Systems and Control
Online Long-Term Voltage Stability Margin Estimation for IBR/DER Dominated Power System with Integrated VSM-Aware TSO-DSO Framework
Ahmed Alkhonain, Kiran Kumar Challa, Amarsagar Reddy Ramapuram Matavalam et al. (+2 more)
The rapid growth of inverter-based resources (IBRs) and distributed energy resources (DERs) has fundamentally altered the long-term voltage stability characteristics of modern power systems. This article leverages the advantages of machine learning (ML) for the online estimation of long-term voltage stability margin (VSM) and enhancement of VSM through coordinated transmission system operator-distribution system operator (TSO-DSO) optimization. An explicit analytical VSM expression is derived from offline T&D co-simulation data using a physics-informed ML-trained model under probabilistic loading and generation mix scenarios, while accounting for unbalanced distribution modeling. The resulting closed-form VSM representation is linearized and embedded into the TSO optimization problem, enabling real-time enforcement of minimum VSM constraints. We further enhance operational efficiency by incorporating VSM sensitivities into both transmission and distribution optimization, allowing prioritization of the most influential reactive power resources. Simulation studies conducted on the IEEE 30-bus transmission network integrated with multiple IEEE 37-node distribution feeders validate that the proposed framework successfully achieves the desired VSM enhancement while maintaining high estimation accuracy.
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All authors Ahmed Alkhonain, Kiran Kumar Challa, Amarsagar Reddy Ramapuram Matavalam, Alok Kumar Bharati, Venkataramana Ajjarapu
Systems and Control
A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems
Mohsen Jalaeian Farimani, Roya Khalili Amirabadi, Davoud Nikkhouy et al. (+4 more)
The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision-making and adaptive control. MPC offers structured optimization, explicit constraint handling, and established stability tools, whereas RL provides data-driven adaptation and performance improvement in the presence of uncertainty and model mismatch. Despite the rapid growth of research on RL–MPC integration, the literature remains fragmented, particularly for control architectures built on linear or linearized predictive models. This paper presents a comprehensive Systematic Literature Review (SLR) of RL–MPC integrations for linear and linearized systems, covering peer-reviewed and formally indexed studies published until 2025. The reviewed studies are organized through a multi-dimensional taxonomy covering RL functional roles, RL
algorithm classes, MPC formulations, cost-function structures, and application domains. In addition, a cross-dimensional synthesis is conducted to identify recurring design patterns and reported associations among these dimensions within the reviewed corpus. The review highlights methodological trends, commonly adopted integration strategies, and recurring practical challenges, including computational burden, sample efficiency, robustness, and closed-loop guarantees. The resulting synthesis provides a structured reference for researchers and practitioners seeking to design or analyze RL–MPC architectures based on linear or linearized predictive control formulations.
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All authors Mohsen Jalaeian Farimani, Roya Khalili Amirabadi, Davoud Nikkhouy, Malihe Abdolbaghi, Mahshad Rastegarmoghaddam, Shima Samadzadeh, Mahdi Ghane
Signal Processing
The Radon Transform, True Time Delay Beamforming, and Ultra-Wideband Antenna Arrays (Invited Paper)
Tyler Ikehara, Ibrahim Pehlivan, Danijela Cabric et al. (+1 more)
The FR3 band has emerged as the major focus of 6G wireless research. FR3 cellular operation presents the challenge of extreme bandwidth combined with physically large
antenna arrays. In this regime, conventional phase-shift beamforming entails a loss of coherence (beam-squint), and has to be replaced by true time delay beamforming (TTD). It happens that TTD is mathematically equivalent to taking the Radon transform of the space/time measurements. We exploit fifty years of research in the application of the Radon transform to computer
tomography and to seismic exploration to elucidate the workings of TTD. We use the Radon transform combined with semblance detection and Radon slowness filtering to remove far-field signals from the measured space/time signals from a linear array, leaving only near-field signals. In turn we partition the array into sub-arrays. For each sub-array we estimate, via the semblance Radon transform, the angles-of-arrival of the near-field signals. We then use triangulation to estimate the coordinates of the near-field sources. Finally we integrate the original space/time data along
hyperbolic trajectories to extract the individual near-field signal envelopes.
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All authors Tyler Ikehara, Ibrahim Pehlivan, Danijela Cabric, Thomas L. Marzetta
Systems and Control · Cross-listed from cs.RO
A Survey of Legged Robotics in Non-Inertial Environments: Past, Present, and Future
I-Chia Chang, Xinyan Huang, Tzu-Yuan Lin et al. (+5 more)
Legged
robots have demonstrated remarkable agility on rigid, stationary ground, but their locomotion reliability remains limited in non-inertial environments, where the supporting ground moves, tilts, or accelerates. Such conditions arise in ground transportation, maritime platforms, and aerospace settings, and they introduce persistent time-varying disturbances that break the stationary-ground assumptions underlying conventional legged locomotion. This survey reviews the state of the art in modeling, state estimation, and control for legged
robots in non-inertial environments. We summarize representative application domains and motion characteristics, analyze the root causes of locomotion performance degradation, and review existing methods together with their key assumptions and limitations. We further identify open problems in
robot-environment coupling, observability, robustness, and experimental validation, and discuss future directions in autonomy, system-level design, bio-inspired strategies, safety, and testing. The survey aims to clarify the technical foundations of this emerging area and support the development of reliable legged
robots for real-world dynamic environments.
