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

• URL: http://arxiv.org/abs/2603.19285v1
• Date: Sun, 08 Mar 2026 08:08:37 GMT
• Title: Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks
• Abstract summary: We propose a Beam-aware Kernelized Contextual Upper Confidence Bound (BKC-UCB) algorithm that estimates instantaneous transmission rates without additional channel measurements.<n>BKC-UCB exploits historical contexts such as vehicle location and velocity, together with past observed transmission rates.
• Score: 2.6527245983035437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 information exchange only when significant explorations are conducted to improve learning efficiency with limited communication overhead.

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