# Learning-Based User Association for MmWave Vehicular Networks With Kernelized Contextual Bandits

arXiv: https://arxiv.org/abs/2504.10959 · HTML: https://mlsanity.com/paper/2504.10959/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Finite-Time Analysis of Kernelised Contextual Bandits](https://mlsanity.com/paper/1309.6869/) (2013)
- [Communication Efficient Distributed Learning for Kernelized Contextual Bandits](https://mlsanity.com/paper/2206.04835/) (2022)
- [Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks](https://mlsanity.com/paper/2410.05785/) (2024)

## Built on by (most cited first, 1 of 1)

- [Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks](https://mlsanity.com/paper/2603.19285/) (2026)

## Abstract (arXiv)

Vehicles require timely channel conditions to determine the base station (BS) to communicate with, but it is costly to estimate the fast-fading mmWave channels frequently. Without additional channel estimations, the proposed Distributed Kernelized Upper Confidence Bound (DK-UCB) algorithm estimates the current instantaneous transmission rates utilizing past contexts, such as the vehicle's location and velocity, along with past instantaneous transmission rates. To capture the nonlinear mapping from a context to the instantaneous transmission rate, DK-UCB maps a context into the reproducing kernel Hilbert space (RKHS) where a linear mapping becomes observable. To improve estimation accuracy, we propose a novel kernel function in RKHS which incorporates the propagation characteristics of the mmWave signals. Moreover, DK-UCB encourages a vehicle to share necessary information when it has conducted significant explorations, which speeds up the learning process while maintaining affordable communication costs.