e-FLASH

The increasing availability of multimodal data holds many promises for developments in millimeter-wave (mmWave) multiple-antenna systems by harnessing the potential for enhanced situational awareness. Specifically, inclusion of non-RF modalities to complement RF-only data in communications-related decisions like beam selection may speed up decision making in situations where an exhaustive search, spanning all candidate options, is required by the standard. However, to accelerate research in this topic, there is a need to collect real-world datasets in a principled manner. This article presents an experimentally obtained dataset, composed of 23 GB of data, which aids in beam selection in vehicle-to-everything mmWave bands, with the goal of facilitating machine learning (ML) in the wireless communication required for autonomous driving. Beyond this specific example, the article describes methodologies of creating such datasets that use time synchronized and heterogeneous types of LiDAR, GPS, and camera images, paired with the RF ground truth data of selected beams in the mmWave band. While we use beam selection as the primary demonstrator, we also discuss how multimodal datasets may be used in other ML-based PHY-layer optimization areas, such as beamforming and localization.

随着多模态数据的日益可得，充分利用其增强情境感知的潜力，为毫米波（mmWave）多天线系统的进步带来了诸多承诺。具体而言，将非射频模态纳入仅包含射频数据的通信相关决策中，如波束选择，可能加快在标准要求对所有候选选项进行详尽搜索的情境下的决策过程。然而，为了加速该领域的研究，有必要以严谨的方式收集现实世界的数据集。本文提出了一组实验获得的数据集，该数据集由23 GB的数据组成，有助于车辆到万物（V2X）毫米波频段的波束选择，旨在促进自动驾驶所需的无线通信中的机器学习（ML）。超越这一具体示例，文章还描述了创建此类数据集的方法，这些方法使用时间同步和异构类型的激光雷达（LiDAR）、全球定位系统（GPS）和摄像头图像，并配以毫米波频段所选波束的射频真实数据。虽然我们以波束选择作为主要示范，但也讨论了多模态数据集如何在其他基于机器学习的物理层优化领域得到应用，例如波束成形和定位。

搜集汇总

背景与挑战

背景概述

e-FLASH是一个23GB的多模态实验数据集，专注于毫米波多天线系统的波束选择，旨在通过结合非RF数据（如LiDAR、GPS和相机图像）来加速自动驾驶中的无线通信机器学习研究。数据集由Jerry Gu提交，包含时间同步的异构数据，适用于波束选择和物理层优化等研究领域。