The cellular network has undergone rapid progress since its inception in 1980s. While rapid iteration of newer generations of cellular technology plays a key role in this evolution, the incremental and eventually wide deployment of every new technology generation also plays a vital role in delivering the promised performance improvement. In this work, we conduct the first metamorphosis study of a cellular network generation, 5G, by measuring the user-experienced 5G performance from 5G network's birth (initial deployment) to maturity (steady state). By analyzing a 4-year 5G performance trace of 2.65M+ Ookla® ~Speedtest Intelligence® ~measurements collected in 9 cities in the United States and Europe from January 2020 to December 2023, we unveil the detailed evolution of 5G coverage, throughput, and latency at the quarterly granularity, compare the performance diversity across the 9 representative cities, and gain insights into compounding factors that affect user-experienced 5G performance, such as adoption of 5G devices and the load on the 5G network. Our study uncovers the typical life-cycle of a new cellular technology generation as it undergoes its ''growing pain'' towards delivering its promised QoE over the previous technology generation.

In 2022, 3 years after the initial 5G rollout, through a cross-country US driving trip (from Los Angeles to Boston), the authors of [28] conducted an in-depth measurement study of user-perceived experience (network coverage, performance, and QoE of a set of major 5G ''killer'' apps) over all three major US carriers. The study revealed disappointingly low 5G coverage and suboptimal network performance -- falling short of the expectations needed to support the new generation of 5G ''killer apps. Now, five years into the 5G era, widely considered its midlife, 5G networks are expected to deliver stable and mature performance. In this work, we replicate the 2022 study along the same coast-to-coast route, evaluating the current state of cellular coverage and network and application performance across all three major US operators. While we observe a substantial increase in 5G coverage and a corresponding boost in network performance, two out of three operators still exhibit less than 50% 5G coverage along the driving route even five years after the initial 5G rollout. We expand the scope of the previous work by analyzing key lower-layer KPIs that directly influence the network performance. Finally, we introduce a head-to-head comparison with Starlink's LEO satellite network to assess whether emerging non-terrestrial networks (NTNs) can complement the terrestrial cellular infrastructure in the next generation of wireless connectivity.

Despite 5G’s promise of enhanced capacity and lower latency, recent measurement studies have shown that users experience significant cellular performance degradation under vehicular mobility. In this paper, we go beyond prior work that merely characterizes 5G performance, by uncovering the underlying mechanisms responsible for throughput degradation episodes during vehicular mobility. Through extensive measurements across 3,687 km of driving routes in the U.S., we collect granular performance and network KPI data from three major carriers across diverse radio access technologies. We introduce a novel KPI-driven clustering methodology that not only quantifies the frequency and duration of performance degradations, but also critically identifies their root causes by analyzing KPIs and their interactions. Our analysis reveals previously unidentified patterns: persistent higher degradation rates in uplink versus downlink, technology-specific vulnerability signatures in LTE and 5G deployments, and the predominance of compound degradation mechanisms where multiple factors interact to create severe throughput reductions. These findings provide essential information for developing mobility-aware resource management strategies to address the unique challenges of vehicular connectivity.

After a rapid deployment worldwide over the past few years, 5G is expected to have reached a mature deployment stage to provide measurable improvement of network performance and user experience over its predecessors. In this study, we aim to assess 5G deployment maturity via three conditions: (1) Does 5G performance remain stable over a long time span (1 year)? (2) Does 5G provide better performance than its predecessor Long-Term Evolution (LTE)? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting two year-long measurement campaigns of 5G uplink performance leveraging a custom Android app: one crowd-sourced, cross-sectional campaign spanning 8 major cities in 7 countries and two different continents (Europe and North America), and one controlled campaign focusing on mmWave deployment at a fixed location in the downtown area of Boston, MA. Our datasets show that 5G deployment in major cities appears to have matured, with no major performance improvements observed over a one-year period, but 5G does not provide consistent, superior measurable performance over LTE, especially in terms of latency, and further there exists clear uneven 5G performance across the 8 cities. Our study suggests that, while 5G deployment appears to have stagnated, it is short of delivering its promised performance and user experience gain over its predecessor.