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All authors I-Chia Chang, Xinyan Huang, Tzu-Yuan Lin, Sangli Teng, Wenjing Li, Maani Ghaffari, Jingang Yi, Yan Gu
Systems and Control · Cross-listed from math.DS
The Riccati Characteristic Equation
Douglas R. Frey
The Riccati differential equation is examined in light of its connection to second order linear time varying systems. In that light it becomes the clear generalization for the characteristic equation of linear time invariant systems, and is called the Riccati Characteristic Equation (RCE). Consequently, the RCE becomes the unifying centerpiece for the study of linear systems. Its solutions are considered in complementary pairs that form a continuum based on a primitive pair. Pairs may always be found as purely real solutions, despite the fact that complex conjugate primitive solutions are shown to exist in many cases. Not only is the pairing unique, but the general form of solutions, shown here for the first time, is uniquely compact and encompasses all known solutions, while allowing for all initial conditions. Classical engineering mathematics examples are shown to conform to this approach, which provides new insights to all, especially Floquet theory.
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Systems and Control
A Complete Approach to Time Varying Linear Systems
Douglas R. Frey
This paper presents a unifying theory of Linear second order systems that allows time-varying and time invariant systems to be treated in the same way for the first time. In the process, a transformation is given that diagonalizes an arbitrary time varying state
matrix in a spectrum invariant way. A canonical form for the fundamental
matrix is given that depends on dynamic
eigenvalues and related eigenvectors dependent upon the Riccati Characteristic Equation for the system, which intuitively generalizes the standard characteristic equation for time invariant systems. The technique is shown by examples to give a unified approach to the solutions of time invariant, time-varying, and periodic systems.
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Systems and Control · Cross-listed from cs.RO
Clinical Evaluation of a Tongue-Controlled Wrist Abduction-Adduction Assistance in a 6-DoF Upper-Limb Exoskeleton for Individuals with ALS and SCI
Juwairiya S. Khan, Mostafa Mohammadi, Alexander L. Ammitzbøll et al. (+7 more)
Upper-limb exoskeletons (ULEs) have the potential to restore functional independence in individuals with severe motor impairments; however, the clinical relevance of wrist degrees of freedom (DoF), particularly abduction-adduction (Ab-Ad), remains insufficiently evaluated. This study investigates the functional and user-perceived impact of wrist Ab-Ad assistance during two activities of daily living (ADLs). Wrist Ab-Ad assistance in a tongue-controlled 6-DoF ULE, EXOTIC2, was evaluated in a within-subject study involving one individual with amyotrophic lateral sclerosis and five individuals with spinal cord injury. Participants performed drinking and scratch stick leveling tasks with EXOTIC2 under two conditions: with and without wrist Ab-Ad assistance. Outcome measure included task success, task completion time, kinematic measures, and a usability questionnaire capturing comfort, functional perception, and acceptance. Enabling wrist Ab-Ad improved task success rates across both ADLs, with consistent reductions in spillage (from 77.8% spillages to 22.2%) and failed placements (from 66.7% to 16.7%). Participants utilized task-specific subsets of the available wrist range of motion, indicating that effective control within functional ranges was more critical than maximal joint excursion. Questionnaire responses indicated no increase in discomfort with the additional DoF and reflected perceived improvements in task performance. In conclusion, wrist Ab-Ad assistance enhances functional task performance in assistive exoskeleton use without compromising user comfort. However, its effectiveness depends on task context, control usability, and individual user strategies. This study provides clinically relevant, user-centered evidence supporting the inclusion of wrist Ab-Ad in ULEs, emphasizing the importance of balancing functional capability with usability in assistive device design.
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All authors Juwairiya S. Khan, Mostafa Mohammadi, Alexander L. Ammitzbøll, Ellen-Merete Hagen, Jakob Blicher Izabella Obál, Ana S. S. Cardoso, Oguzhan Kirtas, Rasmus L. Kæseler, John Rasmussen, Lotte N.S. Andreasen Struijk
Image and Video Processing
EDU-Net: Retinal Pathological Fluid Segmentation in OCT Images with Multiscale Feature Fusion and Boundary Optimization
Zijun Lei, Zikang Xu, Liang Zhang et al. (+5 more)
Objective: Diabetic macular edema (DME) is the leading cause of severe visual impairment in patients with diabetes. Quantification of retinal fluid, particularly intraretinal fluid (IRF) and subretinal fluid (SRF), plays a critical role in the management of DME. Although optical coherence
tomography (OCT) can be used for detection, the variable morphology of fluid accumulation and the blurred boundaries caused by noise interference still limit the accuracy of OCT's automatic
segmentation. Methods: Retrospective model development and validation study. This study proposes a novel edge-guided dual-branch encoder-decoder network (EDU-Net) to achieve accurate and efficient automatic
segmentation of OCT
liquid lesions. The local feature extraction branch is based on the EfficientNet model, which precisely captures tiny lesions by leveraging its lightweight separable convolution and high-resolution feature preservation strategy. The global feature extraction branch is based on the large-kernel efficient convolution (LKEC) module and the downsampling layer design to enhance long-range dependencies and global semantics. EDU-Net applies a multi-category edge-guided
attention module to fuse high-frequency boundary detail information to each resolution feature to optimize the boundary
segmentation performance. Results: Extensive results on the in-house and public
datasets demonstrate that EDU-Net achieves state-of-the-art DSC
segmentation performance in terms of efficiency and robustness, especially in the
segmentation of IRF lesions. Conclusions: EDU-Net integrates local details with global context and optimizes boundaries, achieving an improvement in the accuracy of automatic
segmentation of retinal fluid.
