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.

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.

Multiple-Input Multiple-Output (MIMO) systems have been presented for Terahertz (THz) communications to combat high path loss and enable Terabit-per-second (Tbps) links. The lack of experimental MIMO channel measurements, however, has restricted the majority of THz MIMO work to the theoretical realm. This paper presents a novel, first-of-its-kind correlation-based MIMO channel sounder with a timing reference for sub-THz communications, which enables broadband, long-range, and time-varying channel characterization of MIMO systems. Preliminary results from a conference room setting show that 28.5% of the tested scenarios can support spatial multiplexing, and even with beamforming, the capacity outperforms the SISO cases substantially.

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.

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.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in cellular downlink systems. The system model consists of a base station (BS) that needs to serve multiple users within a region of service. A subset of the users, especially those located close to the cell edge, undergo severe fading and suffer from poor channel quality and low achievable rates. To overcome this problem, CNOMA is proposed as the system design methodology, in which users that have the capability of full-duplex (FD) communication can assist the transmissions between the BS and users with poor channel quality through device-to-device (D2D) communications. To harness both the multiplexing gain from NOMA and the diversity gain from FD-D2D communications, we formulate and solve a novel optimization problem that jointly determines D2D user pairing and power allocation. The formulated problem is a mixed-integer non-linear program (MINLP) with prohibitively high complexity. To overcome this issue, a two-step policy is proposed to solve the problem in polynomial time. Our simulation results show that with reasonable assumptions, the proposed scheme always outperforms some existing schemes in the literature, and that, under undesirable conditions, e.g., poor D2D channel conditions or imperfect self-interference (SI) cancellation, the proposed scheme is reduced to conventional NOMA.

In this paper, we study the downlink transmission in a multi-UAV enabled wireless communication system where different types of contents are requested by ground users. Particularly, in the system, we consider a number of UAVs each equipped with a cache that is refreshed during off-peak hour to store some contents to be requested by ground users. To harness their full potential, a novel cooperative communication for the cache-enabled UAVs is presented, and we formulate an optimization problem that jointly decides the UAV placement as well as the transmit beamforming to maximize the number of users admitted to the system. Since the formulated problem is a mixed-integer non-convex program, which is generally known to be NP-hard, we resort to an appealing framework developed from the conventional difference-of-convex (DC) programming. Numerical results reveal the superiority of employing cooperative UAVs over non-cooperative ones and offer some insights into how the flexible deployment of cooperative UAVs benefits the system performance.

In this paper, we investigate downlink transmissions in a wireless communication system enabled by a swarm of unmanned aerial vehicles (UAVs) which are spatially dispatched to cooperatively deliver requested contents to ground users. First, we propose a communication scheme that exploits the flexible deployment of UAVs as well as their cooperative transmissions to improve in-network user admission. Unlike previous literature, a practical operational constraint of limited storage capacity for UAVs is considered. Then, from the knowledge that cooperation among UAVs depends on the availability of the contents in their limited storage space, we propose a novel joint optimization problem to determine the content placement, location planning, user admission decision and transmit beamforming to maximize the number of users experiencing a minimum required rate, so-called admitted users. Since the formulated problem is a mixed-integer non-linear program which is generally non-deterministic polynomial-time hard, we proposed a framework that is developed on the basis of difference-of-convex (DC) programming to transform the original problem into a series of approximate convex problems which can be iteratively solved until convergence. Our extensive simulation results reveal that the proposed scheme outperforms other schemes that have been introduced in previous work and reflect a notable trend that deploying more cooperative UAVs with fewer resources (power and storage capacity) is more efficient than deploying fewer UAVs with more resources. In particular, in one of our collected results, the total communication power can be reduced by roughly 40 dB when doubling the number of cooperative UAVs.