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

Abstract

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.

Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar

“…While the above mentioned works reduce the overhead of beam training, they are not designed for mobility situations. Recent work involving mobility leverages out-of-band position reports to aid in beam training [4,10]. Similar developments have been published for high speed trains [11].…”

“…Given the communication scenario ( Fig. 1) and transmit power P tx , we assume line-of-sight (LOS), allowing for the following simplified expression for the received power P rx [4] (all quantities in dB):…”

“…where θ el and θ az are the half-power beamwidths (radians) in elevation and azimuth for the corresponding antennas. To be consistent with the previous work in [4] and [10], we assumed an ideal beamforming in (12), characterized by uniformly distributed gain within the beam and no side lobes (ideal antenna efficiency). We model the impairments at the receiver by accounting for the phase noise P noise as follows: P noise = N floor + 10 log 10 B + NF, (13) where N floor is the noise floor, B is the system bandwidth and N F the noise figure of the receiver chain.…”

“…The 802.11ad protocol structures its frames into Beacon Intervals (BI) (Fig. 2) with 30 ms duration for vehicular networks [4]. The BIs consist of Beacon Header Interval (BHI), where antenna beams are formed and network synchronization information is exchanged, and Data Transfer Interval (DTI), during which the actual communication occurs.…”

“…Second, the beam training procedure is time-consuming and lowers efficiency by utilizing network resources that could be used for communication. This worsens in mobile scenarios where the beam training function calls are frequent [4]. Training involves two major steps: through per-sector transmissions, users first locate each other in space during coarse-grained training, followed by exploration of the identified sector fine-grained training to lower the beamwidth.…”