利用历史数据预测毫米波车辆通信速率，减少信道测量开销。
Beam-aware Kernelized Contextual Bandits for User Association and Beamforming in mmWave Vehicular Networks
- 通过核方法建模位置速度与传输速率的非线性关系。
- 融合波束索引上下文，利用波束间相关性加速学习收敛。
- 仅在关键探索时触发信息共享，降低通信开销，适合车联网场景。
及时获取信道信息对车辆确定服务基站和波束赋形向量至关重要，但频繁估计快速衰落的毫米波信道会带来显著开销。为此，我们提出一种波束感知的核化上下文上置信界（BKC-UCB）算法，通过利用车辆位置、速度等历史上下文及过往观测到的传输速率，无需额外信道测量即可估计瞬时传输速率。具体而言，BKC-UCB采用核方法将上下文映射到再生核希尔伯特空间（RKHS），捕捉上下文与传输速率之间的非线性关系，使线性学习成为可能。不同于将每个波束视为独立臂，该算法将波束索引嵌入上下文，从而利用波束间的相关性加速收敛。此外，引入事件触发式信息共享机制，仅在发生显著探索时进行信息交换，以在有限通信开销下提升学习效率。
原文摘要 · Abstract (English)
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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