arXiv subjects

James V. Krogmeier

Publications and source records attributed to James V. Krogmeier.

At least 19 recordsLinked to original sources

arXiv2026

Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement

Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless positioning based on time of arrival (TOA) and received signal strength (RSS) measurements to handle two distinct scenarios: (i) known approximated target location, for which we conduct dynamic sensor selection to minimize the positioning error; and (ii) unknown approximated target location, in which the worst-case positioning error is minimized via robust sensor selection. We derive expressions for the Cramér-Rao lower bound (CRLB) as a performance metric to quantify the positioning accuracy resulted from selected sensors. For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions. These selection strategies are shown to strike an efficient balance between computational complexity and performance suboptimality. For robust sensor selection, we show that the conventional convex relaxation approach leads to instability, and then develop three algorithms based on (i) iterative convex optimization (ICO), (ii) difference of convex functions programming (DCP), and (iii) discrete monotonic optimization (DMO). Each of these strategies exhibits a different tradeoff between computational complexity and optimality guarantee. Simulation results show that the proposed sensor selection strategies provide significant improvements in terms of accuracy and/or complexity compared to existing sensor selection methods.

eess.SP↗

arXiv2026

Simulation-Driven Ensemble Machine Learning for Robust and Generalizable Path Loss Prediction

Machine learning has emerged as a promising approach to path loss prediction, yet its effectiveness often degrades when measurement data are scarce. To address this limitation, we propose an ensemble-based machine learning framework that integrates real measurements with synthetic data generated using a lidar-based simulator. The simulator provides broad spatial coverage through static path loss values that capture terrain variations and physical obstacles in the propagation environment. A dynamically weighted ensemble then combines simulation results with measured data, balancing the contribution of both data sources and improving generalization across diverse environments. To further mitigate the effects of limited measurements, we incorporate the Synthetic Minority Over-sampling Technique (SMOTE), a data augmentation technique that synthesizes additional samples through interpolation between measurements while preserving their statistical properties. By leveraging simulation data, SMOTE, and engineered propagation features, the proposed framework captures geographical and physical variability, enabling adaptability across urban, suburban, residential, industrial, and rural environments. Experimental results demonstrate that the proposed method achieves up to a 50% reduction in mean absolute error (MAE), compared with models trained solely on real data, and up to a 25% improvement relative to models trained exclusively on synthetic data, particularly for cross-environment generalization. These findings highlight the effectiveness of combining simulation-based synthetic data with SMOTE to overcome data scarcity and enhance the model's generalization ability. Overall, the proposed framework provides a robust and practical solution for path loss prediction across diverse environments with limited measurement data, supporting cost-effective planning and optimization of wireless networks.

eess.SP↗

arXiv2026

CITYMPC: A Large-Scale Physics-Informed Benchmark and Tool for Generative Complete Multipath Wireless Channel Modeling

Multipath wireless channels are fully characterized by multipath components (MPCs), including complex channel gain, propagation delay, angle of departure (AoD) and angle of arrival (AoA) in azimuth and elevation. Generating these parameters with the fidelity of ray tracing (RT) remains an open problem. Existing methods either incur the computational cost of RT or require explicit 3D scene geometry at inference. We present CITYMPC, a conditional variational autoencoder (cVAE) that predicts the complete per-path MPC parameter set from point-of-view imagery and terrain height maps alone, achieving environment-aware channel generation without access to any three-dimensional scene geometry at inference. Trained and evaluated across five urban environments spanning 427,397 links, CITYMPC matches RT ground truth to within 1.29 dB received power mean absolute error (MAE) and 7.25 ns $τ_0$ MAE. CITYMPC is a generative channel modeling framework and reproducible benchmark, released together with a large-scale multi-city ray-traced dataset to accelerate future scene-conditioned channel modeling research. We further analyze cross-city distribution shift to characterize the per-city diversity of the benchmark.

eess.SP↗

arXiv2026

Integrated Polarimetric Sensing and Communication with Polarization-Reconfigurable Arrays

