arXiv subjects

Ismail Guvenc

Publications and source records attributed to Ismail Guvenc.

At least 19 recordsLinked to original sources

arXiv2026

Adaptive 5G Resource Allocation for Multistatic ISAC-Based UAV Detection and Tracking

Unmanned aerial vehicles (UAVs) enable numerous commercial and public-safety applications, yet they also create security risks near critical infrastructure, transportation hubs, and restricted airspace. While integrated sensing and communications (ISAC) can leverage existing wireless networks for UAV surveillance, practical deployment must address competition between sensing and communication demands, as well as the challenges associated with tracking highly maneuverable UAVs with low radar cross section (RCS). This paper investigates adaptive multistatic ISAC for load-aware UAV detection and tracking in 5G wireless networks. A shared-resource framework is developed to quantify how sensing waveform length, sensing transmission rate, and beam allocation affect communication throughput in a 5G new radio (NR) system. Detection performance is analyzed using Zadoff-Chu (ZC) sensing waveforms, while tracking continuity is evaluated through an M-of-N detection model. To improve robustness under congestion, software-defined sensor (SDS) nodes exploit external signals of opportunity (SoO) to provide supplemental passive sensing opportunities when network resources become limited. Results show that adaptive sensing policies outperform fixed sensing reservations by preserving throughput under dynamic load while maintaining useful sensing capability. Under heavy congestion, SDS assistance substantially reduces tracking outage in the simulated scenarios. Cramer-Rao lower bound (CRLB) analysis demonstrates that multistatic sensing geometries improve localization accuracy and provide more uniform spatial coverage than monostatic sensing alone. These results highlight coordinated adaptive sensing and distributed multistatic support as a practical path toward resilient UAV surveillance in future wireless networks.

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arXiv2026

Analysis of Altitude-Dependent Electronic Conspicuity in Cellular-Connected UAVs

Unmanned aerial vehicles (UAVs) are increasingly integrated into cellular networks to support emerging Internet of Things (IoT) applications. In such settings, reliable communication is critical for electronic conspicuity (EC), enabling UAV detection and tracking in shared airspace. However, UAVs operate at elevated altitudes where enhanced line-of-sight (LOS) visibility leads to simultaneous exposure to multiple base stations, resulting in strong inter-cell interference. This article presents a system-level analysis of how UAV altitude influences the radio environment and affects EC reliability. Using spatial and network-level metrics, including serving distance, association behavior, and aggregate received power, we show that increasing altitude leads to stronger multi-cell interaction, reduced dominance of nearby sectors, and interference-dominated connectivity. These effects result in fragmented association regions and increased variability in link performance. The analysis is supported by measurement data from a helikite-based spectrum monitoring campaign and corresponding simulation results. Despite differences in experimental conditions, both approaches exhibit consistent altitude-dependent trends. These findings provide practical insights for designing altitude-aware and interference-aware cellular systems to support reliable UAV operation.

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arXiv2026

Altitude-Dependent RSRP and RSRQ Trade-offs in 5G NR UAV Networks

Cellular-connected unmanned aerial vehicles (UAVs) in 5G NR networks experience propagation and interference conditions that vary significantly with altitude and differ substantially from those experienced by terrestrial users. This is primarily caused by the down-tilted antenna sectors in 5G NR networks, which cause UAVs to be served (and interfered with) by the sidelobes. In this paper, we develop a 3GPP-compliant system-level framework for the consistent characterization of key performance indicators (KPIs) such as reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-interference-and-noise ratio (SINR) in a multi-site tri-sector deployment with realistic antenna patterns and probabilistic models for line-of-sight (LOS) and non-LOS (NLOS) conditions. Simulation results demonstrate that a critical transition for aerial users is experienced when going from coverage-limited to interference-limited conditions at higher altitudes. Although RSRP is affected by large-scale propagation characteristics and degrades gradually with increasing altitude and inter-site distance (ISD), SINR degrades much faster due to increased interference caused by LOS conditions. On the contrary, increasing ISD improves SINR and RSRQ due to lower interference, even as received power is reduced.

