Sensor and Map-Aided Cooperative Beam Tracking for Optical V2V Communications

This paper focuses on advanced pointing strategies enabling high data-rate directional vehicular communications. New emerging technologies aim to meet the challenging performance requirements of enhanced Vehicle-to-Everything (eV2X) applications by using highly collimated beams, which must rely on a very precise beam alignment. In this work, Free-Space Optics (FSO) is considered, and a system architecture is introduced together with algorithms for an accurate alignment of laser beam. The presented architecture exploits on-board sensor data sharing among vehicles to predict the pointing directions for FSO, thus counteracting the detrimental effect of motion, vibrations and tilting of vehicles. A solution is proposed for an accurate prediction of the FSO pointing direction, based on the sharing of vehicle kinematic data over a parallel wireless control link, augmented by prior information extracted from digital maps of the driving environment. Simulation results point out the challenges of a FSO V2V communication and highlight the feasibility of the proposed solution, considering both state of the art technology and future perspective hardware.

[1]  Petar Popovski,et al.  5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View , 2018, IEEE Access.

[2]  Roberto Rojas-Cessa,et al.  A Survey on Acquisition, Tracking, and Pointing Mechanisms for Mobile Free-Space Optical Communications , 2018, IEEE Communications Surveys & Tutorials.

[3]  Robert J. Piechocki,et al.  mmWave System for Future ITS: A MAC-Layer Approach for V2X Beam Steering , 2017, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall).

[4]  Robert W. Heath,et al.  Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing , 2016, IEEE Communications Magazine.

[5]  Mirrorcle Technologies MEMS Mirrors – Technical Overview , 2013 .

[6]  Monica Nicoli,et al.  Augmenting Vehicle Localization by Cooperative Sensing of the Driving Environment: Insight on Data Association in Urban Traffic Scenarios , 2020, IEEE Transactions on Intelligent Transportation Systems.

[7]  C. Gueymard Parameterized transmittance model for direct beam and circumsolar spectral irradiance , 2001 .

[8]  Javier Gozalvez,et al.  LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications , 2017, IEEE Vehicular Technology Magazine.

[9]  Fredrik Tufvesson,et al.  5G mmWave Positioning for Vehicular Networks , 2017, IEEE Wireless Communications.

[10]  Ivan B. Djordjevic,et al.  A Survey on FEC Codes for 100 G and Beyond Optical Networks , 2016, IEEE Communications Surveys & Tutorials.

[11]  Agus Budiyono,et al.  Principles of GNSS, Inertial, and Multi-sensor Integrated Navigation Systems , 2012 .

[12]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[13]  Oliver Pink,et al.  A statistical approach to map matching using road network geometry, topology and vehicular motion constraints , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[14]  Andrea Matera,et al.  RF-Assisted Free-Space Optics for 5G Vehicle-to-Vehicle Communications , 2019, 2019 IEEE International Conference on Communications Workshops (ICC Workshops).

[15]  Thomas B. Schön,et al.  Using Inertial Sensors for Position and Orientation Estimation , 2017, Found. Trends Signal Process..

[16]  Umberto Spagnolini,et al.  Inertial Sensor Aided mmWave Beam Tracking to Support Cooperative Autonomous Driving , 2019, 2019 IEEE International Conference on Communications Workshops (ICC Workshops).

[17]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[18]  Ronald Raulefs,et al.  Implicit Cooperative Positioning in Vehicular Networks , 2017, IEEE Transactions on Intelligent Transportation Systems.