Polarization diversity offers a cost- and space-efficient solution to enhance the performance of integrated sensing and communication systems. Polarimetric sensing exploits the signal's polarity to extract details about the target such as shape, pose, and material composition. From a communication perspective, polarization diversity can enhance the reliability and throughput of communication channels. This paper proposes an integrated polarimetric sensing and communication (IPSAC) system that jointly conducts polarimetric sensing and communications. We study the use of single-port polarization-reconfigurable antennas to adapt to channel depolarization effects, without the need for separate RF chains for each polarization. We address two core sensing tasks in IPSAC systems, target parameter estimation and target detection. For parameter estimation, we consider the problem of minimizing the mean-squared error (MSE) of the target depolarization parameter estimate, which is a critical task for various polarimetric radar applications such as rainfall forecasting, vegetation identification, and target classification. To address this nonconvex problem, we apply semi-definite relaxation (SDR) and majorization-minimization (MM) optimization techniques. Next, we consider a design that maximizes the target SINR leveraging prior knowledge of the target and clutter depolarization statistics to enhance the target detection performance. To tackle this problem, we modify the solution developed for MSE minimization subject to the same quality-of-service (QoS) constraints. Extensive simulations show that the proposed polarization reconfiguration method substantially improves the depolarization parameter MSE. Furthermore, the proposed method considerably boosts the target SINR due to polarization diversity, particularly in cluttered environments.

eess.SP↗

arXiv2025

Joint UAV Placement and Transceiver Design in Multi-User Wireless Relay Networks

In this paper, a novel approach is proposed to improve the minimum signal-to-interference-plus-noise-ratio (SINR) among users in non-orthogonal multi-user wireless relay networks, by optimizing the placement of unmanned aerial vehicle (UAV) relays, relay beamforming, and receive combining. The design is separated into two problems: beamforming-aware UAV placement optimization and transceiver design for minimum SINR maximization. A significant challenge in beamforming-aware UAV placement optimization is the lack of instantaneous channel state information (CSI) prior to deploying UAV relays, making it difficult to derive the beamforming SINR in non-orthogonal multi-user transmission. To address this issue, an approximation of the expected beamforming SINR is derived using the narrow beam property of a massive MIMO base station. Based on this, a UAV placement algorithm is proposed to provide UAV positions that improve the minimum expected beamforming SINR among users, using a difference-of-convex framework. Subsequently, after deploying the UAV relays to the optimized positions, and with estimated CSI available, a joint relay beamforming and receive combining (JRBC) algorithm is proposed to optimize the transceiver to improve the minimum beamforming SINR among users, using a block-coordinate descent approach. Numerical results show that the UAV placement algorithm combined with the JRBC algorithm provides a 4.6 dB SINR improvement over state-of-the-art schemes.

eess.SP↗

arXiv2025

Distributed Machine Learning Approach for Low-Latency Localization in Cell-Free Massive MIMO Systems

Low-latency localization is critical in cellular networks to support real-time applications requiring precise positioning. In this paper, we propose a distributed machine learning (ML) framework for fingerprint-based localization tailored to cell-free massive multiple-input multiple-output (MIMO) systems, an emerging architecture for 6G networks. The proposed framework enables each access point (AP) to independently train a Gaussian process regression model using local angle-of-arrival and received signal strength fingerprints. These models provide probabilistic position estimates for the user equipment (UE), which are then fused by the UE with minimal computational overhead to derive a final location estimate. This decentralized approach eliminates the need for fronthaul communication between the APs and the central processing unit (CPU), thereby reducing latency. Additionally, distributing computational tasks across the APs alleviates the processing burden on the CPU compared to traditional centralized localization schemes. Simulation results demonstrate that the proposed distributed framework achieves localization accuracy comparable to centralized methods, despite lacking the benefits of centralized data aggregation. Moreover, it effectively reduces uncertainty of the location estimates, as evidenced by the 95\% covariance ellipse. The results highlight the potential of distributed ML for enabling low-latency, high-accuracy localization in future 6G networks.

eess.SP↗

arXiv2025

Hybrid Fingerprint-based Positioning in Cell-Free Massive MIMO Systems

Recently, there has been an increasing interest in 6G technology for integrated sensing and communications, where positioning stands out as a key application. In the realm of 6G, cell-free massive multiple-input multiple-output (MIMO) systems, featuring distributed base stations equipped with a large number of antennas, present an abundant source of angle-of-arrival (AOA) information that could be exploited for positioning applications. In this paper we leverage this AOA information at the base stations using the multiple signal classification (MUSIC) algorithm, in conjunction with received signal strength (RSS) for positioning through Gaussian process regression (GPR). An AOA fingerprint database is constructed by capturing the angle data from multiple locations across the network area and is combined with RSS data from the same locations to form a hybrid fingerprint which is then used to train a GPR model employing a squared exponential kernel. The trained regression model is subsequently utilized to estimate the location of a user equipment. Simulations demonstrate that the GPR model with hybrid input achieves better positioning accuracy than traditional GPR models utilizing RSS-only and AOA-only inputs.