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All authors Zijun Lei, Zikang Xu, Liang Zhang, Ge Song, Hanyu Guo, Dan Cao, Yujia Zhou, Qianjin Feng
Systems and Control · Cross-listed from astro-ph.IM
Planetary Exploration 3.0: A Roadmap for Software-Defined, Radically Adaptive Space Systems
Masahiro Ono, Daniel Selva, Morgan L. Cable et al. (+23 more)
The surface and subsurface of worlds beyond Mars remain largely unexplored. Yet these worlds hold keys to fundamental questions in planetary science - from potentially habitable subsurface oceans on icy moons to ancient records preserved in Kuiper Belt objects. NASA's success in Mars exploration was achieved through incrementalism: 22 progressively sophisticated missions over decades. This paradigm, which we call Planetary Exploration 2.0 (PE 2.0), is untenable for the outer
Solar System, where cruise times of a decade or more make iterative missions infeasible. We propose Planetary Exploration 3.0 (PE 3.0): a paradigm in which unvisited worlds are explored by a single or a few missions with radically adaptive space systems. A PE 3.0 mission conducts both initial exploratory science and follow-on hypothesis-driven science based on its own in situ data returns, evolving spacecraft capabilities to work resiliently in previously unseen environments. The key enabler of PE 3.0 is software-defined space systems (SDSSs) - systems that can adapt their functions at all levels through software updates. This paper presents findings from a Keck Institute for Space Studies (KISS) workshop on PE 3.0, covering: (1) PE 3.0 systems engineering including science definition, architecture, design methods, and verification & validation; (2) software-defined space system technologies including reconfigurable hardware, multi-functionality, and modularity; (3) onboard intelligence including autonomous science,
navigation, controls, and
embodied AI; and (4) three PE 3.0 mission concepts: a Neptune/Triton smart flyby, an ocean world explorer, and an Oort cloud reconnaissance mission.
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All authors Masahiro Ono, Daniel Selva, Morgan L. Cable, Marie Ethvignot, Margaret Hansen, Andreas M. Hein, Elena-Sorina Lupu, Zachary Manchester, David Murrow, Chad Pozarycki, Pascal Spino, Amanda Stockton, Mathieu Choukroun, Soon-Jo Chung, John Day, Alexander Demagall, Anthony Freeman, Chloe Gentgen, Michel D. Ingham, Charity M. Phillips-Lander, Richard Rieber, Alejandro Salado, Maria Sakovsky, Lori R. Shiraishi, Yisong Yue, Kris Zacny
Signal Processing · Cross-listed from cs.LG
FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels
Sina Gholami, Abdulmoneam Ali, Tania Haghighi et al. (+2 more)
Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi-stage framework for robust FL under noisy labels. Different from existing approaches that mainly rely on designing noise-tolerant loss functions or exploiting loss dynamics during training, our method leverages the spectral structure of client feature representations to identify and mitigate label noise. Our framework consists of three key components. First, we identify clean and noisy clients by analyzing the spectral consistency of class-wise feature subspaces with minimal communication overhead. Second, clean clients provide spectral references that enable noisy clients to relabel potentially corrupted samples using both dominant class directions and residual subspaces. Third, we employ a noise-aware training strategy that integrates logit-adjusted loss, knowledge
distillation, and distance-aware aggregation to further stabilize
federated optimization. Extensive experiments on standard FL
benchmarks demonstrate that FedSIR consistently outperforms state-of-the-art methods for FL with noisy labels. The code is available at https://github.com/sinagh72/FedSIR.
