Remote driving, orteleoperating AutonomousVehicles (AVs), is a key application that emerging 5G networks aim to support. In this paper, we conduct a systematic feasibility study of AV teleoperation over commercial 5G networks from bothcross-layerandend-to-end (E2E)perspectives. Given the critical importance oftimely delivery of sensor data, such as camera and LiDAR data, for AV teleoperation, we focus in particular on the performance of uplink sensor data delivery. We analyze the impact of Physical Layer (PHY layer) 5G radio network factors, including channel conditions, radio resource allocation, and Handovers (HOs), on E2E latency performance. We also examine the impact of 5G networks on the performance of upper-layer protocols and E2E application Quality-of-Experience (QoE) of the adaptation mechanisms used for real-time sensor data delivery, such as Real-Time Streaming Protocol (RTSP) and Web Real Time Communication (WebRTC). Our study reveals the challenges posed by today’s 5G networks and the limitations of existing sensor data streaming mechanisms. The insights gained will help inform the co-design of future-generation wireless networks, edge cloud systems, and applications to overcome the low-latency barriers in AV teleoperation.

Roaming provides users with voice and data connectivity when traveling abroad. This is particularly the case in Europe where the "Roam like Home" policy established by the European Union in 2017 has made roaming affordable. Nonetheless, due to various policies employed by operators, roaming can incur considerable performance penalties as shown in past studies of 3G/4G networks. As 5G provides significantly higher bandwidth, how does roaming affect user-perceived performance? We present, to the best of our knowledge, the first comprehensive and comparative measurement study of commercial 5G in four European countries.Our measurement study is unique in the way it makes it possible to link key 5G mid-band channels and configuration parameters ("policies") used by various operators in these countries with their effect on the observed 5G performance from the network (in particular, the physical and MAC layers) and applications perspectives. Our measurement study not only portrays users’ observed quality of experience when roaming, but also provides guidance to optimize the network configuration and to users and application developers in choosing mobile operators. Moreover, our contribution provides the research community with the largest cross-country roaming 5G dataset to stimulate further research.

5G in mid-bands has become the dominant deployment of choice in the world. We present - to the best of our knowledge - the first comprehensive and comparative cross-country measurement study of commercial mid-band 5G deployments in Europe and the U.S., filling a gap in the existing 5G measurement studies. We unveil the key 5G mid-band channels and configuration parameters used by various operators in these countries, and identify the major factors that impact the observed 5G performance both from the network (physical layer) perspective as well as the application perspective. We characterize and compare 5G mid-band throughput and latency performance by dissecting the 5G configurations, lower-layer parameters as well as deployment settings. By cross-correlating 5G parameters with the application decision process, we demonstrate how 5G parameters affect application QoE metrics and suggest a simple approach for QoE enhancement. Our study sheds light on how to better configure and optimize 5G mid-band networks, and provides guidance to users and application developers on operator choices and application QoE tuning. We released the datasets and artifacts at https://github.com/SIGCOMM24-5GinMidBands/artifacts.

While anonymity networks like Tor aim to protect the privacy of their users, they are vulnerable to traffic analysis attacks such as Website Fingerprinting (WF) and Flow Correlation (FC). Recent implementations of WF and FC attacks, such as Tik-Tok and DeepCoFFEA, have shown that the attacks can be effectively carried out, threatening user privacy. Consequently, there is a need for effective traffic analysis defense. There are a variety of existing defenses, but most are either ineffective, incur high latency and bandwidth overhead, or require additional infrastructure. As a result, we aim to design a traffic analysis defense that is efficient and highly resistant to both WF and FC attacks. We propose DeTorrent, which uses competing neural networks to generate and evaluate traffic analysis defenses that insert 'dummy' traffic into real traffic flows. DeTorrent operates with moderate overhead and without delaying traffic. In a closed-world WF setting, it reduces an attacker's accuracy by 61.5%, a reduction 10.5% better than the next-best padding-only defense. Against the state-of-the-art FC attacker, DeTorrent reduces the true positive rate for a .00001 false positive rate to about .12, which is less than half that of the next-best defense. We also demonstrate DeTorrent's practicality by deploying it alongside the Tor network and find that it maintains its performance when applied to live traffic.

With 5G's support for diverse radio bands and different deployment modes, e.g., standalone (SA) vs. non-standalone (NSA), mobility management - especially the handover process - becomes far more complex. Measurement studies have shown that frequent handovers cause wild fluctuations in 5G throughput, and worst, service outages. Through a cross-country (6,200 km+) driving trip, we conduct in-depth measurements to study the current 5G mobility management practices adopted by three major U.S. carriers. Using this rich dataset, we carry out a systematic analysis to uncover the handover mechanisms employed by 5G carriers, and compare them along several dimensions such as (4G vs. 5G) radio technologies, radio (low-, mid- & high-)bands, and deployment (SA vs. NSA) modes. We further quantify the impact of mobility on application performance, power consumption, and signaling overheads. We identify key challenges facing today's NSA 5G deployments which result in unnecessary handovers and reduced coverage. Finally, we design a holistic handover prediction system Prognos and demonstrate its ability to improve QoE for two 5G applications 16K panoramic VoD and realtime volumetric video streaming. We have released the artifacts of our study at https://github.com/SIGCOMM22-5GMobility/artifact.

We conduct to our knowledge a first measurement study of commercial 5G performance on smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one mid-band carrier) in three U.S. cities. We conduct extensive field tests on 5G performance in diverse urban environments. We systematically analyze the handoff mechanisms in 5G and their impact on network performance. We explore the feasibility of using location and possibly other environmental information to predict the network performance. We also study the app performance (web browsing and HTTP download) over 5G. Our study consumes more than 15 TB of cellular data. Conducted when 5G just made its debut, it provides a “baseline” for studying how 5G performance evolves, and identifies key research directions on improving 5G users’ experience in a cross-layer manner. We have released the data collected from our study (referred to as 5Gophers) at https://fivegophers.umn.edu/www20.