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.

5G networks promise to transform our technology experience by delivering ultra-high speeds and low latency, enabling applications like Augmented Reality (AR) and Connected Autonomous Vehicles (CAVs). However, 5G’s higher frequencies reduce its range and lead to performance inconsistencies, especially for users on the move. Moreover, the energy consumption of 5G is significantly higher than its predecessor, 4G, raising sustainability concerns. In this paper, we explore a solution that combines the strengths of Integrated Sensing and Communication (ISAC) with the advanced analytics capabilities of the Network Data Analytics Function (NWDAF) in 5G networks. We leverage two new functions, Sensing Service Function (SSF) and Energy Efficiency Control Function (EECF), designed to work together to make smarter, more energy-efficient network decisions. By optimizing base station downlink transmit power, our approach not only reduces energy consumption but also carefully balances the trade-offs between latency and energy efficiency. Our findings suggest a promising path toward a greener and more reliable future for 5G and beyond networks.

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.

By aggregating multiple channels, Carrier Aggregation (CA) is an important technology for boosting cellular network bandwidth. Given diverse radio bands made available in 5G networks, CA plays a particularly critical role in achieving the goal of multi-Gbps throughput performance. In this paper, we carry out a timely comprehensive measurement study of CA deployment in commercial 5G networks (as well as 4G networks). We identify the key factors that influence whether CA is deployed and when, as well as which band combinations are used. Thus, we reveal the challenges posed by CA in 5G performance analysis and prediction as well as their implications in application quality-of-experience (QoE). We argue for and develop a novel CA-aware deep learning framework, dubbed Prism5G, which explicitly accounts for the complexity introduced by CA to more effectively predict 5G network throughput performance. Through extensive evaluations, we demonstrate the superiority of Prism5G over existing throughput prediction algorithms. Prism5G improves 5G throughput prediction accuracy by over 14% on average and a maximum of 22%. Using two use cases as examples, we further illustrate how Prism5G can aid applications in optimizing QoE performance.

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.

Unlike previous generation cellular technologies, 5G networks support diverse radio bands from low-band, mid-band to (mmWave) high-band, and offer a wide variety of new and enhanced features. In particular, 3GPP 5G standards adopt a flow-based 5G Quality-of-Service (QoS) framework that allows more flexibility in mapping QoS "flows" to data radio bearers. Nonetheless, the 5G QoS classes are pre-defined and QoS treatment is limited to the "flow" level. As we will argue in an earlier paper, the 5G QoS framework cannot fully and intelligently utilize the diversity of 5G radio bands and other capabilities to cope with fast varying channel conditions, and is therefore inadequate in meeting the quality-of-experience (QoE) requirements of many emerging applications such as augmented/virtual realities (AR/VR) and connected and autonomous vehicles (CAV). This has led us to advance a novel software-defined, fine-grained QoS framework for 5G/NextG networks.In this "work in progress" paper, we share our initial experience in prototyping the proposed fine-grained QoS framework. Our framework extends both the 5G core network and 5G radio access network (RAN) functionality to enable intelligent control of radio resources in a fashion that exploits application semantics to improve user QoE. We discuss in detail about the changes in different systems and its individual components, share the current state of implementation progress (work completed and in-progress) and finally our evaluation plan to validate the framework when the implementation is complete.

As the plethora of Internet of Things (IoT) devices gradually make their way into our lives, several Cloud Service Providers (CSPs) have developed IoT gateway platforms (SDKs) that solely connects IoT devices to their respective cloud. Such gateways have 1) cumbersome IoT device configuration; 2) inflexible IoT data managements; and 3) support no/little cross-vendor edge computation and cloud analytics. We term these commercial gateway SDKs as cloud-centric. In this paper, we study the state-of-the-art vendor-locked IoT Gateway solutions and approaches and propose an edge-centric paradigm through an evolutionary framework, dubbed VeerEdge for developing IoT gateways. We leverage computing and storage capabilities at the network edge for edge-based device & IoT service management and data processing. We exploit availability of multiple cloud services for "best" IoT data analytics. Evaluation results show that VeerEdge achieves this with negligible overhead in terms of latency, CPU and RAM usage when compared to state-of-the-art industrial IoT gateways.

There is a growing demand for real-time analysis of network data streams. In recent years, Model Driven Telemetry (MDT) has been developed - in place of conventional methods such as Simple Network Management Protocol (SNMP), Syslog and CLI commands - to provide a fine-grain holistic view of a network at the control, data and management planes. High-frequency MDT data streams generated from network devices enable new ways of designing Network Operation and Management (OAM) solutions, laying the foundation for future "self-driving" networks.In this paper we study anomaly detection using MDT data streams in a data center environment. In many commercial data centers, BGP is re-purposed for (policy-driven, path-based) intra-routing (as opposed to inter-domain routing that it was originally designed for) to take advantage of rich path diversity. Several vendors have developed MDT data models using YANG that allow routers/switches to express and stream various BGP features for (centralized) network OAM operations. We develop a systematic MDT data processing and feature selection framework that is portable to multiple MDT vendors. Furthermore, we advance NetCorDenstream that builds and improves upon OutlierDenStream proposed in [10] for real-time detection of streamed anomalous MDT data. We show that NetCorDenstream achieves a 59% reduction in alarms raised when compared with OutlierDenStream, thereby reducing the (attention) burden placed on network operators. In particular, it increases alarm detection precision significantly while decreasing false alarms at the expense of a slightly delayed response time.

The emerging 5G services offer numerous new opportunities for networked applications. In this study, we seek to answer two key questions: i) is the throughput of mmWave 5G predictable, and ii) can we build "good" machine learning models for 5G throughput prediction? To this end, we conduct a measurement study of commercial mmWave 5G services in a major U.S. city, focusing on the throughput as perceived by applications running on user equipment (UE). Through extensive experiments and statistical analysis, we identify key UE-side factors that affect 5G performance and quantify to what extent the 5G throughput can be predicted. We then propose Lumos5G -- a composable machine learning (ML) framework that judiciously considers features and their combinations, and apply state-of-the-art ML techniques for making context-aware 5G throughput predictions. We demonstrate that our framework is able to achieve 1.37X to 4.84X reduction in prediction error compared to existing models. Our work can be viewed as a feasibility study for building what we envisage as a dynamic 5G throughput map (akin to Google traffic map). We believe this approach provides opportunities and challenges in building future 5G-aware apps.