Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks
This work addresses the challenge of timely channel information for user association and beamforming in mmWave vehicular networks, representing an incremental improvement by integrating kernel methods and event-triggered sharing into contextual bandits.
The paper tackles the overhead of frequent channel estimation in mmWave vehicular networks by proposing the BKC-UCB algorithm, which estimates instantaneous transmission rates using historical contexts like location and velocity, achieving improved learning efficiency with reduced communication overhead.
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.