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arXiv2026

PRB-RUPFormer: A Recursive Unified Probabilistic Transformer for Residual PRB Forecasting

Accurate forecasting of residual Physical Resource Blocks (PRBs) is critical for proactive network slice provisioning, energy-efficient operation, and spectrum-aware decision making in cellular systems, where residual PRBs serve as a practical proxy for short- and medium-term spectrum availability. Existing PRB prediction methods typically rely only on historical PRB values and are trained independently per carrier or sector, limiting their ability to capture cross-carrier dependencies and providing no measure of forecast uncertainty. Moreover, point forecasts alone are insufficient for robust spectrum-aware control under highly variable traffic conditions. This paper proposes PRB-RUPFormer, a recursive unified probabilistic Transformer for residual PRB forecasting. The proposed model jointly processes multivariate KPI time series using temporal, seasonal, and carrier-aware embeddings, preserving inter-metric temporal coupling during recursive rollout and stabilizing long-horizon forecasting. A single shared model is trained across all carriers and sectors of an eNB, enabling efficient learning of joint traffic dynamics with low computational overhead. Forecast uncertainty is captured through quantile-based prediction intervals, providing confidence-aware estimates of future PRB availability. Evaluations on six months of commercial LTE network data from multiple U.S. locations demonstrate median MAE below 0.05 and hit probabilities above 0.80 for both one-day and seven-day recursive forecasts. These probabilistic predictions directly support spectrum-aware RAN functions such as dynamic carrier activation, congestion avoidance, and proactive spectrum sharing, making the proposed framework well-suited for dynamic spectrum access scenarios.

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arXiv2026

Aerial Booster-Cell Enabled Inter-Cell Interference Coordination for 5G NR Networks

Cellular-connected unmanned aerial vehicles (UAVs) operating in 5G New Radio (NR) macro networks experience severe and spatially non-uniform downlink interference. This is primarily caused by the interference from the sidelobes of downtilted base station (BS) antennas serving terrestrial users, which limits the ability of the network to provide uniform and high-quality coverage to aerial users. Supporting aerial users requires boosting the coverage of certain cells or sectors, which can further exacerbate inter-cell interference in dense macro deployments. This motivates the need for inter-cell interference coordination (ICIC) in multi-cell 5G NR networks serving both aerial and terrestrial users. In this work, we propose an ICIC framework that jointly optimizes antenna-domain coordination through BS uptilt angle optimization and time-domain interference coordination (TDIC) through NR-compliant scheduling. The framework is formulated as a multi-cell NR macro deployment problem that maximizes the minimum UAV signal-to-interference ratio (SIR) over a spatial grid of UAV locations while maintaining acceptable performance for ground user equipment (GUEs). The resulting optimization problem is non-convex and is solved using bio-inspired optimization techniques, including particle swarm optimization (PSO) and genetic algorithm (GA). Simulation results demonstrate that coordinated uptilt optimization with the booster-cell architecture significantly improves worst-case UAV SIR and downlink reliability in multi-cell 5G NR networks. booster-cell architecture significantly improves worst-case UAV SIR and downlink reliability in multi-cell 5G NR networks.

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arXiv2026

Multi-Year Spectral Structure of 6G Candidate Bands at 2.7 GHz and 4.4 GHz

Mid-band spectrum between 2 and 8 GHz is a critical resource for sixth-generation (6G) systems as it uniquely balances favorable propagation characteristics with scalable bandwidth. Recent U.S. policy highlights candidate bands near 2.7, 4.4, and 7.1 GHz, all of which host substantial federal and non-federal incumbency, including high-power radiolocation and aeronautical telemetry systems. Although these segments are being considered for potential relocation of federal incumbents to enable commercial use, their long-term viability depends on the structural integrity of the spectrum. In such environments, the practical value of spectrum depends on the reliability and contiguity of available spectrum opportunities. This paper presents a measurement-driven feasibility analysis of two representative segments, 2.69-2.9 GHz and 4.4-4.94 GHz, using Software-Defined Radio (SDR) measurements collected during Packapalooza campaigns from 2022 to 2025. Deployment-oriented metrics are introduced to quantify scan-window reliability (SWR), altitude-dependent usable spectrum availability ratio (USAR), largest contiguous clean bandwidth (LCCB), spectral fragmentation, and extreme interference excursions. The results reveal significant year-to-year structural variability. In the 2.69-2.9 GHz band, USAR remains near unity in 2022 and 2023, but drops to approximately 0.65 in 2024 and 0.8 in 2025, accompanied by fragmentation and limited contiguous bandwidth across altitudes. The 4.4-4.94 GHz band exhibits a similar temporal pattern, but with smaller reliability degradation and larger contiguous support, often exceeding several hundred megahertz even during incumbent-dominant periods. The results highlight that wideband feasibility in these candidate bands depends strongly on spectral contiguity and structural stability rather than nominal bandwidth alone.

