Systems and Control
Efstathios Iliakis, Wallace Gian Yion Tan, Liang Wu et al. (+2 more)
This paper develops a parametric Koopman operator framework for
Stochastic Model Predictive Control (SMPC), where the Koopman operator is parametrized by
Polynomial Chaos Expansions (PCEs). The model is learned from data using the Extended Dynamic Mode Decomposition – Dictionary Learning (EDMD-DL) method, which preserves the convex least-squares structure for the PCE coefficients of the EDMD
matrix. Unlike conventional
stochastic Galerkin projection approaches, we derive a condensed deterministic reformulation of the SMPC problem whose dimension scales only with the control horizon and input dimension, and is independent of both the lifted state dimension and the number of retained PCE terms. Our framework, therefore, enables efficient nonlinear SMPC problems with expectation and second-order moment constraints with standard convex optimization solvers. Numerical examples demonstrate the efficacy of our framework for uncertainty-aware SMPC of nonlinear systems.
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All authors Efstathios Iliakis, Wallace Gian Yion Tan, Liang Wu, Jan Drgona, Richard D. Braatz
Systems and Control
Dispatch-Embedded Long-Term Tail Risk Assessment and Mitigation via CVaR for Renewable Power Systems
Kai Kang and Feng Liu
Renewable energy (RE) generation exhibits pronounced seasonality and variability, and neglecting these features can lead to significant underestimation of long-term power system risks in power supply. While long-term dispatch strategies are essential for evaluating and mitigating tail risks, they are often excluded from existing models due to their complexity. This paper proposes a long-term tail risk assessment and mitigation framework for renewable power systems, explicitly
embedding dispatch strategies. A representative scenario generation method is designed, combining multi-timescale Copula modeling to capture RE's long-range variability and correlation. Building on these scenarios, an
evolution-based risk assessment model is established, where Conditional Value-at-Risk (CVaR) is employed as a robust metric to quantify tail risks. Finally, a controlled
evolution-based risk mitigation scheme is introduced to refine long-term dispatch strategies for mitigating tail risks. Case studies on a modified IEEE-39 bus system incorporating real-world data substantiate the efficacy of the proposed method.
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Systems and Control
Soft projections for robust data-driven control
András Sasfi, Jaap Eising and Florian Dörfler
We consider data-based predictive control based on behavioral systems theory. In the linear setting this means that a system is described as a subspace of trajectories, and predictive control can be formulated using a projection onto the intersection of this behavior and a constraint set. Instead of learning the model, or subspace, we focus on determining this projection from data. Motivated by the use of regularization in data-enabled predictive control (DeePC), we introduce the use of soft projections, which approximate the true projector onto the behavior from noisy data. In the simplest case, these are equivalent to known regularized DeePC schemes, but they exhibit a number of benefits. First, we provide a bound on the approximation error consisting of a bias and a variance term that can be traded-off by the regularization weight. The derived bound is independent of the true system order, highlighting the benefit of soft projections compared to low-dimensional subspace estimates. Moreover, soft projections allow for intuitive generalizations, one of which we show has superior performance on a case study. Finally, we provide update formulas for soft projectors enabling the efficient adaptation of the proposed data-driven control methods in the case of streaming data.
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Signal Processing
DOA Estimation for Low-Altitude Networks: HAD Architectures, Methods, and Challenges
Ye Tian, Tuo Wu, Jintao Wu et al. (+4 more)
With the rapid expansion of low-altitude economy (LAE) services and the growing demand for integrated sensing and communication (
ISAC) in air-ground networks, reliable direction-of-arrival (DOA) estimation has become essential for both directional communication and sensing functions. DOA underpins beam
alignment, spatial-reuse scheduling, and
ISAC-critical tasks such as airspace situational awareness and multi-target monitoring. Hybrid analog-digital (HAD) architectures have emerged as a practical solution for large-aperture directional operation under stringent
radio frequency (RF), analog-to-digital converter (ADC), and size, weight, and power (SWaP) constraints. However, HAD compresses
antenna-domain observations through analog combining, fundamentally reshaping the measurement model and introducing new algorithmic and system-level challenges for DOA estimation. This article first reviews the principles and representative architectures of HAD, highlighting their advantages for scalable beam-centric and
ISAC-oriented operation in LAE scenarios. We then provide a structured overview of HAD-enabled DOA estimation methodologies, including spatial covariance
matrix (SCM) reconstruction, multi-combiner scan-based acquisition, and pilot-aided estimation, along with key design tradeoffs. Finally, we discuss open challenges and outline reliability-driven research directions toward robust, deployable HAD-enabled DOA solutions for practical
ISAC-enabled low-altitude environments.
