Combining Millimeter-Wave Radar and Communication Paradigms for Automotive Applications: A Signal Processing Approach (106)

As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars (RAdio Detection and Ranging) are used for object detection, visual cameras as virtual mirrors, and LIDARs (LIght Detection and Ranging) for generating high resolution depth associated range maps, all to enhance the safety and efficiency of driving. Connected vehicles can use wireless communication to exchange sensor data, allowing them to enlarge their sensing range and improve automated driving functions. Unfortunately, conventional technologies, such as dedicated shortrange communication (DSRC) and 4G cellular communication, do not support the gigabit-per-second data rates that would be required for raw sensor data exchange between vehicles. This paper makes the case that millimeter wave (mmWave) communication is the only viable approach for high bandwidth connected vehicles. The motivations and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. A high-level solution to one key challenge — the overhead of mmWave beam training — is proposed. The critical feature of this solution is to leverage information derived from the sensors or DSRC as side information for the mmWave communication link Junil Choi is with the Department of Electrical Engineering, POSTECH, Pohang, Gyeongbuk, Korea (email: [email protected]). Vutha Va and Robert Heath are with Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712, USA (email: {vutha.va,rheath}@utexas.edu). Nuria Gonzalez-Prelcic is with Universidade de Vigo, Vigo, Spain (email: [email protected]). Robert Daniels with PHAZR, Inc, USA (email:[email protected]). Chandra Bhat is with Center for Transportation Research, The University of Texas at Austin, Austin, TX 78712, USA (email: [email protected]). This research was partially supported by the U.S. Department of Transportation through the Data-Supported Transportation Operations and Planning (D-STOP) Tier 1 University Transportation Center and by the Texas Department of Transportation under Project 0-6877 entitled “Communications and Radar-Supported Transportation Operations and Planning (CAR-STOP)”. September 14, 2016 DRAFT