Berlin V2X

The Berlin V2X dataset offers high-resolution GPS-located wireless measurements across diverse urban environments in the city of Berlin for both cellular and sidelink radio access technologies, acquired with up to 4 cars over 3 days. The data enables thus a variety of different ML studies towards vehicle-to-anything (V2X) communication.The data includes information onphysical layer parameters (such as signal strength and signal quality)cellular radio resource management like cell identity, carrier aggregation and assigned resource blockswireless Quality of Service (QoS) like delay and throughput (for cellular) or packet error rate (for sidelink)positioning information.The datasets are labelled and pre-filtered for a fast on-boarding and applicability. The measurement methodology pursues an application to Machine Learning (ML) for tasks such as QoS prediction, transfer learning, proactive radio resource allocation or link selection, among others.

柏林V2X数据集提供高分辨率GPS定位无线测量数据，采集自柏林市内各类城市场景，覆盖蜂窝与侧链路（sidelink）两种无线接入技术，采集工作由至多4辆测试车辆在3天内完成。该数据集可支撑面向车到万物（Vehicle-to-Anything, V2X）通信的多样化机器学习研究。数据集包含以下多类信息：物理层参数（如信号强度、信号质量）、蜂窝无线资源管理相关数据（如小区标识、载波聚合及分配的资源块）、无线服务质量（Quality of Service, QoS）指标（蜂窝链路下为时延与吞吐量，侧链路下为分组错误率）以及定位信息。所有数据集均经过标注与预滤波处理，可实现快速部署与实际应用适配。本数据集的采集方案旨在适配机器学习（Machine Learning, ML）应用场景，可支撑服务质量预测、迁移学习、主动无线资源分配及链路选择等多项任务。

搜集汇总

背景与挑战

背景概述

Berlin V2X数据集提供了柏林市多种城市环境下高分辨率GPS定位的无线测量数据，包括蜂窝和侧链无线接入技术，适用于车辆到万物(V2X)通信的机器学习研究。数据集包含物理层参数、无线服务质量(QoS)、GPS定位信息等，并经过标记和预过滤以便快速应用。