channel metric design and uncertainty quantification

This study addresses a critical gap in communication-aware robotic planning, where existing approaches commonly rely on channel-level metrics to predict end-to-end 5G throughput—a practice lacking empirical validation in private 5G deployments. Conducted in a shielded underground industrial environment, the work integrates commercial ray-tracing simulations, Gaussian process regression with a rational quadratic kernel, a mobile robotic platform, and off-the-shelf 5G user equipment to perform real-world measurements. It reveals for the first time that dynamic adaptation of MIMO spatial layers is the primary cause of systematic overestimation of throughput by conventional channel models, with ray tracing significantly overpredicting performance even in line-of-sight conditions. In contrast, a data-driven approach that directly learns end-to-end throughput reduces prediction error by approximately two-thirds and exhibits near-zero bias, demonstrating clear superiority over traditional channel-centric modeling.

To address insufficient uncertainty quantification in path loss modeling and radio metric prediction for wireless networks, this paper introduces the first large-scale application of Conformal Prediction Systems (CPS) to cross-city signal strength modeling. We propose a novel CPS framework integrated with a difficulty estimator to dynamically calibrate prediction interval widths while guaranteeing 95% statistical coverage. Leveraging 2D map embeddings and machine learning–based path loss models, our Toronto-trained model achieves high coverage (≥95%) in Vancouver and Montreal, with RMSE decreasing significantly as sample difficulty decreases. This work breaks from conventional point-prediction paradigms by enabling interpretable, statistically rigorous, and strongly generalizable uncertainty quantification. It establishes a new paradigm for intelligent wireless network deployment grounded in reliable predictive uncertainty.

This work addresses the lack of a standardized observability framework in quantum networks, which hinders effective fault diagnosis and adaptive control. It proposes the first multidimensional performance metric system tailored for quantum networks, encompassing key parameters such as entanglement fidelity, quantum bit error rate, dark count rate, and timing jitter, while integrating environmental sensor data. Building on this foundation, the authors design and implement a non-intrusive, integrable real-time monitoring prototype, which has been deployed and validated at Oak Ridge National Laboratory. The system enables real-time data acquisition, performance alerting, and dynamic feedback, thereby establishing a critical observability infrastructure for quantum software-defined networking and autonomous control.

This paper addresses the challenge of real-time acquisition and exchange of Key Performance Indicators (KPIs) across multi-vendor equipment in 5G and beyond networks. We propose a KPI extraction and exchange framework compatible with both standardized/commercial components and proprietary tools. Leveraging 3GPP-standard interfaces (e.g., N4, N6, N11), we conduct systematic empirical comparisons of three KPI collection techniques—active probing, passive traffic mirroring, and API polling—across latency, sampling granularity (down to millisecond-level), signaling load sensitivity, and deployment overhead. To our knowledge, this is the first cross-vendor, multi-dimensional empirical evaluation that quantifies performance boundaries and identifies precise applicability conditions for each method. The proposed framework enables on-demand KPI acquisition and protocol-level interoperability, providing telecom operators with reusable, evidence-based guidelines for intelligent network operations and closed-loop optimization.

This work proposes a novel approach to wireless channel statistical prediction that integrates uncalibrated, open-source map-based digital twins with Gaussian processes. Existing methods either rely on dense measurements while neglecting environmental geometry or require costly calibration to leverage geometric information from digital twins. In contrast, the proposed method uniquely embeds geometric priors directly into the Gaussian process framework, enabling accurate, scene-wide channel statistics prediction from only a few real-world measurements. Furthermore, it employs Bayesian optimization to actively select optimal measurement locations, minimizing data acquisition effort. The approach significantly reduces measurement overhead while enhancing prediction accuracy, offering a practical and data-efficient channel modeling solution for resource-constrained wireless systems.

This study addresses the lack of accurate channel modeling for 3.4 GHz air-to-air (A2A) communications, which has hindered the design of unmanned aerial vehicle (UAV) communication systems. Leveraging an open-source, reconfigurable channel sounding platform built with USRP B210 and a GNSS-disciplined oscillator, the authors conducted spherical-trajectory flight experiments at the AERPAW Lake Wheeler testbed to systematically collect A2A channel measurements across varying altitudes, elevation angles, and relative headings. This work presents the first comprehensive characterization of sub-6 GHz A2A channel properties at 3.4 GHz and introduces a geometry-aware fading model that explicitly incorporates real flight trajectories. The study quantifies the relationship between RMS delay spread and link geometry and publicly releases both the lightweight sounding platform and the measured dataset, providing a reliable foundation for simulation, protocol design, and performance evaluation of UAV communication systems.

This work addresses the methodological fragility and limited verifiability inherent in complex Internet measurement analyses, which traditionally rely on manual orchestration by experts. To overcome these limitations, the authors propose the first multi-agent framework tailored for Internet measurement, capable of collaboratively generating verifiable measurement workflows. The framework encodes five decades of domain knowledge into a reasoning-enabled knowledge graph and integrates a methodology validation engine with a tool registry to automatically recommend and verify technical approaches. Evaluated across four case studies, the system autonomously produces workflows comparable to those crafted by experts, makes sound architectural decisions, effectively tackles novel problems lacking ground truth, and uncovers methodological flaws undetectable by conventional testing practices.

This work addresses the limitations of the 3GPP-standardized tapped delay line (TDL) channel model in accurately capturing the spatial propagation characteristics of MIMO systems, which can lead to biased performance evaluations. To overcome this, the authors propose and validate a reduced cluster delay line (rCDL) model. Through comparative analysis against real-world channel measurements in representative commercial scenarios, the study evaluates the spatial modeling accuracy of rCDL relative to TDL and further assesses their discriminative capability via CSI reporting performance simulations. Results demonstrate that rCDL significantly improves the fidelity of spatial channel characterization while maintaining reasonable computational complexity. It outperforms TDL in both measurement-to-model alignment and evaluation of CSI feedback schemes, thereby offering strong support for future 3GPP standardization efforts.

Internet measurement research suffers from an accessibility crisis due to tool fragmentation and high domain expertise requirements—especially during sudden network outages, where manually constructing diagnostic workflows (e.g., topology discovery, routing analysis, dependency modeling) is time-consuming and heavily reliant on expert knowledge. This paper introduces ArachNet, the first LLM-based agent system that automatically generates expert-level measurement workflows. Methodologically, it employs a novel four-role collaborative agent architecture enabling problem decomposition, tool orchestration, multi-framework integration, and closed-loop reasoning. Its key contribution lies in empirically uncovering and formalizing compositional regularities in measurement expertise—demonstrating their automation feasibility for the first time. Experiments show that ArachNet-generated workflows match expert quality, reducing complex analyses from days to minutes, thereby significantly improving diagnostic efficiency, reproducibility, and accessibility across the networking research community.

This work proposes a cross-layer, interpretable performance diagnosis method to address the challenge of detecting subtle radio-layer dynamic anomalies in O-RAN systems when end-to-end latency appears stable. Leveraging real-world measurements across multiple distances and user equipment (UE) types, the approach jointly analyzes application-layer tail latency—such as the 95th percentile—with radio-layer metrics including scheduling behavior, modulation and coding scheme (MCS), block error rate (BLER), and signal quality to construct lightweight “degradation flags.” The method enables non-intrusive yet effective detection of radio-layer performance degradation, revealing the sensitivity of tail latency to UE type, distance, and network load. This facilitates practical and efficient fault localization and monitoring in O-RAN deployments.