Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing

By Junil Choi, Vutha Va, Nuria González-Prelcic, Robert C. Daniels, Chandra R. Bhat, Robert W. Heath. IEEE Communications Magazine, 2016

751 citations recorded by the source corpus.

As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars are used for object detection, visual cameras as virtual mirrors, and LIDARs 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 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 article makes the case that 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 configuration. Examples and simulation results show that the beam alignment overhead can be reduced by using position information obtained from DSRC.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1192ff49d8e5392eaf1699bf452aec0d42cbd5e9

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Citations in the corpus

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Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems (2018, background): “In [32] and [33], the BSs serving vehicular systems build a database relating the vehicle position and the beam training result.”
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