arXiv subjects

George Sklivanitis

Publications and source records attributed to George Sklivanitis.

10 recordsLinked to original sources

arXiv2026

Designing, Deployment and Field Testing of C2Stack for Networked Intelligent Software-Defined UAVs

Unmanned Aerial Vehicles (UAVs) are emerging as critical enablers of next-generation wireless networking and autonomous systems. Despite their potential, deploying and testing networked UAV systems in real-world environments remains challenging, largely due to the absence of well-developed, end-to-end, ready-to-use protocol stacks. To fill this gap, we present C2Stack, a configurable protocol stack and experimental framework designed for real-time control, evaluation, and optimization of UAV networks. C2Stack incorporates a modular control plane, referred to as the~C2Stack Network Operating System (CNOS), alongside a programmable data plane that exposes APIs for cross-layer algorithm development, digital twin integration, and autonomous swarm control. In this article, we share our experience with the deployment and testing of C2Stack. We implemented C2Stack on a custom UAV swarm platform that integrates multiprocessor system-on-chip (MPSoC) radios with Intel NUC computing modules, enabling interoperability with various RF front ends. Field trials were conducted in both netted environments and large-scale outdoor test ranges, focusing on two representative use cases: (i) network utility maximization through online reinforcement learning, and (ii) collaborative interference source localization. The experiments demonstrate the feasibility of real-time, data-driven optimization in dynamic aerial environments, while also revealing practical challenges in field deployments of networked UAV systems, including power constraints, sensing limitations, and deployment logistics. We have made C2Stack source code available to the community under the MIT License, with the goal of establishing it as a foundational framework for experimental research on intelligent networked aerial systems.

eess.SY↗

arXiv2026

Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Existing approaches struggle with severe domain shift across collection platforms, fine-grained visual similarity between closely related species, and uneven annotation granularity, where many specimens can only be identified to genus or a coarser taxonomic rank. We present a taxonomy-aware deep learning framework that aligns both the training loss and the inference rule with the hierarchical structure of biological classification, combining a taxonomy-weighted loss, minimum-risk Bayesian inference, multi-scale feature encoding, and independent per-rank classification heads. Evaluated on the FathomNet 2025 dataset1 (79 marine classes across seven taxonomic ranks), the system achieves a mean taxonomic distance of 1.581, within 3% of the 1st-place solution (1.535), with the largest gains from metric-aligned inference and simple, decoupled components that generalize better than learned dependencies under distribution shift.

cs.CV↗

arXiv2026

StormWave: An Open-Source Portable SDR Platform for Over-the-Air Resilience Evaluation of Terrestrial and Aerial Communications

This paper presents \emph{StormWave}, an open-source, portable software-defined Radio Frequency (RF) interference generation and monitoring platform designed for realistic field-based evaluation of the resilience of wireless communication systems. StormWave enables seamless composition and runtime switching among a wide range of narrowband and wideband waveforms, while supporting multiple digital modulations, adaptive coding, and multi-radio orchestration with real-time spectrum visualization. We evaluate the effectiveness of StormWave through both outdoor ground and air-to-air (A2A) experiments. Ground experiments demonstrate clear waveform- and modulation-dependent interference effects under realistic propagation conditions, while A2A experiments reveal pronounced distance-dependent constellation distortion and access-symbol degradation under active interference. The StormWave source code will be released to the community, with the expectation that StormWave will be used as a flexible, extensible, and field-ready platform for systematically validating interference resilience of wireless systems under realistic operating conditions.

eess.SY↗

arXiv2026

Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation

We consider the problems of computing the optimal rank-1 Hankel and Toeplitz-structured approximation of arbitrary matrices under L2 and L1-norm error. Such problems arise naturally in engineered systems, including the basic few-shot signal Direction-of-Arrival (DoA) estimation problem that is of importance to modern autonomous systems applications. We develop accurate and computationally efficient structured matrix decomposition algorithms for both formulations and then derive analytically grounded small-sample-support DoA estimators for practical sensing system deployments. The resulting estimators under the L2 and L1 norms are formally shown to be maximum-likelihood optimal under white Gaussian and Laplace noise, respectively. The estimators are further validated through extensive simulation studies and real-world data experiments in few-shot DoA inference.

cs.LG↗

arXiv2026

Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition

Motivated by sensing modalities in modern autonomous systems that involve hardware-constrained spatial sampling over large arrays with limited coherence time, we develop a novel framework for rapid super-resolution multi-signal direction-of-arrival (DoA) estimation based on Hankel-structured sensing and data matrix decomposition of arbitrary rank, under both the $L_2$ and $L_1$-norm formulation. The resulting $L_2$-norm estimator is shown to be maximum-likelihood optimal in white Gaussian noise. The $L_1$-norm estimator is shown to be maximum-likelihood optimal in independent, identically distributed (i.i.d.) isotropic Laplace noise, offering broad robustness to impulsive interference and corrupted measurements commonly encountered in practice. Extensive simulations demonstrate that the proposed methods exhibit powerful super-resolution capabilities, requiring significantly lower SNR and achieving substantially higher resolution probability than recent competing approaches.

