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We investigate sensing-assisted predictive beamforming schemes forvehicle-to-infrastructure (V2I) communication by exploiting the integratedsensing and communication (ISAC) functionalities at the roadside unit (RSU).The RSU deploys a massive multi-input-multi-output (mMIMO) array and operatesat millimeter wave (mmWave) frequencies. The pencil-sharp mMIMO beams and finerange resolution achieved at mmWave, implicates that the point targetassumption is impractical in such V2I networks, as the volume and shape of thevehicles become essential for beamforming. Simply pointing a beam to thevehicle may result in the communication receiver (CR) never lying in the beam,even when the vehicle's trajectory is accurately tracked. To tackle thisproblem, we consider the extended vehicle target with two novel beam trackingschemes. For the first scheme, the beamwidth is adjusted in real-time to coverthe entire vehicle, followed by an extended Kalman filtering (EKF) algorithm topredict and track the position of CR according to the resolved high-resolutionscatterers. An upgraded scheme is further proposed by splitting eachtransmission block into two stages. The first stage is exploited for ISACtransmission, where a wide beam is adopted for both communication and sensing.Based on the sensed results at the first stage, the second stage is dedicatedto communication by adopting a pencil-sharp beam, yielding a significantimprovement of the achievable rate. We further reveal the inherent tradeoffbetween the two stages in terms of their durations, and develop an optimal timeallocation strategy that maximizes the average achievable rate. Finally,numerical results are provided to verify the superiorities of proposed schemesover the state-of-the-art methods.

Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets