Supporting high mobility in millimeter wave (mmWave) systems enables a wide
range of important applications such as vehicular communications and wireless
virtual/augmented reality. Realizing this in practice, though, requires
overcoming several challenges. First, the use of narrow beams and the
sensitivity of mmWave signals to blockage greatly impact the coverage and
reliability of highly-mobile links. Second, highly-mobile users in dense mmWave
deployments need to frequently hand-off between base stations (BSs), which is
associated with critical control and latency overhead. Further, identifying the
optimal beamforming vectors in large antenna array mmWave systems requires
considerable training overhead, which significantly affects the efficiency of
these mobile systems. In this paper, a novel integrated machine learning and
coordinated beamforming solution is developed to overcome these challenges and
enable highly-mobile mmWave applications. In the proposed solution, a number of
distributed yet coordinating BSs simultaneously serve a mobile user. This user
ideally needs to transmit only one uplink training pilot sequence that will be
jointly received at the coordinating BSs using omni or quasi-omni beam
patterns. These received signals draw a defining signature not only for the
user location, but also for its interaction with the surrounding environment.
The developed solution then leverages a deep learning model that learns how to
use these signatures to predict the beamforming vectors at the BSs. This
renders a comprehensive solution that supports highly-mobile mmWave
applications with reliable coverage, low latency, and negligible training
overhead. Simulation results show that the proposed deep-learning coordinated
beamforming strategy approaches the achievable rate of the genie-aided solution
that knows the optimal beamforming vectors with no training overhead.

本文提出一种整合了机器学习和协调波束成形技术的解决方案，以克服毫米波系统中窄波束的使用以及高度移动用户在基站之间切换等挑战，并支持高度移动的毫米波应用，同时实现可靠的覆盖、低延迟和可忽略的训练开销。

高移动毫米波系统的深度学习协同波束成形

Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave  Systems

In this paper, we propose a learning-based low-overhead channel estimation
method for coordinated beamforming in ultra-dense networks. We first show
through simulation that the channel state information (CSI) of geographically
separated base stations (BSs) exhibits strong non-linear correlations in terms
of mutual information. This finding enables us to adopt a novel learning-based
approach to remotely infer the quality of different beamforming patterns at a
dense-layer BS based on the CSI of an umbrella control-layer BS. The proposed
scheme can reduce channel acquisition overhead by replacing pilot-aided channel
estimation with the online inference from an artificial neural network, which
is fitted offline. Moreover, we propose to exploit joint learning of multiple
CBSs and involve more candidate beam patterns to obtain better performance.
Simulation results based on stochastic ray-tracing channel models show that the
proposed scheme can reach an accuracy of 99.74% in settings with 20 beamforming
patterns.

本文提出了一种基于学习的低开销信道估计方法，在超密集网络的协调波束赋形中使用，可以通过人工神经网络在线推导出不同波束赋形的质量，从而替换需要大量的导频辅助信道估计，多个基站联合同时学习并增加候选波束，实现更好的性能。实验结果显示，在具有 20 个波束赋形的设置中，该方案的准确率可达 99.74％。