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All authors Sina Gholami, Abdulmoneam Ali, Tania Haghighi, Ahmed Arafa, Minhaj Nur Alam
Systems and Control
Accurate Frequency Response Modeling in Integrated T&D Co-Simulation via EWMA-RTTA-Based Quadratic Extrapolation
Jong Ha Woo, Qi Xiao, Yu Ma et al. (+3 more)
The large-scale integration of inverter-based resources (IBRs), particularly distributed photovoltaics (DPVs), into distribution networks increases the need for integrated transmission and distribution (T&D) co-simulation. A key challenge in such co-simulation lies in accurately modeling system frequency across two asynchronous simulation environments. For example, the transmission system, simulated in the phasor domain, can operate with a simulation timestep of 10 ms, while the distribution system, simulated in the electromagnetic transient domain (EMT) to include IBR models, uses a much finer timestep of 100 microseconds. To ensure accurate PLL-based frequency estimation in distribution systems, it is essential to predict voltage magnitude and phase angle variations within the 10 ms transmission intervals, rather than using constant values that cause inaccurate frequency calculations. This issue becomes particularly critical when modeling primary and secondary frequency response services provided by IBRs. To address this challenge, we propose an automated Exponentially Weighted Moving Average Real-Time Threshold Adaptation (EWMA-RTTA) method, which utilizes Quadratic Extrapolation to predict voltage magnitude and phase angle trends more precisely. The proposed method is validated using two Opal-RT simulators: one simulating an IEEE 118-bus transmission system and the other simulating an IEEE 123-bus distribution network. Simulation results demonstrate that our approach improves the normalized mean absolute error (nMAE) by a factor of 25.7 compared to methods that do not account for time mismatches, offering a scalable and accurate solution for modeling IBR-based frequency response in modern power systems.
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All authors Jong Ha Woo, Qi Xiao, Yu Ma, Zishuo Yang, Victor Daldegan Paduani, Ning Lu
Systems and Control
Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data
Sreechakra Vasudeva Raju Rachavelpula and Sangwhan Cha
This paper presents a personalized Battery Electric Vehicle (BEV) energy consumption estimation framework that integrates map-based contextual features with driver-specific velocity prediction and physics-based energy consumption modeling. The system combines route selection, detailed road feature processing, a rule-based reference velocity generator, a PID controller-based vehicle dynamics simulator, and a Bidirectional LSTM model trained to reproduce individual driving behavior. The predicted individual-specific velocity profiles are coupled with a quasi-steady backward energy consumption model to compute tractive power, regenerative braking, and State-of-Charge (SOC)
evolution. Evaluation across urban, freeway, and hilly routes demonstrates that the proposed approach captures key driver behavioral patterns such as deceleration at intersections, speed-limit tracking, and road grade-dependent responses, while producing accurate power and SOC trajectories. The results highlight the effectiveness of combining learned driver behavior with map-based context and physics-based energy consumption modeling to produce accurate, personalized BEV SOC depletion profiles.
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Systems and Control · Cross-listed from physics.flu-dyn
RG-Based Local Hopf Reduction and Slow-Manifold Reconstruction for Nonlinear Aeroelastic Systems
Gelin Chen, Chen Song and Chao Yang
Self-excited limit-cycle oscillations (LCOs) from
Hopf bifurcations are a key feature of nonlinear aeroelasticity and depend sensitively on structural and aerodynamic parameters. Classical center-
manifold and normal-form theory describe this local behavior, but can be cumbersome to apply in large discretized models and standard reduced-order modeling (ROM) workflows. A renormalization-group (RG)-based reduction is developed that directly yields a
Hopf-type amplitude equation on a local invariant
manifold, specialized for
polynomial nonlinearities in tensor-based discretizations and compatible with finite-element-type settings. The method provides explicit coefficients governing the
Hopf threshold, criticality, and leading LCO amplitude/frequency trends, and admits a companion slow-
manifold approximation with selected stable modes retained as static coordinates. Representative nonlinear-aeroelastic examples illustrate how the proposed framework supplies compact, parameter-aware
Hopf/LCO descriptors suitable for local ROM construction near flutter.
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Systems and Control · Cross-listed from cs.GT
Decoupling Speculation from Merit: The Identity-Bound Asset Integrity Model (IBAIM) for Sustainable Web3 Gaming
Jinliang Xu
The rapid collapse of decentralized game economies, often characterized by the death spiral, remains the most formidable barrier to the mass adoption of Web3 gaming. This paper proposes that the sustainability of an open game economy is predicated on three necessary and sufficient conditions: Anti-Sybil Resilience, Anti-Capital Dominance, and Anti-Inflationary Saturation. The first section establishes a theoretical proof of these conditions, arguing that the absence of any single dimension leads to systemic failure. The second section explores the dialectical relationship between these dimensions, illustrating how unchecked automation and capital-driven monopolies accelerate asset hyperinflation. In the third section, we introduce the Identity-Bound Asset Integrity Model (IBAIM) as a comprehensive technical solution. IBAIM utilizes Zero-Knowledge (ZK) biometric hashing and Account Abstraction (AA) to anchor asset utility to unique human identities through a
privacy-preserving and regulatory-compliant architecture. By exogenizing biometric verification to trusted local environments and utilizing Zero-Knowledge Proofs of Identity (zk-PoI), the model ensures absolute user
privacy. Furthermore, by implementing an Asymmetric Utility Decay (AUD) engine-whereby assets suffer a vertical 50% utility cliff upon secondary transfer-and an
entropy-driven thermodynamic degradation mechanism., the model successfully decouples financial speculation from in-game merit. Finally, we apply this framework to analyze prominent historical failures in the GameFi sector, demonstrating that their collapse was an inevitable consequence of violating these core economic constraints. Our findings suggest that trading a degree of asset liquidity for system integrity is the only viable path toward long-term economic viability in decentralized virtual worlds.