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All authors Ye Tian, Tuo Wu, Jintao Wu, He Xu, Yuanjun Shen, Xianfu Lei, Kin-Fai Tong
Signal Processing
Optimal Anchor Placement for Wireless Localization in Mixed LOS and NLOS Scenarios
Gaurav Duggal, R. Michael Buehrer, Harpreet S. Dhillon et al. (+1 more)
This paper develops a general optimal experimental design framework for anchor placement in localization and related estimation problems whose Fisher information
matrix (FIM) can be expressed as a weighted sum of rank-one
matrices. Within this framework, anchor locations are selected to optimize Cramér–Rao lower bound (CRLB)-based performance measures, while accounting jointly for measurement geometry and information strength. As a challenging motivating application, we consider localization in diffraction-dominated non-line-of-sight (NLOS) outdoor-to-indoor (O2I) environments, where the direct path is severely attenuated or blocked. Although diffraction-based time-of-arrival (TOA) measurements can provide useful positioning information, optimal anchor placement is less intuitive than in conventional line-of-sight (LOS) settings because both the measurement directions and their information strengths depend on the propagation environment. The proposed $S_n,r_n$ -based FIM representation compactly captures this interaction between geometry and signal strength. For a single target, it yields a polygon-closure interpretation and characterizes the conditions under which A-, D-, and E-optimal anchor geometries coincide. We then extend the framework to region-wide anchor placement by constructing a precomputed anchor–target dictionary and formulating min–max E- and D-optimal anchor-selection problems as mixed-integer second-order cone programs. Numerical results demonstrate the resulting anchor layouts, localization bounds, and computational tradeoffs in a mixed-LOS/NLOS O2I environment. While the analysis is instantiated and evaluated for diffraction-based O2I localization, its underlying results apply broadly to estimation and anchor-placement problems with weighted rank-one-sum FIMs.
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All authors Gaurav Duggal, R. Michael Buehrer, Harpreet S. Dhillon, Jeffrey H. Reed
Signal Processing
Spatial Upper Bound of Radiated Power in Active Antenna Systems
Dominique Nussbaum, Christ Rizk, Eric Seguenot et al. (+4 more)
The assessment of unwanted radiated emissions from Active
Antenna Systems (AAS) has become a critical issue in adjacent-band coexistence scenarios. In this paper, we establish the existence of a deterministic spatial upper bound on the radiated power of active
antenna arrays. We show that the maximum radiated power always occurs in the boresight direction, irrespective of frequency or signal nature (useful signal, nonlinear distortion, or noise), or instantaneous beamforming configuration, thereby defining a conservative spatial upper bound whose angular envelope is solely determined by the elementary radiating building block of the
antenna architecture, i.e., the element or sub-array radiation pattern. Starting from a two-element array with third-order nonlinearities, we derive the spatial envelope and extend the result to realistic AAS architectures. The theoretical findings are validated by over-the-air (OTA) measurements performed on a 3.5 GHz Massive Multiple-Input Multiple-Output (
MIMO)
antenna. The proposed approach offers a simple, robust, and measurement-oriented methodology for coexistence assessments involving beamformed
radio systems.
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All authors Dominique Nussbaum, Christ Rizk, Eric Seguenot, Florian Kaltenberger, Andrea Moro, Alessandro Sinicco, Laura Pometcu
Systems and Control
Bridging RL and MPC for mixed-integer optimal control with application to Formula 1 race strategies
Joschua Wüthrich, Romir Damle, Giona Fieni et al. (+3 more)
We propose a hybrid reinforcement learning (RL) and model predictive control (MPC) framework for mixed-integer optimal control, where discrete variables enter the cost and dynamics but not the constraints. Existing hierarchical approaches learn a policy only for the discrete action space, leaving continuous optimization to MPC. Unlike these methods, we train an RL
agent on the full hybrid action space, such that the learned critic approximates the Q-function of the underlying Markov decision process. During deployment, the RL actor is rolled out over the prediction horizon to parametrize an integer-free nonlinear MPC through the discrete action sequence and provide a continuous warm-start. The learned critic can additionally serve as a terminal cost to capture long-term performance. We prove recursive feasibility, and validate the framework on a Formula 1 race strategy problem, where an ablation study identifies the contribution of each learned component. The hybrid method achieves near-optimal performance relative to an offline mixed-integer nonlinear program
benchmark, outperforming a standalone RL
agent. Moreover, the hybrid scheme enables adaptation to unseen disturbances through modular MPC extensions at zero retraining cost.
