Compressive Sensing for Blockage Detection in Vehicular Millimeter Wave Antenna Arrays
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
KAUST Grant NumberOSR-2016-KKI-2899
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AbstractThe radiation pattern of an antenna array depends on the excitation weights and the geometry of the array. Due to mobility, some vehicular antenna elements might be subjected to full or partial blockages from a plethora of particles like dirt, salt, ice, and water droplets. These particles cause absorption and scattering to the signal incident on the array, and as a result, change the array geometry. This distorts the radiation pattern of the array mostly with an increase in the sidelobe level and decrease in gain. In this paper, we propose a blockage detection technique for millimeter wave vehicular antenna arrays that jointly estimates the locations of the blocked antennas and the attenuation and phase-shifts that result from the suspended particles. The proposed technique does not require the antenna array to be physically removed from the vehicle and permits real-time array diagnosis. Numerical results show that the proposed technique provides satisfactory results in terms of block detection with low detection time provided that the number of blockages is small compared to the array size.
CitationEltayeb ME, Al-Naffouri TY, Heath RW (2016) Compressive Sensing for Blockage Detection in Vehicular Millimeter Wave Antenna Arrays. 2016 IEEE Global Communications Conference (GLOBECOM). Available: http://dx.doi.org/10.1109/glocom.2016.7841677.
SponsorsThis 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). The work of T. Y. Al-Naffouri is supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2016-KKI-2899.