用历史位置速度信息预测毫米波车路通信速率，省去频繁信道估计。
Learning-Based User Association for MmWave Vehicular Networks With Kernelized Contextual Bandits
- 基于车辆位置速度等上下文，用核方法推断实时传输速率。
- 新核函数融合毫米波传播特性，提升速率预测精度。
- 鼓励探索后共享信息，加速学习且通信开销低。
车辆需及时获取信道状态以选择通信基站，但频繁估计快速衰落的毫米波信道成本高昂。本文提出的分布式核化上置信界（DK-UCB）算法，利用车辆历史位置、速度等上下文及过往瞬时传输速率，推断当前瞬时传输速率，避免额外信道估计。为捕捉上下文到传输速率的非线性映射，DK-UCB将上下文映射至再生核希尔伯特空间（RKHS），使线性映射可实现。本文提出一种融合毫米波信号传播特性的新型核函数，提升估计精度。此外，DK-UCB在车辆进行显著探索后鼓励共享必要信息，加速学习过程，同时保持较低通信开销。
原文摘要 · Abstract (English)
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.
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