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5G wireless networks leverage complex scheduling, retransmission, and adaptation mechanisms to maximize their efficiency. These mechanisms interact to produce significant fluctuations in uplink and downlink capacity and latency, markedly impacting the the performance of real-time communication and multimedia applications, such as video conferencing. These applications are particularly sensitive to such fluctuations, resulting in lag, stuttering, distorted audio, and low video quality. In this paper, we present a cross-layer view of 5G networks and their impact on and interaction with video-conferencing applications. We conduct novel, detailed measurements of both private CBRS and commercial carrier cellular network dynamics, capturing physical- and link-layer events and correlating them with their effects at the network and transport layers, and the video-conferencing application itself. Our two datasets comprise days of low-rate campus-wide Zoom telemetry data, and hours of high-rate, correlated WebRTC-network-5G telemetry data. Based on these data, we trace performance anomalies back to root causes, identifying 24 previously unknown causal event chains that degrade 5G video conferencing. Armed with this knowledge, we build Domino, a tool that automates this process and is user-extensible to future wireless networks and interactive applications. 

Dataset release for the paper "Automated, Cross-Layer Root Cause Analysis of 5G Video-Conferencing Quality Degradation"; published in the proceedings of ACM IMC 2025. 

Release for IMC (v0.1). This repository provides the experimental setup and automation scripts used for measuring WebRTC performance over 5G networks. It reproduces the setup used in our paper, where: One WebRTC client runs on a Google Cloud VM, and the other client runs on a local machine connected via T-Mobile 5G. The setup includes video streaming via virtual cameras, synchronized system clocks, and data collection through tcpdump and custom packet parsers. 

NR-Scope is a 5G wireless network telemetry tool for 5G Standalone network measurement and performance optimization. NR-Scope decodes DCI, SIB and RACH information from a 5G Standalone (New Radio) base station. 

We present the design and implementation of WaveFlex, the first smart surface that enhances Private 5G networks operating under the shared-license framework in the Citizens Broadband Radio Service frequency band. WaveFlex works in the presence of frequency diversity: multiple nearby base stations operating on different frequencies, as dictated by a Spectrum Access System coordinator. It also handles time dynamism: due to the dynamic sharing rules of the CBRS band, base stations occasionally switch channels, especially when priority users enter the network. Finally, WaveFlex operates independently of the network itself, not requiring access to nor modification of the gNB or UEs, yet it remains compliant with and effective on prevailing cellular protocols. We have designed and fabricated WaveFlex on a custom multi-layer PCB, software defined radio based network monitor, and supporting control software and hardware. Our experimental evaluation benchmarks operational Private 5G and LTE networks running at full line rate. In a realistic indoor office scenario, 5G experimental results demonstrate an 8.58~dB average SNR gain, and an average throughput gain of 10.77 Mbps under a single gNB, and 12.84 Mbps under three gNBs, corresponding to throughput improvements of 18.4% and 19.5%, respectively. 

Rapid delay variations in today’s access networks impair the QoE of low-latency, interactive applications, such as video conferencing. To tackle this problem, we propose Athena, a framework that correlates high-resolution measurements from Layer 1 to Layer 7 to remove the fog from the window through which today’s video-conferencing congestion-control algorithms see the network. This cross-layer view of the network empowers the networking community to revisit and re-evaluate their network designs and application scheduling and rate-adaptation algorithms in light of the complex, heterogeneous networks that are in use today, paving the way for network-aware applications and application-aware networks. 

Abstract A major aim of gravitational wave astronomy is to test observationally the Kerr nature of black holes. The strongest such test, with minimal additional assumptions, is provided by observations of multiple ringdown modes, also known as black hole spectroscopy. For the gravitational wave merger event GW190521, we have previously claimed the detection of two ringdown modes emitted by the remnant black hole. In this paper we provide further evidence for the detection of multiple ringdown modes from this event. We analyse the recovery of simulated gravitational wave signals designed to replicate the ringdown properties of GW190521. We quantify how often our detection statistic reports strong evidence for a sub-dominant $(\ell ,m,n)=(3,3,0)$ringdown mode, even when no such mode is present in the simulated signal. We find this only occurs with a probability ∼0.02, which is consistent with a Bayes factor of $56\pm 1$(1σuncertainty) found for GW190521. We also quantify our agnostic analysis of GW190521, in which no relationship is assumed between ringdown modes, and find that only 1 in 250 simulated signals without a $(3,3,0)$mode yields a result as significant as GW190521. Conversely, we verify that when simulated signals do have an observable $(3,3,0)$mode they consistently yield a strong evidence and significant agnostic results. We also find that constraints on deviations from the $(3,3,0)$mode on GW190521-like signals with a $(3,3,0)$mode are consistent with what was obtained from our previous analysis of GW190521. Our results support our previous conclusion that the gravitational wave signal from GW190521 contains an observable sub-dominant $(\ell ,m,n)=(3,3,0)$mode.