MEASUREMENT METHODOLOGY

5G HO Measurement Tool. We extend 5G Tracker [52] to capture several key pieces of information relevant to mobility management in commercial 5G: PCIs, HOs, and radio bands. The above information is extracted from 5G-specific APIs introduced in Android 11 [16]. Regarding the last item, we use the onDisplayInfoChanged() API of Android TelephonyManager to identify the radio band (low-band vs. mmWave) of the UE. Our app also logs additional information such as UE’s geolocation, radio technology (4G/LTE vs. 5G), ping measurements, etc.

为了捕获与5G移动性管理相关的关键信息，我们扩展了5G Tracker工具。该工具利用Android 11引入的5G特定API，提取物理单元ID（PCI）、切换事件和无线频段等信息。我们使用Android TelephonyManager的onDisplayInfoChanged() API来识别用户设备（UE）的无线频段（例如低频段与毫米波）。此外，我们的应用程序还记录UE的地理位置、无线技术（4G/LTE与5G）、ping测量等其他信息。

5G UE and Other Measurement Tools. We use two UE models: Samsung Galaxy S21 Ultra 5G/SM-G998U (S21U) and Samsung Galaxy S20 Ultra 5G/SM-G988U (S20U). A total of four mobile phones (three S21U and one S20U) are used in our study. They are equipped with the Qualcomm Snapdragon 888 and 865 chipsets, respectively [25, 26]. The radio hardware profile of these chipsets represent the state-of-the-art, and the measurement findings hold true for other 5G smartphone models, especially Qualcomm models. To ensure a fair comparison among carriers, we place multiple smartphones side-by-side to concurrently conduct experiments and make external factors (e.g., driving speed, location, etc.) remain consistent. Acquiring and parsing lower layer information from smartphones requires access to Diag (diagnostic interface), which needs special licenses and tools [23]. Therefore, we rely on a professional tool called Accuver XCAL [15] to read Qualcomm Diag. This tool runs on a laptop and can collect physical layer radio KPIs (e.g., PCI, RRS values) and RRC layer signaling messages [10] (such as HO commands, event configurations, measurement reports, etc.). For power measurements, we use Monsoon Power Monitor [22] to power a high-end S20U smartphone. Note that all experiments except power measurements use S21U.

5G and 4G Networks. Our analysis focuses on three dimensions: (1) 5G Carriers: We collected data across three major U.S. 5G carriers (OpX, OpY, OpZ). (2) Radio Access Technologies (RAT): We compare different radio technologies (LTE vs. NSA 5G vs. SA 5G). At the time of this study, both OpX and OpZ had deployed their 5G services in NSA while OpY was in both SA and NSA modes. (3) Radio Frequency Bands: The bands considered in this study were dictated by how carriers rolled out their services in the areas we covered. In 5G-NR, we capture mmWave and low-band data for OpX and OpZ. For OpY, we collect data from their mid-band and low-band 5G deployments. Additionally, the 4G/LTE dataset contains low-band and mid-band ranges for all carriers.

我们使用两种5G用户设备：三星Galaxy S21 Ultra 5G和三星Galaxy S20 Ultra 5G。总共使用四部手机，其中三部为S21U，一部为S20U。这些设备配备了高通骁龙888和865芯片组，代表了当前的技术水平。为了确保在不同运营商之间的公平比较，我们将多部智能手机并排放置，以保持外部因素（例如驾驶速度、位置等）的一致性。获取和解析智能手机的底层信息需要访问诊断接口，这需要特殊许可和工具。因此，我们使用专业工具Accuver XCAL来读取高通诊断信息。该工具在笔记本电脑上运行，可以收集物理层无线KPI（例如PCI、RRS值）和RRC层信令消息（例如切换命令、事件配置、测量报告等）。对于功耗测量，我们使用Monsoon Power Monitor为高端S20U智能手机供电。除功耗测量外，所有实验均使用S21U。

Drive Tests. To conduct drive-tests across major cities and interstate freeways in the U.S., we tether three S21U smartphones - one for each carrier - to a laptop running XCAL via USB3 cables (Fig. 3). As summarized in Table 1, our field trip covers a total travel distance of 6,200 km+. The city data mostly comprises of dense deployments and mmWave 5G coverage, while the inter-state data loosely represents suburban deployments and Low-Band 5G coverage. This helps us understand key mobility configurations employed by commercial 5G networks and their impacts in a large scale. Most of the data is collected while driving. For analysis where walking data is used, we mention it before discussing the results.