As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments. This requires hardware acceleration to scale the Physical (PHY) layer performance, programmable elements in the Radio Access Network (RAN) and intelligent controllers at the edge, careful planning of the Radio Frequency (RF) environment, as well as end-to-end integration and testing. In this paper, we describe how we developed the programmable X5G testbed, addressing these challenges through the deployment of the first 8-node network based on the integration of NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA), OpenAirInterface (OAI), and a near-real-time RAN Intelligent Controller (RIC). The Aerial Software Development Kit (SDK) provides the PHY layer, accelerated on Graphics Processing Unit (GPU), with the higher layers from the OAI open-source project interfaced with the PHY through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). An E2 agent provides connectivity to the O-RAN Software Community (OSC) nearreal-time RIC. We discuss software integration, network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, as well as up to 25 emulated User Equipments (UEs), measuring a cell rate higher than 1.65 Gbps in downlink and 143 Mbps in uplink.

After 4 years of rapid deployment in the US, 5G is expected to have significantly improved the performance and overall user experience of mobile networks. However, recent measurement studies have focused either on static performance or a single aspect (e.g., handovers) under driving conditions of 5G, and do not provide a complete picture of cellular network performance today under driving conditions - a major use case of mobile networks. Through a cross-continental US driving trip (from LA to Boston, 5700km+), we conduct an in-depth measurement study of user-perceived experience (network coverage/performance and QoE of a set of major latency-critical 5G "killer'' apps) To understand the root cause of the observed network performance, while collecting low-level 5G statistics and signaling messages. Our study shows disappointingly low coverage of 5G networks today under driving and highly fragmented coverage by cellular technologies. More importantly, network and application performance are often poor under driving even in areas with full 5G coverage. We also examine the correlation of technology-wise coverage and performance with geo-location and the vehicle's speed and analyze the impact of a number of lower layer KPIs on network performance.

For its stringent QoE requirement, augmented reality (AR) has been widely hailed as a representative of ultra-high bandwidth and ultra-low latency apps that will be enabled by 5G networks/edge clouds. Such a portrait of AR by the telco and cloud industry raises an important research question - can 5G enable latency-critical applications such as (edge-assisted) AR? In this paper, we conduct to our knowledge the first in-depth measurement study of whether 5G mmWave in combination with in-network edge cloud can support the baseline edge-assisted object detection. After we discover 5G mmWave is unlikely to achieve the level of uplink network performance needed to support a baseline edge-assisted object detection implementation in the near future, we quantify the performance benefits in retrofitting app-level optimizations developed in the pre-5G era on top of baseline edge-assisted object detection, as well as the performance benefits from hardware upgrade on the edge. We find that these optimizations can significantly boost object detection performance over both LTE and 5G mmWave; however, the improvement with 5G mmWave over LTE is marginal, and 5G mmWave still fails to provide satisfactory performance in all scenarios under consideration. Overall, we conclude that today's 5G mmWave deployment is not a deciding factor in enabling edge-assisted object detection.

5G networks are being deployed rapidly across the world and have opened door to many uplink-oriented, bandwidth-intensive applications such as Augmented Reality and Connected Autonomous Vehicles. However, the roll-out is still in the early phase and the nature of deployment also varies across different geographic regions of the world. In this demo, we present NextG-UP, an Android-based tool designed to help understand the performance and evolution of 5G networks around the world. The crowd-sourcing mobile app collects various cellular network metrics and runs a short uplink throughput/latency test.

5G networks are being deployed rapidly across the world and have opened door to many uplink-oriented, bandwidth-intensive applications such as Augmented Reality and Connected Autonomous Vehicles (CAV). However, the roll-out is still in the early phase and the nature of deployment also varies across different geographic regions of the world. In this demo, we present NextG-UP, an open-source Android-based tool designed to help understand the performance and evolution of 5G networks around the world. The crowd-sourcing mobile app collects various cellular network metrics and runs a short uplink throughput/latency test.

Future WLAN devices will combine both IEEE 802.11ad and 802.11ac interfaces. The former provides multi-Gbps rates but is susceptible to blockage, whereas the latter is slower but offers reliable connectivity. A fundamental challenge is thus how to combine those complementary technologies, to make the most of the advantages they offer. In this work, we leverage Multipath TCP (MPTCP) to use both interfaces simultaneously in order to achieve a higher overall throughput as well as seamlessly switch to a single interface when the other one fails. We find that standard MPTCP often performs sub-optimally and can yield a throughput much lower than that of single path TCP over the faster of the two interfaces. We analyze the cause of these performance issues in detail and then designMuSher, an agile MPTCP scheduler that allows MPTCP to fully utilize the channel resources available to both interfaces. Our evaluation in realistic scenarios shows thatMuShercan provide a throughput improvement of 50%/130% under WLAN/Internet settings respectively, compared to the default MPTCP scheduler. It further speeds up the recovery of a traffic stream after disruption by a factor of 8x/75x.