Millimeter-wave communication is a challenge in the highly mobile vehicular
context. Traditional beam training is inadequate in satisfying low overheads
and latency. In this paper, we propose to combine machine learning tools and
situational awareness to learn the beam information (power, optimal beam index,
etc) from past observations. We consider forms of situational awareness that
are specific to the vehicular setting including the locations of the receiver
and the surrounding vehicles. We leverage regression models to predict the
received power with different beam power quantizations. The result shows that
situational awareness can largely improve the prediction accuracy and the model
can achieve throughput with little performance loss with almost zero overhead.

本文提供一种将机器学习工具和情境感知相结合的方法，在车辆环境中学习射总体信息（功率、最佳射总索引等），实验结果表明情境感知可以极大提高预测准确性和模型吞吐量，同时几乎不影响性能和性能损失。

基于情境感知的毫米波波束预测：一种机器学习方法

MmWave Beam Prediction with Situational Awareness: A Machine Learning  Approach

In millimeter-wave communication, large antenna arrays are required to
achieve high power gain by steering towards each other with narrow beams, which
poses the problem to efficiently search the best beam direction in the angle
domain at both Tx and Rx sides. As the exhaustive search is time consuming,
hierarchical search has been widely accepted to reduce the complexity, and its
performance is highly dependent on the codebook design. In this paper, we
propose two basic criteria for the hierarchical codebook design, and devise an
efficient hierarchical codebook by jointly exploiting sub-array and
deactivation (turning-off) antenna processing techniques, where closed-form
expressions are provided to generate the codebook. Performance evaluations are
conducted under different system and channel models. Results show superiority
of the proposed codebook over the existing alternatives.

该论文提出了一种基于子阵列和抑制天线处理技术的高效分层码簿设计方法，并给出了闭式表达式。该方法在不同的系统和信道模型下进行性能评估，结果表明该方法优于现有的替代方法。