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Systems and Control · Cross-listed from cs.LG
Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
Peter Collett, Alexander Johannes Stasik, Simone Casolo et al. (+1 more)
Accurate condition monitoring of industrial equipment requires inferring latent degradation parameters from indirect sensor measurements under uncertainty. While traditional
Bayesian methods like Markov Chain Monte Carlo (MCMC) provide rigorous uncertainty quantification, their heavy computational bottlenecks render them impractical for real-time process control. To overcome this limitation, we propose an AI-driven framework utilizing Simulation-Based Inference (SBI) powered by amortized neural posterior estimation to diagnose complex failure modes in heat exchangers. By training neural density
estimators on a simulated
dataset, our approach learns a direct, likelihood-free mapping from thermal-fluid observations to the full posterior distribution of degradation parameters. We
benchmark this framework against an MCMC baseline across various synthetic fouling and leakage scenarios, including challenging low-probability, sparse-event failures. The results show that SBI achieves comparable diagnostic accuracy and reliable uncertainty quantification, while accelerating inference time by a factor of82$\times$ compared to traditional sampling. The amortized nature of the neural network enables near-instantaneous inference, establishing SBI as a highly scalable, real-time alternative for probabilistic fault diagnosis and digital twin realization in complex engineering systems.
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All authors Peter Collett, Alexander Johannes Stasik, Simone Casolo, Signe Riemer-Sørensen
Systems and Control · Cross-listed from cs.AI
Interval POMDP Shielding for Imperfect-Perception Agents
William Scarbro and Ravi Mangal
Autonomous systems that rely on learned perception can make unsafe decisions when sensor readings are misclassified. We study shielding for this setting: given a proposed action, a shield blocks actions that could violate safety. We consider the common case where system dynamics are known but perception uncertainty must be estimated from finite labeled data. From these data we build confidence intervals for the probabilities of perception outcomes and use them to model the system as a finite Interval Partially Observable Markov Decision Process with discrete states and actions. We then propose an
algorithm to compute a conservative set of beliefs over the underlying state that is consistent with the observations seen so far. This enables us to construct a runtime shield that comes with a finite-horizon guarantee: with high probability over the training data, if the true perception uncertainty rates lie within the learned intervals, then every action admitted by the shield satisfies a stated lower bound on safety. Experiments on four case studies show that our shielding approach (and variants derived from it) improves the safety of the system over state-of-the-art baselines.
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Audio and Speech Processing · Cross-listed from cs.SD
ONOTE: Hypergraph-Grounded Omnimodal Reasoning for Computational Music Science
Menghe Ma, Siqing Wei, Yuecheng Xing et al. (+7 more)
Omnimodal notation processing, centered on sheet music, is a controlled scientific setting in which auditory, visual, symbolic, and physical representations must encode the same musical events. Yet existing work remains fragmented across recognition and transcription, rarely testing structural consistency across notation systems. Western-staff bias and underspecified model judges further conceal errors in pitch, timing, ordering, and instrument-specific constraints. We introduce ONOTE, a unified framework that treats music as a scientifically structured domain of measurable cross-representation correspondences. Its test-only
benchmark draws on a diverse collection of musical sources covering staff, Jianpu, and tablature across varied genres, instruments, and structural conditions, with aligned
multimodal derivatives. Four complementary tasks cover score understanding, notation conversion, audio transcription, and symbolic generation, testing pitch and duration ordering, output syntax, and disclosed instrument-specific constraints. ONOTE also constructs a provenance-bearing proposition hypergraph from external music-theory materials for entity- and hyperedge-based evidence
retrieval. Deterministic validity checks, disclosed structural-compliance SMG scoring, and controlled RAG comparisons reveal gaps between visual recognition and structure-preserving outputs. Results separate perception from music-theory application and structural or physical constraint satisfaction. ONOTE provides an auditable framework for studying representation invariance and knowledge-grounded intervention in computational music science.
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All authors Menghe Ma, Siqing Wei, Yuecheng Xing, Ziyue Zhu, Zhenghong Lin, Yaheng Wang, Fanhong Meng, Peijun Han, Luu Anh Tuan, Haoran Luo
Systems and Control
Low-Cost Turntable Designed for RF Phased Array Antenna Active Element Pattern Measurement
Rebekah Edwards, Taylor Martini, Jonathan E. Swindell et al. (+5 more)
Accurate
antenna array calibrations and measurements of aspects such as active element pattern (AEP) are critical for enabling integrated sensing and communication (
ISAC) technologies such as directional modulation. One reliable way of obtaining accurate and repeatable AEP measurements is to
spin the
antenna array on a turntable, but many turntables designed for
antenna array measurements are prohibitively expensive for small labs and may not be designed with RF considerations, such as cable phase stability, in mind. This paper details the design of a motorized 3D printed turntable for use in directional modulation and in-situ measurement experiments that will allow for rotation of an
antenna array around a point, such that the far field of the
antenna pattern can be measured by a stationary receiver.