cs.LG↗

arXiv2026

Demonstration of a 1.2 Gbps Always-on Fully-Connected Mesh Network with RFSoC SDRs

We design and implement on Radio Frequency System-on-Chip (RFSoC) software-defined radios (SDRs) a complete-graph network of four unmanned aerial vehicles and demonstrate real-time 4K video streaming over twelve always-on 2x2 multiple-input multiple-output (MIMO) links. The testbed operates at an aggregate network throughput of approximately 1.2 Gbps (i.e., 12 links of 99.84 Mbps) across a shared bandwidth of 200 MHz. To the best of our knowledge, this is the first demonstration of low-latency digitally controlled frequency-division duplex (FDD) RFSoC-based MIMO wireless links capable of simultaneously supporting multiple real-time, uncompressed 4K video streams. The testbed consists of four AMD/Xilinx Zynq UltraScale+ RFSoC ZCU111 evaluation kits configured as a fully-connected mesh network with custom-built physical and medium-access-control layers, adaptive equalization, and adjacent-band filtering implemented entirely in RFSoC's programmable logic. A host-side graphical user interface (GUI) provides real-time visualization of each link's performance including error vector magnitude (EVM), pre-detection signal-to-interference-plus-noise ratio (SINR), and bit error rate (BER), and enables dynamic reconfiguration of link parameters during operation.

cs.NI↗

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.

cs.NI↗

arXiv2025

BenchLink: An SoC-Based Benchmark for Resilient Communication Links in GPS-Denied Environments

Accurate timing and synchronization, typically enabled by GPS, are essential for modern wireless communication systems. However, many emerging applications must operate in GPS-denied environments where signals are unreliable or disrupted, resulting in oscillator drift and carrier frequency impairments. To address these challenges, we present BenchLink, a System-on-Chip (SoC)-based benchmark for resilient communication links that functions without GPS and supports adaptive pilot density and modulation. Unlike traditional General Purpose Processor (GPP)-based software-defined radios (e.g. USRPs), the SoC-based design allows for more precise latency control. We implement and evaluate BenchLink on Zynq UltraScale+ MPSoCs, and demonstrate its effectiveness in both ground and aerial environments. A comprehensive dataset has also been collected under various conditions. We will make both the SoC-based link design and dataset available to the wireless community. BenchLink is expected to facilitate future research on data-driven link adaptation, resilient synchronization in GPS-denied scenarios, and emerging applications that require precise latency control, such as integrated radar sensing and communication.

eess.SP↗

arXiv2025

Spectrum Sharing by Space-Time Waveform Shaping

In this paper, we consider the task of introducing a new wireless data link over a given occupied frequency band using a multi-antenna transmitter and receiver. We design formally a dynamic multiple-input multiple-output (MIMO) wireless link that can coexist in the fixed congested frequency band by (a) optimally avoiding sensed interference in the joint space-time domain, and (b) protecting existing links by minimizing its own transmitted power in the band. In particular, the transmit beam weight vector and time domain pulse code sequence are jointly optimized to minimize the transmit energy per bit per antenna, while maintaining a pre-defined signal-to-interference-plus-noise ratio (SINR) at the output of the joint space-time maximum SINR receiver filter. Extensive numerical studies are carried out to demonstrate the derived algorithmic solution in light and heavily congested band scenarios with non-cooperative co-channel links. We show that the proposed autonomously reconfigurable 4x4 MIMO link outperforms a non-adaptive transceiver and other forms of waveform shaping in terms of the pre-detection SINR performance and the capability to protect ongoing non-cooperative links by not occupying the band with redundant transmissions.

cs.NI↗

arXiv2025

MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence

WiFi-based mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movements. However, MAC address randomization introduces a significant obstacle in accurately estimating congestion levels and path trajectories. To this end, we consider radio frequency fingerprinting and re-identification for attributing WiFi traffic to emitting devices without the use of MAC addresses. We present MobRFFI, an AI-based device fingerprinting and re-identification framework for WiFi networks that leverages an encoder deep learning model to extract unique features based on WiFi chipset hardware impairments. It is entirely independent of frame type. When evaluated on the WiFi fingerprinting dataset WiSig, our approach achieves 94% and 100% device accuracy in multi-day and single-day re-identification scenarios, respectively. We also collect a novel dataset, MobRFFI, for granular multi-receiver WiFi device fingerprinting evaluation. Using the dataset, we demonstrate that the combination of fingerprints from multiple receivers boosts re-identification performance from 81% to 100% on a single-day scenario and from 41% to 100% on a multi-day scenario.

eess.SP↗