Millimeter Wave Vehicular Communication to Support Massive Automotive Sensing

Choi, Junil; Va, Vutha; Gonzalez-Prelcic, Nuria; Daniels, Robert V.; Bhat, Chandra R.; Heath, Robert W.

doi:10.48550/arxiv.1602.06456

Cited by 2 publications

(4 citation statements)

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“…Massive amount of data will be generated and exchanged between the various entities of the system. As an example, it is expected that just one self-driving vehicle will generate more than a gigabit-per-second of sensory data [2].…”

Section: Introductionmentioning

confidence: 99%

Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles

Mavromatis¹,

Tassi²,

Piechocki³

et al. 2017

Antennas, Propagation &Amp; RF Technology for Transport and Autonomous Platforms 2017

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Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves) communications can fulfil these requirements. However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming -thus introducing increased overhead. In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training. Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly. To minimise the position errors, an analysis of the distinct error components was presented. The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error. Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.

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Section: Introductionmentioning

confidence: 99%

Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles

Mavromatis¹,

Tassi²,

Piechocki³

et al. 2017

Antennas, Propagation &Amp; RF Technology for Transport and Autonomous Platforms 2017

View full text Add to dashboard Cite

show abstract

“…These technologies allow each vehicle to share its radars and sensors data with other vehicles or the infrastructure [2]. However, current vehicles have an average number of 100 sensors per vehicle and it is expected to double by 2020, since vehicles are getting more intelligent and the Internetof-things (IoT) is growing fast [3]. This number of sensors and equipment, that can be used for safety applications [4], [5], and other potential applications such as [6]: cooperative environmental perception, sensor data sharing, map downloading, sensor data gathering, and media downloading, needs a high data rate about multi giga-bits-per-second (Gbps).…”

Section: Introductionmentioning

confidence: 99%

“…These applications can help to ease traffic on roads and improve safety (by providing 360-degree awareness, emergency break, awareness for entering the road at cross, and etc.) [2], [3], [7].…”

Section: Introductionmentioning

confidence: 99%

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Beam switching techniques for millimeter wave vehicle to infrastructure communications

Mohammadi

Mohammadkhani

2017

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)

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Beam alignment for millimeter wave (mm Wave) vehicular communications is challenging due to the high mobility of vehicles. Recent studies have proposed some beam switching techniques at Road Side Unit (RSU) for vehicle to infrastructure (V2I) communications, employing initial position and speed information of vehicles, that are sent through Dedicated Short Range Communications (DSRC) to the RSU. However, inaccuracies of the provided information lead to beam misalignment. Some beam design parameters are suggested in the literature to combat this effect. But how these parameters should be tuned? Here, we evaluate the effect of all these parameters, and propose a beam design efficiency metric to perform beam alignment in the presence of the estimation errors, and to improve the performance by choosing the right design parameters.

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Millimeter Wave Vehicular Communication to Support Massive Automotive Sensing

Cited by 2 publications

References 0 publications

Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles

Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles

Beam switching techniques for millimeter wave vehicle to infrastructure communications

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