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All authors Rebekah Edwards, Taylor Martini, Jonathan E. Swindell, David W. Cox, Adam C. Goad, Austin Egbert, Charles Baylis, Robert J. Marks
Systems and Control · Cross-listed from cs.LG
Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems
Pascal Archambault, Houari Sahraoui and Eugene Syriani
Digital twins of natural systems must remain aligned with physical systems that evolve over time, are only partially observed, and are typically modeled by mechanistic simulators whose parameters cannot be measured directly. In such settings, model adaptation is naturally posed as a simulation-based inference problem. However, sparse and indirect observations often fail to identify a unique and optimal calibration, leaving several simulator parameterizations compatible with the available evidence. This article presents a GFlowNet-based approach to model adaptation for digital twins of natural systems. We formulate adaptation as a generative modeling problem over complete simulator configurations, so that plausible parameterizations can be sampled with probability proportional to a reward derived from agreement between simulated and observed behavior. Using a controlled environment agriculture case study based on a mechanistic tomato model, we show that the learned policy recovers dominant regions of the adaptation landscape, retrieves strong calibration hypotheses, and preserves multiple plausible configurations under uncertainty.
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Signal Processing · Cross-listed from cs.IT
Wideband Direct Satellite Uplink Enabled by Pilot-less Sparse Superposition Codes
Alberto G. Perotti, Branislav M. Popovic and Renaud-Alexandre Pitaval
Direct satellite uplink is severely constrained by limited link budgets, which hinder the exploitation of wideband resources, and ultimately limit the throughout. This paper presents a pilot-less coded modulation scheme based on sparse superposition coding (SSC) to enable efficient wideband usage in coverage-limited scenarios. This scheme leverages the structured Zadoff-Chu quasi-orthogonal (ZC-QO) dictionary to support scalable transmission. To address decoding complexity, the SSC transmitted signal embeds root index information via indicator sequences, allowing the receiver to restrict the decoding search space. In addition, a multi-codeword transmission framework with repetition and stop-feedback is developed, enabling reliable communication and better resource utilization. Simulation results show that the proposed scheme achieves throughput gains compared to a more conventional narrow-band multi-dimensional constellation-based approach.
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Image and Video Processing
CKM Beyond Channel Gain: Spatial Correlation Map Construction with Deep Learning
Z. Chen, S. Fu, Y. Zeng et al. (+2 more)
Channel knowledge map (CKM) is a promising technique to achieve environment-aware wireless communication and sensing. Constructing the complete CKM based on channel knowledge observations at sparse locations is a fundamental problem for CKM-enabled wireless networks. However, most existing works on CKM construction only consider the special type of CKM, i.e., the channel gain map (CGM), which only records the channel gain value for each location. In this paper, we consider the channel spatial correlation map (SCM) construction, which signifies the location-specific spatial correlation
matrix for multi-
antenna systems. Unlike CGM construction, constructing SCM poses significant challenges due to its extremely high-dimensional structure. To address this issue, we first decompose the high-dimensional SCM into lower-dimensional path gain map (PGM) and path angle map (PAM). Then we propose a deep learning model termed E-SRResNet for constructing high-quality SCM from sparse samples, which incorporates multi-head
attention (MHA) mechanisms and multi-scale feature fusion (MSFF) to accurately model both local and global spatial relationships of channel parameters and complex nonlinear mappings. Furthermore, we preprocess the
dataset to provide priors including line-of-sight (LoS) map, binary building map and base station (BS) map for the model to reconstruct SCM more accurately. Simulations conducted on the CKMImageNet
dataset demonstrate that the proposed E-SRResNet achieves significant performance improvements over baseline methods. Moreover, the cosine similarity between the constructed SCM and the ground truth exceeds 0.8 in most regions, validating the effectiveness of the proposed construction method.