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arXiv2026

UAV-Based 3D Spectrum Sensing: Insights on Altitude, Bandwidth, Trajectory, and Effective Antenna Patterns on REM Reconstruction

Spectrum sensing and the generation of 3D Radio Environment Maps (REMs) are essential for enabling spectrum sharing within cognitive radio networks. While Uncrewed Aerial Vehicles (UAVs) offer high-mobility 3D sensing, REM accuracy is challenged by dynamic flight behaviors, where fluctuations in UAV speed and direction introduce measurement inconsistencies. Furthermore, the airframe itself impacts the onboard antenna's radiation characteristics. In this paper, using real-world data, we systematically analyze how REM reconstruction accuracy is shaped by three key pillars: physical sensing parameters like altitude and bandwidth, environmental shadowing, and distortions caused by the UAV airframe. First, we benchmark diverse spatial prediction models, including simple Kriging (SK), ordinary Kriging (OK), trans-Gaussian Kriging, and Gaussian process regression (GPR). We demonstrate that while SK and its trans-Gaussian variant are highly accurate at extreme sample sparsity, OK improves as sample size increases, and GPR serves as the most stable overall baseline. Building on this, we propose a novel matrix completion (MC)-assisted GPR framework that enhances REM reconstruction in the presence of non-uniform spatial smoothness. The method operates by decomposing the REM into two distinct layers: a global smooth component and a highly varying local component. Our analysis based on real-world measurements reveals three key findings: 1) REM accuracy and shadowing variance follow a distinct tri-phasic trend as the UAV altitude increases; 2) REM accuracy significantly improves with increased spectrum bandwidth; and 3) antenna pattern calibration from in-field measurements significantly enhances REM accuracy by accounting for the effect of the UAV airframe.

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arXiv2026

3D Spectrum Awareness for Radio Dynamic Zones Using Kriging and Matrix Completion

Radio Dynamic Zones (RDZs) are geographically defined areas specifically allocated for testing new wireless technologies. It is essential to safeguard the regular spectrum users outside the zones from the interference caused by the deployed equipment within this zone. Previous works have utilized sparse reference signal received power (RSRP) measurements collected by unmanned aerial vehicles (UAVs) to construct a dense 3D radio map through ordinary Kriging. In this work, we illustrate that matrix completion can outperform ordinary Kriging. We partitioned a 2D area of interest into small square grids where each grid corresponds to a single entry of a matrix. The matrix completion algorithm learns the global structure of the radio environment map by leveraging the low-rank property of propagation maps. Additionally, we illustrate that the simple Kriging and trans-Gaussian Kriging yield better results when the density of known measurements is lower. Earlier works of RSRP prediction involved a training dataset at a single altitude. In this work, we also show that performance can be improved by utilizing a combined dataset from multiple altitudes.

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arXiv2026

GS-SBL: Bridging Greedy Pursuit and Sparse Bayesian Learning for Efficient 3D Wireless Channel Modeling

Robust cognitive radio development requires accurate 3D path loss models. Traditional empirical models often lack environment-awareness, while deep learning approaches are frequently constrained by the scarcity of large-scale training datasets. This work leverages the inherent sparsity of wireless propagation to model scenario-specific channels by identifying a discrete set of virtual signal sources. We propose a novel Greedy Sequential Sparse Bayesian Learning (GS-SBL) framework that bridges the gap between the computational efficiency of Orthogonal Matching Pursuit (OMP) and the robust uncertainty quantification of SBL. Unlike standard top-down SBL, which updates all source hyperparameters simultaneously, our approach employs a ``Micro-SBL'' architecture. We sequentially evaluate candidate source locations in isolation by executing localized, low-iteration SBL loops and selecting the source that minimizes the $L_2$ residual error. Once identified, the source and its corresponding power are added to the support set, and the process repeats on the signal residual to identify subsequent sources. Experimental results on real-world 3D propagation data demonstrate that the GS-SBL framework significantly outperforms OMP in terms of generalization. By utilizing SBL as a sequential source identifier rather than a global optimizer, the proposed method preserves Bayesian high-resolution accuracy while achieving the execution speeds necessary for real-time 3D path loss characterization.

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arXiv2026

Collection: UAV-Based Wireless Multi-modal Measurements from AERPAW Autonomous Data Mule (AADM) Challenge in Digital Twin and Real-World Environments

In this work, we present an unmanned aerial vehicle (UAV) wireless dataset collected as part of the AERPAW Autonomous Aerial Data Mule (AADM) challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) project. The AADM challenge was the second competition in which an autonomous UAV acted as a data mule, where the UAV downloaded data from multiple base stations (BSs) in a dynamic wireless environment. Participating teams designed flight control and decision-making algorithms for choosing which BSs to communicate with and how to plan flight trajectories to maximize data download within a mission completion time. The competition was conducted in two stages: Stage 1 involved development and experimentation using a digital twin (DT) environment, and in Stage 2, the final test run was conducted on the outdoor testbed. The total score for each team was compiled from both stages. The resulting dataset includes link quality and data download measurements, both in DT and physical environments. Along with the USRP measurements used in the contest, the dataset also includes UAV telemetry, Keysight RF sensors position estimates, link quality measurements from LoRa receivers, and Fortem radar measurements. It supports reproducible research on autonomous UAV networking, multi-cell association and scheduling, air-to-ground propagation modeling, DT-to-real-world transfer learning, and integrated sensing and communication, which serves as a benchmark for future autonomous wireless experimentation.

