Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks
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. Specifically, BKC-UCB leverages kernel methods to capture the nonlinear relationship between context and transmission rate by mapping contexts into a reproduci...
| Authors | Xiaoyang He, Manabu Tsukada |
|---|---|
| Primary category | cs.IT |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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