arXiv subjects

Md Sharif Hossen

Publications and source records attributed to Md Sharif Hossen.

11 recordsLinked to original sources

arXiv2026

Beyond RSRP Coverage: UAV Serviceability in Interference-Limited Urban 5G NR Networks

Reference signal received power (RSRP) is commonly used to assess 5G cellular coverage, but it may substantially overestimate the connectivity available to cellular-connected unmanned aerial vehicles (UAVs) under strong intercell interference (ICI). As a UAV ascends, reduced blockage can strengthen the serving signal, while increased line-of-sight visibility to neighboring sectors can intensify ICI. This paper develops a trajectory-aware, 3GPP-based system-level framework for evaluating UAV radio serviceability during a three-dimensional flight in an urban 5G New Radio network. The framework jointly evaluates RSRP, reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR) in a 19-site, 57-sector deployment. We introduce the concept of coverage-serviceability gap to quantify the difference between RSRP availability and joint service availability. Under evaluation thresholds of -100 dBm for RSRP, -20 dB for RSRQ, and 0 dB for SINR, an inter-site distance (ISD) of 500 m provides 99.9% RSRP availability but only 13.6% joint service availability, yielding a gap of 86.3 percentage points. Increasing the ISD to 1000 m improves mean SINR and joint availability from 13.6% to 18.4%, while SINR remains the dominant constraint. Results averaged over three independent simulation realizations show that RSRP-based aerial coverage does not necessarily translate into mission-level radio serviceability, motivating trajectory-aware, multi-key performance metric (KPM) aerial radio planning.

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arXiv2026

OpenTwin: Closed-Loop Digital Twins for Trustworthy Policy Deployment in Open RAN

In open radio access networks (O-RAN), the near-real-time RAN Intelligent Controller (RIC) hosts third-party xApps whose training and validation risk disrupting the operational network. Disaggregation amplifies this risk, as no single party can certify a control action end to end. Indeed, our testbed shows an E2 control request reported as successful while the base station never applies the change. Digital twins (DTs) promise safe policy evaluation, yet existing O-RAN DTs largely rely on hand-crafted models, run without feedback from the deployment, and never say how often their predictions can be trusted. To fill this gap, we present OpenTwin, a closed-loop framework that learns the simulator configuration reproducing an operating deployment streamed measurements, certifies the resulting DT by re-simulation, calibrates it online, and evaluates each xApp action before it executes on the physical network. Every trust decision carries an error rate bounded by an operator-prescribed budget, with a drift detector limiting needless resynchronizations and a conformal fidelity gate admitting an action only when its predicted outcome range lies in the safe region. Extensive experiments across simulation and real-world testbeds confirm single-digit percentage error in reproduced measurements, false approvals an order of magnitude below every budget from 0.05 to 0.30, and a gated energy-saving xApp that retains roughly 40% of the saving achievable with perfect foresight.

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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

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

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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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

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

A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN

The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN, which can severely undermine network performance, is jamming attacks. This paper presents SAJD- a self-adaptive jammer detection framework that autonomously detects jamming attacks in AI/ML framework-integrated ORAN environments without human intervention. The SAJD framework forms a closed-loop system that includes near-realtime inference of radio signal jamming via our developed ML-based xApp, as well as continuous monitoring and retraining pipelines through rApps. In this demonstration, we will show how SAJD outperforms state-of-the-art jamming detection xApp (offline trained with manual labels) in terms of accuracy and adaptability under various dynamic and previously unseen interference scenarios in the O-RAN-compliant testbed.

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arXiv2025

SAJD: Self-Adaptive Jamming Attack Detection in AI/ML Integrated 5G O-RAN Networks

The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking (SDN), network function virtualization (NFV), and implementation of standardized open interfaces. It also facilitates closed loop control and (non/near) real-time optimization of radio access network (RAN) through the integration of non-real-time applications (rApps) and near-real-time applications (xApps). However, one of the security concerns for O-RAN that can severely undermine network performance and subject it to a prominent threat to the security & reliability of O-RAN networks is jamming attacks. To address this, we introduce SAJD-a self-adaptive jammer detection framework that autonomously detects jamming attacks in artificial intelligence (AI) / machine learning (ML)-integrated O-RAN environments. The SAJD framework forms a closed-loop system that includes near-real-time inference of radio signal jamming interference via our developed ML-based xApp, as well as continuous monitoring and retraining pipelines through rApps. Specifically, a labeler rApp is developed that uses live telemetry (i.e., KPIs) to detect model drift, triggers unsupervised data labeling, executes model training/retraining using the integrated & open-source ClearML framework, and updates deployed models on the fly, without service disruption. Experiments on O-RAN-compliant testbed demonstrate that the SAJD framework outperforms state-of-the-art (offline-trained with manual labels) jamming detection approach in accuracy and adaptability under various dynamic and previously unseen interference scenarios.

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arXiv2025

Accelerating Development in UAV Network Digital Twins with a Flexible Simulation Framework

Unmanned aerial vehicles (UAVs) enhance coverage and provide flexible deployment in 5G and next-generation wireless networks. The performance of such wireless networks can be improved by developing new navigation and wireless adaptation approaches in digital twins (DTs). However, challenges such as complex propagation conditions and hardware complexities in real-world scenarios introduce a realism gap with the DTs. Moreover, while using real-time full-stack protocols in DTs enables subsequent deployment and testing in a real-world environment, development in DTs requires high computational complexity and involves a long development time. In this paper, to accelerate the development cycle, we develop a measurement-calibrated Matlab-based simulation framework to replicate performance in a full-stack UAV wireless network DT. In particular, we use the DT from the NSF AERPAW platform and compare its reports with those generated by our developed simulation framework in wireless networks with similar settings. In both environments, we observe comparable results in terms of RSRP measurement, hence motivating iterative use of the developed simulation environment with the DT.

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