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All authors Z. Chen, S. Fu, Y. Zeng, X. Xu, Z. Wei
Signal Processing
Tri-Hybrid Beamforming Design for ISAC Systems with Reconfigurable Antennas
Jiangong Chen, Xia Lei, Yuchen Zhang et al. (+2 more)
Integrated Sensing and Communication (
ISAC) systems require efficient beamforming architectures to jointly support communication and sensing functionalities. To reduce hardware overhead, Hybrid Beamforming (HBF) has been widely studied and shown to achieve performance close to fully digital beamforming under practical hardware constraints. As a promising
evolution, Reconfigurable
Antenna (RA) technologies have recently emerged to further enhance beamforming Degrees of Freedom (DoFs) by dynamically reconfiguring
antenna Electromagnetic(EM) characteristics, yet their integration into
ISAC systems remains largely unexplored. In this paper, we investigate an RA-assisted
ISAC system and develop a decoupled Triple-Hybrid Beamforming (Tri-HBF) framework that alternatively optimizes digital, analog, and EM beamformers to maximize the communication rate and sensing Signal-to-Clutter-plus-NoiseRatio (SCNR). For both Single-user Single-target (SUST) and Multiple-user Multiple-target (MUMT) scenarios, we first transform the original fractional objectives into fraction-free ones via methods tailored to their respective structures. The resulting problems are then solved via alternating optimization over different variable blocks. Closed-form updates are derived for all variables except the EM beamforming subproblem in the MUMT scenario. To further reduce the complexity introduced by Semidefinite Relaxation (SDR) in EM beamforming, we propose a low-complexity iterative approach across
antennas with closed-form updates. Simulation results demonstrate that the proposed scheme significantly outperforms
benchmark designs with conventional omnidirectional and directional
antennas, achievingalmost 100% improvement in spectrum efficiency and 62.5% reduction in
antenna overhead, thereby unveiling the
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All authors Jiangong Chen, Xia Lei, Yuchen Zhang, Kaitao Meng, Christos Masouros
Signal Processing
Sample entropy for graph signals: An approach to nonlinear analysis of graph signals
Mei-San Maggie Lei, John Stewart Fabila Carrasco and Javier Escudero
We introduce a graph-signal generalisation of Sample
Entropy, denoted SampEn$_{G}$, to quantify irregularity of graph signals on a continuous state space, complementing existing methods on symbolic dynamics. Our approach replaces the temporal delay
embedding of classical SampEn with a multi-hop graph-based
embedding: for each node, we aggregate patterns from local walk-weighted neighbourhood averages computed via powers of the graph shift operator. We show empirically that SampEn$_{G}$ reduces to classical 1D SampEn on directed path graphs, and validate its nonlinear sensitivity using the logistic map. Experiments on directed Erdős–Rényi graph signals further characterise its behaviour with connectivity and pattern length $m$, with practical runtimes on the order of thousands of nodes. We expect SampEn$_{G}$ to open up new ways to analyse graph signals, generalising SampEn and the concept of conditional
entropy to extending nonlinear analysis to a wide variety of network data.
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Systems and Control
Model Predictive Communication for Timely Status Updates in Low-Altitude Networks
Bowen Li, Jiping Luo, Themistoklis Charalambous et al. (+1 more)
Timely information delivery in low-altitude networks is critical for many time-sensitive applications, such as unmanned aerial vehicle (
UAV)
navigation, inspection, and surveillance. The key challenge lies in balancing three competing factors: stringent data freshness requirements,
UAV onboard energy consumption, and interference with terrestrial services. Addressing this challenge requires not only efficient power and channel allocation strategies but also effective communication timing over the entire operation horizon. In this work, we propose a model predictive communication (MPComm) framework, enabled by advanced channel sensing techniques, in which the channel conditions that the
UAV will experience are largely predictable. Within this framework, we formulate a constrained bi-objective optimization problem to achieve a desired trade-off between energy consumption and terrestrial channel occupation, subject to a strict timeliness constraint. We solve this problem using Pareto analysis and show that the original non-convex, mixed-integer problem can be decomposed into a two-layer structure: the outer layer determines the optimal communication timing, while the inner layer determines the optimal power and channel allocation for each communication interval. An efficient
algorithm for the inner problem is developed using non-convex analysis, with asymptotic optimality guarantees, while the outer problem is solved optimally via a simple graph search, with edges characterized by inner solutions. The proposed approach applies to a broad class of problem variants, including objective transformations and single-objective specializations. Numerical results demonstrate the efficiency of the proposed solution, achieving up to a six-fold reduction in terrestrial channel occupation and a 6dB energy saving compared to
benchmark schemes.
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All authors Bowen Li, Jiping Luo, Themistoklis Charalambous, Nikolaos Pappas
Systems and Control · Cross-listed from cs.LG
A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs
Patrick Wilk, Ethan Cantor, Yikui Liu et al. (+1 more)
The ongoing shift towards decentralization of the electric energy sector, driven by the growing electrification across end-use sectors, and widespread adoption of distributed energy resources (DERs), necessitates their active participation in the electricity
markets to support grid operations. Furthermore, with bi-directional energy and communication flows becoming standard, intelligent, easy-to-deploy, resource-conservative demand-side participation is expected to play a critical role in securing power grid operational flexibility and
market efficiency. This work proposes a
market engagement framework that leverages a hierarchical multi-
agent deep reinforcement learning (MARL) approach to enable individual prosumers to participate in peer-to-peer retail auctions and further aggregate these intelligent prosumers to facilitate effective DER participation in wholesale
markets. Ultimately, a Stackelberg game is proposed to coordinate this hierarchical MARL-based DER
market participation framework toward enhanced
market performance.
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All authors Patrick Wilk, Ethan Cantor, Yikui Liu, Jie Li
Signal Processing
Enhancing the physical layer security with bending beams
Sotiris Droulias, Giorgos Stratidakis and Angeliki Alexiou
Wavefront engineering for applications in near-field wireless connectivity is gradually becoming common ground. In this landscape, beams that propagate on bent paths are ideal candidates for dynamic blockage avoidance and suppression of potential eavesdropping. In this work we study the physical layer security offered by bending beams, and we demonstrate their capabilities for line-of-sight and non-line-of-sight eavesdropping. We analyze the dependencies between the possible locations of an eavesdropper and the design parameters of such beams, and we introduce metrics to assess their physical layer security performance. Our results demonstrate their superiority with respect to beams generated with conventional beam-forming.