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arXiv2026

TransfoREM: Transformer aided 3D Radio Environment Mapping

Providing reliable cellular connectivity to Unmanned Aerial Vehicles (UAV) is a key challenge, as existing terrestrial networks are deployed mainly for ground-level coverage. The cellular network coverage may be available for a limited range from the antenna side lobes, with poor connectivity further exacerbated by UAV flight dynamics. In this work, we propose TransfoREM, a 3D Radio Environment Map (REM) generation method that combines deterministic channel models and real-world data to map terrestrial network coverage at higher altitudes. At the core of our solution is a transformer model that translates radio propagation mapping into a sequence prediction task to construct REMs. Our results demonstrate that TransfoREM offers improved interpolation capability on real-world data compared against conventional Kriging and other machine learning (ML) techniques. Furthermore, TransfoREM is designed for holistic integration into cellular networks at the base station (BS) level, where it can build REMs, which can then be leveraged for enhanced resource allocation, interference management, and spatial spectrum utilization.

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arXiv2026

Resilient UAV Data Mule via Adaptive Sensor Association under Timing Constraints

Unmanned aerial vehicles (UAVs) can be critical for time-sensitive data collection missions, yet existing research often relies on simulations that fail to capture real-world complexities. Many studies assume ideal wireless conditions or focus only on path planning, neglecting the challenge of making real-time decisions in dynamic environments. To bridge this gap, we address the problem of adaptive sensor selection for a data-gathering UAV, considering both the buffered data at each sensor and realistic propagation conditions. We introduce the Hover-based Greedy Adaptive Download (HGAD) strategy, designed to maximize data transfer by intelligently hovering over sensors during periods of peak signal quality. We validate HGAD using both a digital twin (DT) and a real-world (RW) testbed at the NSF-funded AERPAW platform. Our experiments show that HGAD significantly improves download stability and successfully meets per-sensor data targets. When compared with the traditional Greedy approach that simply follows the strongest signal, HGAD is shown to outperform in the cumulative data download. This work demonstrates the importance of integrating signal-to-noise ratio (SNR)-aware and buffer-aware scheduling with DT and RW signal traces to design resilient UAV data-mule strategies for realistic deployments.

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arXiv2025

Fusion of Cellular ISAC and Passive RF Sensing for UAV Detection and Tracking

The rapid growth of unmanned aerial vehicles (UAVs) in civilian and critical-infrastructure airspace has created a need for reliable detection and tracking systems that operate under diverse environmental and sensing conditions. This paper presents a UAV detection and tracking system that fuses measurements from a network of passive Keysight N6841A RF sensors and a Ku-band Fortem TrueView R20 radar operating in the FR3 spectrum (16.3 GHz) as an ISAC proxy. Real-world experiments at the NSF AERPAW testbed demonstrate that radar and RF sensing provide complementary strengths under varying geometric, range, and line-of-sight conditions. A Kalman filter using a constant-velocity motion model integrates the asynchronous 2D RF and 3D radar observations, suppressing large standalone errors, improving accuracy over individual modalities, and increasing tracking coverage without degrading performance. These results demonstrate the effectiveness of multi-modal, ISAC-oriented sensing for robust UAV tracking in outdoor environments.

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arXiv2025

Elevation- and Tilt-Aware Shadow Fading Correlation Modeling for UAV Communications

Future wireless networks demand a more accurate understanding of channel behavior to enable efficient communication with reduced interference. Uncrewed Aerial Vehicles (UAVs) are poised to play an integral role in these networks, offering versatile applications and flexible deployment options. However, accurately characterizing the shadow fading (SF) behavior in UAV communications remains a challenge. Traditional SF correlation models rely on spatial distance and neglect the UAV's 3D orientation and elevation angle. Yet even slight variations in pitch angle (5 to 10 degrees) can significantly affect the signal strength observed by a UAV. In this study, we investigate the impact of UAV pitch and elevation geometry on SF and propose an elevation- and tilt-aware spatial correlation model. We use a real-world fixed-altitude UAV measurement dataset collected in a rural environment at 3.32 GHz with a 125 kHz bandwidth. Results show that a 10-degree tilt-angle separation and a 20-degree elevation-angle separation can reduce the SF correlation by up to 15% and 40%, respectively. In addition, integrating the proposed correlation model into the ordinary Kriging (OK) framework for signal strength prediction yields an approximate 1.5 dB improvement in median RMSE relative to the traditional correlation model that ignores UAV orientation and elevation.

