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.

Accurate localization in dense urban areas remains a significant challenge due to the limitations of Global Navigation Satellite Systems (GNSS) in environments with obstacles and reflections, such as urban canyons. While the most recent 3GPP standards offer sophisticated network-centric positioning techniques, their widespread deployment will take time and is hindered by high infrastructure costs and complexity. In this work, we present mm-NOLOC, a UE-centric localization system, designed as a practical fallback when GNSS fails to deliver high accuracy, that leverages the growing deployment of 5G mmWave infrastructure in dense urban areas. Unlike traditional approaches, mm-NOLOC operates independently of 3GPP location support and utilizes only standardized control-plane information collected solely on the UE side – Synchronization Signal Block (SSB) Indices that are mapped to 5G mmWave beam directions – to obtain robust position estimations. To address the uncertainty introduced by urban multipath, mm-NOLOC models the SSB-to-angle relationship as a discrete and multimodal distribution, based on empirical measurements in operational 5G mmWave networks, and uses a particle filter to refine position estimates by integrating probabilistic observations with UE-side motion dynamics. We validate mm-NOLOC through experiments over commercial 5G mmWave deployments, as well as trace-based simulations. Our results show that mm-NOLOC achieves a median localization error below 3 m and a 95th percentile error below 10 m, offering a practical fallback localization solution in urban canyon scenarios for 5G networks without network location support.

5G, officially rolled out in 2019, has been rapidly deployed by major mobile network operators across the globe. Key advancements in 5G such as higher-order modulation, massive MIMO, wider channels, and higher operating frequencies, have set high expectations over the predecessor technology LTE, especially for enabling high-bandwidth and latency-sensitive applications such as Mixed Reality (XR), Connected Autonomous Vehicles (CAVs), and 360° video streaming — often referred to as 5G "killer" apps. While 5G's technical promises have fueled widespread excitement, understanding its true impact requires a closer look at the real-world performance delivered to end users.

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.

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.

Five years after the initial 5G rollout, several research works have analyzed the performance of operational 5G mmWave networks. However, these measurement studies primarily focus on single-user performance, leaving the sharing and resource allocation policies largely unexplored. In this paper, we fill this gap by conducting the first systematic study, to our best knowledge, of resource allocation policies of current 5G mmWave mobile network deployments through an extensive measurement campaign across four major US cities and two major mobile operators. Our study reveals that resource allocation among multiple flows is strictly governed by the cellular operators and flows are not allowed to compete with each other in a shared queue. Operators employ simple threshold-based policies and often over-allocate resources to new flows with low traffic demands or reserve some capacity for future usage. Interestingly, these policies vary not only among operators but also for a single operator in different cities. We also discuss a number of anomalous behaviors we observe in our experiments across different cities and operators.

Ubiquitous connectivity is vital for emerging applications like extended reality, factory automation, and robotics, necessitating low latency, high data rates, and reliability in both downlink and uplink. From the network protocol perspective, successfully supporting these new use cases hinges on the network being resilient enough to address the heterogeneous demand in dynamic channel conditions. To assess the performance of legacy 5G networks for these applications, we focus on the physical (PHY) layer and analyze the existing 5G time division duplexing (TDD) method in terms of throughput. Our preliminary experiments with 3rd Generation Partnership Protocol (3GPP) compliant Matlab 5G toolbox reveal limitations of the fixed configuration of the PHY frames, that are typically used by commercial 5G networks, hindering adaptability to heterogeneous demands and compromising quality of service (QoS). To overcome this, we propose a machine learning-enabled optimization framework facilitating proactive PHY frame reconfiguration based on realtime prediction of wireless channel metrics computed at User Equipment (UE). Implementation and validation of our approach on the 3GPP-compliant Open Air Interface (OAI) 5G testbed demonstrate the practicality of our solution and its adherence to 3GPP standards. Overall, our dynamic PHY frame configuration approach consistently meets overall traffic demands better than any fixed configuration across various scenarios, while also having the lowest percentage of un-transmitted bytes in each scenario.

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.

Despite the surge in commercially available multiplayer mixed reality (XR) devices and applications, few studies have focused on the player experience during co-located, multiplayer gameplay in XR environments. To address this gap, we designed and developed Escape from Kyle-Earth, a co-located, multiplayer XR game that can be played in both AR and VR using a head-mounted display. We then conducted a user study with 26 participants, in which each participant played both versions of the game. Our results indicate that VR evoked a stronger sense of presence while its AR counterpart increased co-presence between players. However, there was no significant difference in game enjoyment between the two platforms. Our work contributes to the burgeoning literature on co-located immersive gaming.

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.

5G mmWave is being rapidly deployed by all major mobile operators. With the technology still in its infancy, several early research works analyze the performance of operational 5G mmWave networks. Nonetheless, these measurement studies primarily focus on single-user performance, leaving the sharing and resource allocation policies largely unexplored. In this paper, we fill this gap by conducting the, to our best knowledge, first systematic study of resource allocation policies of current 5G mmWave mobile network deployments through an extensive measurement campaign across four major US cities and two major mobile operators. Our study reveals that resource allocation among multiple flows is strictly governed by the cellular operators and flows are not allowed to compete with each other in a shared queue. Operators employ simple threshold-based policies and often over-allocate resources to new flows with low traffic demands or reserve some capacity for future usage. Interestingly, these policies vary not only among operators but also for a single operator in different cities. We also discuss a number of anomalous behaviors we observed in our experiments across different cities and operators.

We present an extensive experimental evaluation of the performance and power consumption of the 60 GHz IEEE 802.11ad technology on commercial smartphones. We also compare 802.11ad against its main competitors in the 5 GHz band - 802.11ac and, for first time, 802.11ax, on mobile devices. Our performance comparison focuses on two aspects that have not been extensively studied before: (i) dense multi-client and multi-AP topologies and (ii) popular mobile applications under realistic mobility patterns. Our power consumption study covers both non-communicating and communicating modes. We also present the first study of the power saving mode in 802.11ad-enabled smartphones and its impact on performance. Our results show that 802.11ad is better able to address the needs of emerging bandwidth-intensive applications in smartphones than its 5 GHz counterparts. At the same time, we identify several key research directions towards realizing its full potential.

We conduct one of the first extensive experimental studies of the two main link adaptation mechanisms in 60 GHz WLANs, namely rate adaptation and beam adaptation. We first show, using a variety of commodity 60 GHz devices, that simple heuristics, used by these devices to determine which the two mechanisms should be triggered, can lead to wrong decisions even in seemingly simple scenarios. We then explore for first time the feasibility of leveraging PHY layer information and ML to guide link adaptation, using a large dataset collected with a 60 GHz software-define radio testbed in a variety of indoor environments and scenarios. Finally, we design LiBRA, a practical, standard-compliant link adaptation framework that leverages ML and PHY layer information to determine when to trigger link adaptation and which adaptation mechanism to use. LiBRA strikes a balance between throughput and link recovery delay, performing close to an oracle solution, and outperforming significantly two simple heuristics used by off-the-shelf devices.