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Signal Processing
Multi-Objective RIS Deployment Optimization for Physical Layer Security in ISAC Networks
Wenqing Dai, Jan Herbst, Jan Petershans et al. (+2 more)
Reconfigurable Intelligent Surfaces (RIS) have emerged as a key enabler for programmable wireless environments in future Beyond-5G (B5G) and 6G networks. In the meantime, Integrated Sensing and Communication (
ISAC) and Physical-Layer Security (PLS) are becoming essential functionalities for next-generation wireless systems, particularly in safety and mission-critical applications. However, jointly optimizing RIS-assisted systems to support communication, sensing, and security introduces complex and often conflicting design trade-offs. In this work, a multi-objective optimization framework for RIS-assisted networks is proposed, aiming to jointly analyze communication performance, sensing accuracy, and security-related channel properties in a unified system perspective. The proposed model jointly considers RIS deployment location, orientation, surface size, and an
ISAC configuration weight that controls the allocation of RIS reflection gain between communication and sensing tasks. Simulation results reveal inherent trade-offs among communication reliability, sensing accuracy, and security performance. The proposed framework provides valuable insights into the interplay between communication, sensing, and security, and enables the design of efficient RIS deployment and configuration strategies for secure
ISAC-enabled 6G wireless networks.
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All authors Wenqing Dai, Jan Herbst, Jan Petershans, Christoph Lipps, Hans D. Schotten
Systems and Control
Designing Active Operation in Low-Voltage Distribution Grids: Requirements, Interfaces and Roadmap
Eric Tönges, Andrea Schoen, Frank Marten et al. (+2 more)
This paper outlines a pathway towards active operation of lowvoltage distribution grids. In these grids, the growing deployment of distributed generation, controllable demand and storage, together with the roll-out of intelligent metering systems, creates new requirements and opportunities for distribution system operators. On the basis of the German and European regulation, and in particular of recent directives enabling grid-oriented interventions and
market-based procurement of flexibility, the paper identifies three key pillars for active low-voltage operation: (a) measurement placement and observability, (b) secure and interoperable information and communication architectures and interfaces, and (c) integration of
market-based and gridoriented optimisation for controlling connected assets. A structured system overview is developed that specifies main actors and data flows, highlighting central research topics across these pillars. Building on this, a four-phase roadmap is presented, spanning requirements and use-case definition, method development and simulation, laboratory and field validation, and roll-out with system-level feedback, thus providing guidance for distribution system operators and researchers.
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All authors Eric Tönges, Andrea Schoen, Frank Marten, Marco Pau, Denis Mende
Signal Processing
Hiding Secrets in the CSI Quotient: A Robust Wi-Fi CSI Steganography System
Jiamu Guo, Hailang Jia, Guanxiong Shen et al. (+3 more)
Physical layer (PHY) steganography conceals secrets by making subtle modifications to transmitted
radio waveforms, which can be applied to establish covert communication systems. Given the widespread deployment of Wi-Fi infrastructures, hiding secrets within Wi-Fi transmissions exhibits significant covertness and has attracted increasing research
attention. Recent advances in Wi-Fi steganography have focused on
embedding secrets within channel state information (CSI) by applying artificial finite impulse response (FIR) filters to outgoing signals. These methods can emulate natural wireless propagation effects, thereby evading detection by eavesdroppers. However, existing CSI-based approaches suffer from two critical limitations: vulnerability to environmental variations and limited steganographic capacity. This work presents a Wi-Fi steganography system that mitigates these constraints. Specifically, we introduce a CSI division mechanism to decouple artificial CSI components from natural wireless channel responses. In essence, secrets are embedded within the quotient of two consecutive CSI measurements. Furthermore, we propose an encoder-decoder neural network framework that automatically learns optimal strategies for FIR filter generation and secret recovery, substantially enhancing steganographic capacity. We implemented a prototype using commercial off-the-shelf hardware, including a software-defined
radio (SDR) transmitter and two receiver platforms: ANTSDR and ESP32. Experimental evaluations demonstrate that the system achieves robust performance under dynamic environmental conditions while significantly improving steganographic capacity.
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All authors Jiamu Guo, Hailang Jia, Guanxiong Shen, Junqing Zhang, Linning Peng, Liquan Chen
Systems and Control
Approximate Simulation-based Hierarchical Control of Nonlinear Systems
Zirui Niu, Antoine Girard and Giordano Scarciotti
Controlling complex dynamical systems to satisfy sophisticated specifications remains a significant challenge in modern engineering. A promising approach to this problem is the approximate simulation-based hierarchical control (ASHC) technique. In this method, a simplified representation of the complex system, called the abstract system, is first designed and controlled. An interface function is then designed to translate the control law into the input of the complex system, thereby achieving approximate control synthesis. However, most existing results in ASHC are only for linear systems. This paper proposes a constructive method for solving the ASHC problem for nonlinear systems. To this end, we propose invariance equation-based methods to achieve the two classical requirements of the ASHC technique, namely the bounded output discrepancy and the $m$-relation. We then study the solvability conditions of the problem and summarise the overall design procedures. We illustrate the results with a practical example, providing step-by-step solutions to the ASHC problem of a DC-to-DC Ćuk converter.
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