Connected and Autonomous Vehicles
Channel Modeling and Transceiver Architectures in Support of High Data Rate Communications in Mobile Environments
Problem: Autonomous and automated driving requires sensing of the environment based on which actions related to driving are performed. This requires potentially exchange of large volumes of data in a very mobile environment. This project examines physical layers solutions to supporting the data communication needs in a mobile environment.
The project will examine massive MIMO communication, both distributed antenna systems and co-located antenna systems, to support the high data rate needs of autonomous driving. Both sub 6 Ghz and mm-Wave communication will be of interest with some preference to mm-Wave communication. The specific aims of our project are: 1) Development and analysis of low complexity receiver and transmitter architectures (mm-Wave) in support of the communication needs. In particular, development of suitable antenna array, such as nested array, architectures along with the RF chains topology for rich channel sensing. 2) Novel channel estimation methods that are synergistic with the architectures , such as the sparse Bayesian methods, to enable a richer characterization of the communication environment than hitherto possible. 3) Novel channel models to incorporate mobility as well as the development of algorithms to track the channel in mobile environments. 4) Distributed antennas with emphasis on access point (AP) placement for maximizing throughput. Of particular interest are hybrid networks, which is composed of fixed-position terrestrial-APs and flexible-position UAV-APs.