Channel charting builds a map of the radio environment in an unsupervised
way. The obtained chart locations can be seen as low-dimensional compressed
versions of channel state information that can be used for a wide variety of
applications, including beam prediction. In non-standalone or cell-free
systems, chart locations computed at a given base station can be transmitted to
several other base stations (possibly operating at different frequency bands)
for them to predict which beams to use. This potentially yields a dramatic
reduction of the overhead due to channel estimation or beam management, since
only the base station performing charting requires channel state information,
the others directly predicting the beam from the chart location. In this paper,
advanced model-based neural network architectures are proposed for both channel
charting and beam prediction. The proposed methods are assessed on realistic
synthetic channels, yielding promising results.

通过引入先进的模型化神经网络结构，本文提出了用于信道绘图和波束预测的方法，并在实际合成信道上进行评估，取得了有希望的结果。

基于信道图的模型化深度学习用于波束预测

Model-based Deep Learning for Beam Prediction based on a Channel Chart

Leveraging sensory information to aid the millimeter-wave (mmWave) and
sub-terahertz (sub-THz) beam selection process is attracting increasing
interest. This sensory data, captured for example by cameras at the
basestations, has the potential of significantly reducing the beam sweeping
overhead and enabling highly-mobile applications. The solutions developed so
far, however, have mainly considered single-candidate scenarios, i.e.,
scenarios with a single candidate user in the visual scene, and were evaluated
using synthetic datasets. To address these limitations, this paper extensively
investigates the sensing-aided beam prediction problem in a real-world
multi-object vehicle-to-infrastructure (V2I) scenario and presents a
comprehensive machine learning-based framework. In particular, this paper
proposes to utilize visual and positional data to predict the optimal beam
indices as an alternative to the conventional beam sweeping approaches. For
this, a novel user (transmitter) identification solution has been developed, a
key step in realizing sensing-aided multi-candidate and multi-user beam
prediction solutions. The proposed solutions are evaluated on the large-scale
real-world DeepSense $6$G dataset. Experimental results in realistic V2I
communication scenarios indicate that the proposed solutions achieve close to
$100\%$ top-5 beam prediction accuracy for the scenarios with single-user and
close to $95\%$ top-5 beam prediction accuracy for multi-candidate scenarios.
Furthermore, the proposed approach can identify the probable transmitting
candidate with more than $93\%$ accuracy across the different scenarios. This
highlights a promising approach for nearly eliminating the beam training
overhead in mmWave/THz communication systems.

利用传感信息，基于机器学习的框架在真实场景下对车联网基础设施间通信的波束预测问题进行研究，通过利用视觉和位置数据，成功预测了优化波束指标，高达 95% 的准确率，为减少毫米波 / 太赫兹通信系统的波束训练开销提供了有前景的方法。

基于相机的毫米波波束预测：多候选真实场景

Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World  Scenarios

Millimeter wave (mmWave) and terahertz (THz) drones have the potential to
enable several futuristic applications such as coverage extension, enhanced
security monitoring, and disaster management. However, these drones need to
deploy large antenna arrays and use narrow directive beams to maintain a
sufficient link budget. The large beam training overhead associated with these
arrays makes adjusting these narrow beams challenging for highly-mobile drones.
To address these challenges, this paper proposes a vision-aided machine
learning-based approach that leverages visual data collected from cameras
installed on the drones to enable fast and accurate beam prediction. Further,
to facilitate the evaluation of the proposed solution, we build a synthetic
drone communication dataset consisting of co-existing wireless and visual data.
The proposed vision-aided solution achieves a top-$1$ beam prediction accuracy
of $\approx 91\%$ and close to $100\%$ top-$3$ accuracy. These results
highlight the efficacy of the proposed solution towards enabling highly mobile
mmWave/THz drone communication.

该论文提出了一种基于视觉数据的机器学习方法来帮助无线电通信的快速和准确的波束预测，并证明了该方法可以提高高移动 mmWave / THz 无人机的通信效率。

毫米波相机无人机：计算机视觉辅助的无线波束预测

Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam  Prediction

Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless
communication applications requires overcoming the critical challenges
associated with the large antenna arrays deployed at these systems. In
particular, adjusting the narrow beams of these antenna arrays typically incurs
high beam training overhead that scales with the number of antennas. To address
these challenges, this paper proposes a multi-modal machine learning based
approach that leverages positional and visual (camera) data collected from the
wireless communication environment for fast beam prediction. The developed
framework has been tested on a real-world vehicular dataset comprising
practical GPS, camera, and mmWave beam training data. The results show the
proposed approach achieves more than $\approx$ 75\% top-1 beam prediction
accuracy and close to 100\% top-3 beam prediction accuracy in realistic
communication scenarios.

提出了一种基于多模态机器学习的方法，利用无线通信环境中收集到的位置和视觉数据快速预测光束方向，实现了超过 75％ 的准确度和近乎 100％ 的前 3 名光束预测准确度。