eess.SP↗

arXiv2025

Spatial-Division ISAC: A Practical Waveform Design Strategy via Null-Space Superimposition

Integrated sensing and communications (ISAC) is a key enabler of new applications, such as precision agriculture, extended reality (XR), and digital twins, for 6G wireless systems. However, the implementation of ISAC technology is very challenging due to practical constraints such as high complexity. In this paper, we introduce a novel ISAC waveform design strategy, called the spatial-division ISAC (SD-ISAC) waveform, which simplifies the ISAC waveform design problem by decoupling it into separate communication and radar waveform design tasks. Specifically, the proposed strategy leverages the null-space of the communication channel to superimpose sensing signals onto communication signals without interference. This approach offers multiple benefits, including reduced complexity and the reuse of existing communication and radar waveforms. We then address the problem of optimizing the spatial and temporal properties of the proposed waveform. We develop a low-complexity beampattern matching algorithm, leveraging a majorization-minimization (MM) technique. Furthermore, we develop a range sidelobe suppression algorithm based on manifold optimization. We provide comprehensive discussions on the practical advantages and potential challenges of the proposed method, including null-space feedback. We evaluate the performance of the proposed waveform design algorithm through extensive simulations. Simulation results show that the proposed method can provide similar or even superior performance to existing ISAC algorithms while reducing computation time significantly.

eess.SP↗

arXiv2025

Enhancing Pavement Sensor Data Acquisition for AI-Driven Transportation Research

Effective strategies for sensor data management are essential for advancing transportation research, especially in the current data-driven era, due to the advent of novel applications in artificial intelligence. This paper presents comprehensive guidelines for managing transportation sensor data, encompassing both archived static data and real-time data streams. The real-time system architecture integrates various applications with data acquisition systems (DAQ). By deploying the in-house designed, open-source Avena software platform alongside the NATS messaging system as a secure communication broker, reliable data exchange is ensured. While robust databases like TimescaleDB facilitate organized storage, visualization platforms like Grafana provide real-time monitoring capabilities. In contrast, static data standards address the challenges in handling unstructured, voluminous datasets. The standards advocate for a combination of cost-effective bulk cloud storage for unprocessed sensor data and relational databases for recording summarized analyses. They highlight the role of cloud data transfer tools like FME for efficient migration of sensor data from local storages onto the cloud. Further, integration of robust visualization tools into the framework helps in deriving patterns and trends from these complex datasets. The proposals were applied to INDOT's real-world case studies involving the I-65 and I-69 Greenfield districts. For real-time data collection, Campbell Scientific DAQ systems were used, enabling continuous generation and monitoring of sensor metrics. In the case of the archived I-69 database, summary data was compiled in Oracle, while the unprocessed data was stored in SharePoint. The results underline the effectiveness of the proposed guidelines and motivate their adoption in research projects.

cs.DB↗

arXiv2024

Ambient IoT: Communications Enabling Precision Agriculture

One of the most intriguing 6G vertical markets is precision agriculture, where communications, sensing, control, and robotics technologies are used to improve agricultural outputs and decrease environmental impact. Ambient IoT (A-IoT), which uses a network of devices that harvest ambient energy to enable communications, is expected to play an important role in agricultural use cases due to its low costs, simplicity, and battery-free (or battery-assisted) operation. In this paper, we review the use cases of precision agriculture and discuss the challenges. We discuss how A-IoT can be used for precision agriculture and compare it with other ambient energy source technologies. We also discuss research directions related to both A-IoT and precision agriculture.

eess.SP↗

arXiv2024

Constant Modulus Waveform Design with Block-Level Interference Exploitation for DFRC Systems

Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware. In this paper, we propose an algorithm for designing constant modulus waveforms for DFRC systems. Particularly, we jointly optimize the correlation properties and the spatial beam pattern. For communication, we employ constructive interference-based block-level precoding (CI-BLP) to exploit distortion due to multi-user and radar transmission. We propose a majorization-minimization (MM)-based solution to the formulated problem. To accelerate convergence, we propose an improved majorizing function that leverages a novel diagonal matrix structure. We then evaluate the performance of the proposed algorithm through rigorous simulations. Simulation results demonstrate the effectiveness of the proposed approach and the proposed majorizer.

eess.SP↗

arXiv2024

Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction

Machine learning (ML) offers a promising solution to pathloss prediction. However, its effectiveness can be degraded by the limited availability of data. To alleviate these challenges, this paper introduces a novel simulation-enhanced data augmentation method for ML pathloss prediction. Our method integrates synthetic data generated from a cellular coverage simulator and independently collected real-world datasets. These datasets were collected through an extensive measurement campaign in different environments, including farms, hilly terrains, and residential areas. This comprehensive data collection provides vital ground truth for model training. A set of channel features was engineered, including geographical attributes derived from LiDAR datasets. These features were then used to train our prediction model, incorporating the highly efficient and robust gradient boosting ML algorithm, CatBoost. The integration of synthetic data, as demonstrated in our study, significantly improves the generalizability of the model in different environments, achieving a remarkable improvement of approximately 12dB in terms of mean absolute error for the best-case scenario. Moreover, our analysis reveals that even a small fraction of measurements added to the simulation training set, with proper data balance, can significantly enhance the model's performance.

cs.LG↗

arXiv2023

Compressed Training for Dual-Wideband Time-Varying Sub-Terahertz Massive MIMO

6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access. Sub-THz communication comes with many existing challenges of mmWave communication and adds new challenges associated with the wider bandwidths, more antennas, and harsher propagations. Notably, the frequency- and spatial-wideband (dual-wideband) effects are significant at sub-THz. This paper presents a compressed training framework to estimate the time-varying sub-THz MIMO-OFDM channels. A set of frequency-dependent array response matrices are constructed, enabling channel recovery from multiple observations across subcarriers via multiple measurement vectors (MMV). Using the temporal correlation, MMV least squares (LS) is designed to estimate the channel based on the previous beam support, and MMV compressed sensing (CS) is applied to the residual signal. We refer to this as the MMV-LS-CS framework. Two-stage (TS) and MMV FISTA-based (M-FISTA) algorithms are proposed for the MMV-LS-CS framework. Leveraging the spreading loss structure, a channel refinement algorithm is proposed to estimate the path coefficients and time delays of the dominant paths. To reduce the computational complexity and enhance the beam resolution, a sequential search method using hierarchical codebooks is developed. Numerical results demonstrate the improved channel estimation accuracy of MMV-LS-CS over state-of-the-art techniques.

eess.SP↗

arXiv2023

Propagation Measurements and Analyses at 28 GHz via an Autonomous Beam-Steering Platform

This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28 GHz V2X propagation modeling. A pan-and-tilt subsystem facilitates uninhibited rotational mobility along the yaw and pitch axes, driven by open-loop servo units and orchestrated via inertial motion controllers. A geo-positioning subsystem augmented in accuracy by real-time kinematics enables navigation events to be shared between a transmitter and receiver over an Apache Kafka messaging middleware framework with fault tolerance. Herein, our system demonstrates a 3D geo-positioning accuracy of 17 cm, an average principal axes positioning accuracy of 1.1 degrees, and an average tracking response time of 27.8 ms. Crucially, fully autonomous antenna alignment and tracking facilitates continuous series of measurements, a unique yet critical necessity for millimeter wave channel modeling in vehicular networks. The power-delay profiles, collected along routes spanning urban and suburban neighborhoods on the NSF POWDER testbed, are used in pathloss evaluations involving the 3GPP TR38.901 and ITU-R M.2135 standards. Empirically, we demonstrate that these models fail to accurately capture the 28 GHz pathloss behavior in urban foliage and suburban radio environments. In addition to RMS direction-spread analyses for angles-of-arrival via the SAGE algorithm, we perform signal decoherence studies wherein we derive exponential models for the spatial/angular autocorrelation coefficient under distance and alignment effects.

eess.SP↗

arXiv2023

Data-Driven Web-Based Patching Management Tool Using Multi-Sensor Pavement Structure Measurements

