from users within the same area towards a common access point (AP).
Thrust 2: The second thrust will first explore the empirical limits of multi-user multi-stream communication in S-T bands. It will then design and evaluate low-overhead user and beam selection protocols for enabling downlink and uplink multi-user multi-stream communication in S-T WLANs, leveraging the hierarchical modulation schemes and multi-beam codebooks from the first thrust.
Thrust 3:. The third thrust will design the first PHY-assisted link adaptation framework to realize robust S-T WLANs. The thrust will develop algorithms that leverage unique PHY layer metrics to diagnose the cause of link degradation and perform fine-grained mobility and blockage classification. These algorithms will be leveraged to determine when to trigger adaptation and select the right adaptation strategies in different scenarios.
This project is sponsored by the National Science Foundation under Grant CNS-1801903.