[2603.19285] Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks

Computer Science > Information Theory arXiv:2603.19285 (cs) [Submitted on 8 Mar 2026] Title:Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks Authors:Xiaoyang He, Manabu Tsukada View a PDF of the paper titled Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks, by Xiaoyang He and Manabu Tsukada View PDF HTML (experimental) Abstract: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 reproducing kernel Hilbert space (RKHS), where linear learning becomes feasible. Rather than treating each beam as an independent arm, the beam index is embedded into the context, enabling BKC-UCB to exploit correlations among beams to accelerate convergence. Furthermore, an event-triggered information sharing mechanism is incorporated into BKC-UCB, enabling in...