Automating pavement maintenance suggestions is challenging,especially for actionable recommendations such as patching location,depth and priority.It is common practice among State agencies to manually inspect road segments of interest and decide maintenance requirements based on the pavement condition index (PCI).However,standalone PCI only evaluates the pavement surface condition and coupled with the variability in human perception of pavement distress,limits the accuracy and quality of current pavement maintenance practices.Here,a need for multi-sensor data integrated with standardized pavement distress condition ratings is required.This study explores the possibility of estimating the appropriate pavement patching strategy (i.e.,patching location,depth,and quantity) by integrating pavement structural and surface condition assessment with pavement specific ratings of distress.Especially,it combines pavement structural condition assessment parameter;falling weight deflectometer deflections along with surface condition assessment parameters;international roughness index,and cracking density for a better representation of overall pavement distress condition.Then,a pavement specific threshold-based patching suggestion algorithm is implemented to evaluate the pavement overall distress condition into a priority-based patching suggestion.The novelty in the use of pavement specific thresholds is placed on its data-driven ability to determine threshold values from current road condition measurements using a reliability concept validated by the theoretical pavement condition rating,pavement structural number.A web-based patching manager tool (PMT) was implemented to automate the patching suggestion procedure and visualize the results.Validated with road surface images obtained from three-dimensional laser sensors,PMT could successfully capture localized distresses in existing pavements.

eess.SP↗

arXiv2022

AGAPECert: An Auditable, Generalized, Automated, Privacy-Enabling Certification Framework with Oblivious Smart Contracts

This paper introduces AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private data and reporting real-time aggregate certification status without disclosing underlying private data. AGAPECert utilizes a novel mix of trusted execution environments, blockchain technologies, and a real-time graph-based API standard to provide automated, oblivious, and auditable certification. Our technique allows a privacy-conscious data owner to run pre-approved Oblivious Smart Contract code in their own environment on their own private data to produce Private Automated Certifications. These certifications are verifiable, purely functional transformations of the available data, enabling a third party to trust that the private data must have the necessary properties to produce the resulting certification. Recently, a multitude of solutions for certification and traceability in supply chains have been proposed. These often suffer from significant privacy issues because they tend to take a" shared, replicated database" approach: every node in the network has access to a copy of all relevant data and contract code to guarantee the integrity and reach consensus, even in the presence of malicious nodes. In these contexts of certifications that require global coordination, AGAPECert can include a blockchain to guarantee ordering of events, while keeping a core privacy model where private data is not shared outside of the data owner's own platform. AGAPECert contributes an open-source certification framework that can be adopted in any regulated environment to keep sensitive data private while enabling a trusted automated workflow.

cs.CR↗

arXiv2021

A Robotic Antenna Alignment and Tracking System for Millimeter Wave Propagation Modeling

In this paper, we discuss the design of a sliding-correlator channel sounder for 28 GHz propagation modeling on the NSF POWDER testbed in Salt Lake City, UT. Beam-alignment is mechanically achieved via a fully autonomous robotic antenna tracking platform, designed using commercial off-the-shelf components. Equipped with an Apache Zookeeper/Kafka managed fault-tolerant publish-subscribe framework, we demonstrate tracking response times of 27.8 ms, in addition to superior scalability over state-of-the-art mechanical beam-steering systems. Enhanced with real-time kinematic correction streams, our geo-positioning subsystem achieves a 3D accuracy of 17 cm, while our principal axes positioning subsystem achieves an average accuracy of 1.1 degrees across yaw and pitch movements. Finally, by facilitating remote orchestration (via managed containers), uninhibited rotation (via encapsulation), and real-time positioning visualization (via Dash/MapBox), we exhibit a proven prototype well-suited for V2X measurements.

eess.SP↗

arXiv2021

Challenges and Opportunities of Future Rural Wireless Communications

Broadband access is key to ensuring robust economic development and improving quality of life. Unfortunately, the communication infrastructure deployed in rural areas throughout the world lags behind its urban counterparts due to low population density and economics. This article examines the motivations and challenges of providing broadband access over vast rural regions, with an emphasis on the wireless aspect in view of its irreplaceable role in closing the digital gap. Applications and opportunities for future rural wireless communications are discussed for a variety of areas, including residential welfare, digital agriculture, and transportation. This article also comprehensively investigates current and emerging wireless technologies that could facilitate rural deployment. Although there is no simple solution, there is an urgent need for researchers to work on coverage, cost, and reliability of rural wireless access.

eess.SP↗