为了在美国主要城市和州际高速公路上进行驾驶测试，我们将三部S21U智能手机（每个运营商一部）通过USB3线缆连接到运行XCAL的笔记本电脑（如图3所示）。如表1所总结，我们的实地考察覆盖了超过6,200公里的行驶距离。城市数据主要包括密集部署和毫米波5G覆盖，而州际数据则代表了郊区部署和低频段5G覆盖。这有助于我们在大规模上理解商用5G网络采用的关键移动性配置及其影响。绝大部分数据是在驾驶过程中收集的。在分析中使用步行数据时，我们会在讨论结果之前进行说明。

Profiling Applications under Mobility. In order to understand the impact of mobility on application QoE, we utilize three existing mobile applications shown in Fig. 3: (i) real-time volumetric video streaming leverages a state-of-the-art system (ViVo) [40], (ii) cloud gaming adopts three popular games cloud-powered on Steam Remote Play [28], and (iii) live video conferencing utilizes a popular application, Zoom [31]. The detailed experimental setup can be found in Appendix A.2. All the applications are tested with OpX (NSA Low-Band, NSA mmWave, and LTE) while driving. UDP/TCP Experiments. Using a bulk transfer application iPerf3 [12], we study the impact of mobility on transport layer performance. We use two flavors of TCP congestion control: CUBIC [30] and BBR [29]. The iPerf server runs on an AWS EC2 instance (g4dn.2xlarge | 8vCPUs | 32GB | Ubuntu 18.04) with 3 Gbps+ network bandwidth. The server captures iPerf logs, packet traces (pcap) and socket statistics (ss) logs [21]. On the UE, we run the iPerf client (cross-complied within 5G Tracker) and collect its logs.

TL; DR

上面太长了，没啥营养，重点就是:

5G和4G网络

我们的分析关注三个维度：

1. 5G运营商：我们在美国三家主要5G运营商（OpX、OpY、OpZ）上收集数据。
2. 无线接入技术（RAT）：我们比较了不同的无线技术（LTE、NSA 5G和SA 5G）。在研究期间，OpX和OpZ部署了NSA 5G，而OpY同时部署了SA和NSA模式。
3. 无线频段：研究中使用的频段由运营商在我们覆盖区域的部署情况决定。在5G-NR中，我们为OpX和OpZ捕获了毫米波和低频段数据，而OpY则提供了中频段和低频段5G部署数据。此外，4G/LTE数据集包含所有运营商的低频段和中频段范围。

在移动性下分析应用程序

为了解移动性对应用程序服务质量（QoE）的影响，我们利用了三个现有的移动应用程序（如图3所示）：

（i）实时体积视频流媒体利用了一种最先进的系统（ViVo）

（ii）云游戏采用了Steam Remote Play上流行的三款游戏

（iii）实时视频会议使用了一款流行的应用程序Zoom。详细的实验设置可以在附录A.2中找到。

所有应用程序均在驾驶过程中与OpX（NSA低频段、NSA毫米波和LTE）一起测试。

UDP/TCP实验

使用批量传输应用程序iPerf3，我们研究了移动性对传输层性能的影响。我们使用了两种TCP拥塞控制算法：CUBIC和BBR。

1) iPerf服务器运行在AWS EC2实例（g4dn.2xlarge | 8vCPUs | 32GB | Ubuntu 18.04）上，具有3 Gbps以上的网络带宽。服务器捕获iPerf日志、数据包跟踪（pcap）和套接字统计（ss）日志

2) UE上，我们运行iPerf客户端（在5G Tracker中交叉编译）并收集其日志