AI RESEARCH
Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks
arXiv CS.LG
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ArXi:2603.19285v1 Announce Type: cross Timely channel information is necessary for vehicles to determine both the serving base station (BS) and the beamforming vector, but frequent estimation of fast-fading mmWave channels incurs significant overhead. To address this challenge, we propose a Beam-aware Kernelized Contextual Upper Confidence Bound (BKC-UCB) algorithm that estimates instantaneous transmission rates without additional channel measurements by exploiting historical contexts such as vehicle location and velocity, together with past observed transmission rates.