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arXiv2025

Energy Efficient Sleep Mode Optimization in 5G mmWave Networks via Multi Agent Deep Reinforcement Learning

Dynamic sleep mode optimization (SMO) in millimeter-wave (mmWave) networks is essential for maximizing energy efficiency (EE) under stringent quality-of-service (QoS) constraints. However, existing optimization and reinforcement learning (RL) approaches rely on aggregated, static base station (BS) traffic models that fail to capture non-stationary traffic dynamics and suffer from large state-action spaces, limiting real-world deployment. To address these challenges, this paper proposes a multi-agent deep reinforcement learning (MARL) framework using a Double Deep Q-Network (DDQN), referred to as MARL-DDQN, for adaptive SMO in a 3D urban environment with a time-varying and community-based user equipment (UE) mobility model. Unlike conventional single-agent RL, MARL-DDQN enables scalable, distributed decision-making with minimal signaling overhead. A realistic BS power consumption model and beamforming are integrated to accurately quantify EE, while QoS is defined in terms of throughput. The method adapts SMO policies to maximize EE while mitigating inter-cell interference and ensuring throughput fairness. Simulations show that MARL-DDQN outperforms state-of-the-art strategies, including All On, iterative QoS-aware load-based (IT-QoS-LB), MARL-DDPG, and MARL-PPO, achieving up to 0.60 Mbit/Joule EE, 8.5 Mbps 10th-percentile throughput, and meeting QoS constraints 95% of the time under dynamic scenarios.

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arXiv2025

Stochastic Geometry Analysis of Asymmetric Uplink Interference for Urban UAV-RC Networks

Uncrewed aerial vehicles (UAVs) have emerged as a flexible platform for providing coverage over challenging environments, particularly for public safety and surveillance missions in urban areas. However, deploying the UAVs in dense urban areas introduces unique challenges, most notably asymmetric uplink (UL, remote controller to UAV) interference due to a higher chance of line-of-sight (LoS) interference at the UAV. In this letter, we propose a stochastic geometry framework to tractably analyze the large-scale asymmetric interference in urban areas. We incorporate a log-Gaussian Cox process (LGCP) model to capture the spatial correlation of the interference field in both UL and downlink (DL) as a function of the UAV altitude and the two-dimensional (2-D) distance between the remote controller and UAV. To quantify the UL and the DL interference asymmetry, we also define the interference asymmetry ratio characterizing the interference disparity between the UL and the DL. Our numerical results demonstrate that the interference asymmetry ratio increases as the UAV altitude and 2-D distance increase, highlighting that the UL interference worsens.

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arXiv2025

Curated Wireless Datasets for Aerial Network Research

This Review consolidates publicly available aerial wireless measurement datasets collected using AERPAW. We organize signal-level, power-level, and KPI-level datasets under a unified taxonomy, harmonize metadata, and provide verified access with reproducible post-processing scripts. The curated catalog supports propagation modeling, machine learning, localization, and system-level evaluation for 5G-Advanced and emerging 6G aerial networks.

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arXiv2025

Interference-Asymmetric UAV Remote Control Links: Measurements and Performance Evaluation

Reliable and secure connectivity is crucial for remote control (RC) and uncrewed aerial vehicles (UAVs) links. A major problem for UAV RC links is that interference sources within the coverage may degrade the link quality. Such interference problems are a higher concern for the UAV than the RC unit on the ground due to the UAV being in line of sight (LoS) with a larger number of interference sources. As a result, lost hybrid automatic repeat request (HARQ) indicators (ACK/NACK) feedback in the uplink (UL, RC to UAV) may degrade the downlink (DL, UAV to RC) throughput. To get physical evidence for our interference asymmetry argument, we first conducted a measurement campaign using a helikite platform at the Main Campus area of NC State University during the 2024 Packapalooza festival. Subsequently, we evaluated the throughput impact of the loss of HARQ indicator feedback caused by UL asymmetry using MATLAB long-term-evolution (LTE) and fifth-generation (5G) toolboxes. Our numerical results confirm that UL interference asymmetry substantially degrades the throughput performance due to the loss of HARQ indicator feedback.

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