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All authors Joschua Wüthrich, Romir Damle, Giona Fieni, Melanie N. Zeilinger, Christopher H. Onder, Andrea Carron
Systems and Control
Min-Max Grassmannian Optimization for Online Subspace Tracking
Shreyas Bharadwaj, Bamdev Mishra, Cyrus Mostajeran et al. (+3 more)
We propose GeRoST (Geometrically Robust Subspace Tracking), an online subspace tracking
algorithm that models uncertainty in a subspace using a Grassmannian ball. We derive an exact scalar dual for the worst-case subspace problem, establish conditions for a unique worst-case subspace and a Riemannian gradient, and characterize the minimum radius needed to cover a dimensional extension of the target subspace. Each update uses either a spectral direction computed in a reduced subspace or the gradient of the window reconstruction loss. Our numerical experiments show that GeRoST achieves lower mean post-fault prediction error than GREAT in system identification. In
video separation, it achieves higher precision and a better precision–recall balance, as measured by the F$_1$ score, than both GREAT and GRASTA at the reported thresholds, with lower recall and longer runtime.
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All authors Shreyas Bharadwaj, Bamdev Mishra, Cyrus Mostajeran, Alberto Padoan, Jeremy Coulson, Ravi Banavar
Signal Processing
Novel Single Clad Ho-doped Fiber with High Slope Efficiency and Low Ion Pairing
Robert E. Tench, Wiktor Walasik, Alexandre Amavigan et al. (+3 more)
We report the design and experimental and simulated performance for a 2050 nm band fiber amplifier with high optical-optical slope efficiency and low ion pairing, using a novel high performance single clad Ho-doped fiber from the Naval Research Laboratory (NRL). We measure an optical-optical slope efficiency of 57% using 1 mW input signal power and 1860 nm pumping which we believe is the highest slope efficiency obtained to date for a single clad single stage copumped HDFA. A new method for non-destructive measurement of the ion pairing coefficient in Ho-doped fibers is introduced and validated. Using this method, we link our 57% slope efficiency to a low ion pairing coefficient of 4% in the NRL Ho-doped fiber as derived from our experimental data. We present an overview and survey of the ion pairing results for Ho-doped fiber amplifiers and
lasers reported so far in the literature.
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All authors Robert E. Tench, Wiktor Walasik, Alexandre Amavigan, Jean-Marc Delavaux, Colin C. Baker, Daniel Rhonehouse
Signal Processing
Unsupervised End-to-End Array Calibration for Multi-Target Integrated Sensing and Communication
José Miguel Mateos-Ramos, Baptiste Chatelier, Luc Le Magoarou et al. (+3 more)
In this work, we consider end-to-end calibration of an integrated sensing and communication (
ISAC) base station (BS) under gain-phase and
antenna displacement impairments without collecting signals from predefined positions (labeled data). We consider a BS with two impaired uniform linear arrays used for simultaneous multi-target sensing and communication with a user equipment (UE) leveraging orthogonal frequency-division multiplexing signals. The main contribution is the design of a framework that can compensate for the impairments without labeled data and considering coherent receive signals. We harness a differentiable precoder based on the maximum array response in an angular direction at the transmitter and the orthogonal matching pursuit (OMP)
algorithm at the sensing receiver. We propose an
ISAC loss as a combination of sensing and communication losses that provides a trade-off between the two functionalities. We compare two sensing objective alternatives: (i) maximize the maximum response of the angle-delay map of the targets or (ii) minimize the norm of the residual signal at the output of the OMP
algorithm after all estimated targets have been removed. The communication objective maximizes the energy of the received signal at the UE. Additionally, our framework leverages an approximation of the channel gradient that avoids the impractical knowledge of the gradient of the channel. Our results show that the proposed method performs closely to using labeled data and knowledge of the channel gradient in terms of sensing position estimation and communication symbol error rate. When comparing the two sensing losses, minimizing the norm of the OMP residual yields significantly better sensing position estimation with slightly increased complexity.
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All authors José Miguel Mateos-Ramos, Baptiste Chatelier, Luc Le Magoarou, Nir Shlezinger, Henk Wymeersch, Christian Häger
Audio and Speech Processing
Description and Discussion on DCASE 2026 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Binh Thien Nguyen, Masahiro Yasuda, Noboru Harada et al. (+8 more)
This paper presents an overview of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2026 Challenge Task 4, Spatial Semantic
Segmentation of Sound Scenes (S5). The S5 task focuses on the joint detection and separation of sound events in complex spatial audio mixtures, contributing to the foundation of immersive communication. First introduced in DCASE 2025, the S5 task continues in DCASE 2026 Task 4 with key changes to better reflect real-world conditions, including allowing mixtures to contain multiple sources of the same class and to contain no target sources. In this paper, we describe task setting, along with the corresponding updates to the evaluation metrics and
dataset. The experimental results of the submitted systems are also reported and analyzed. The official access point for data and code is https://github.com/nttcslab/dcase2026_task4_baseline.
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All authors Binh Thien Nguyen, Masahiro Yasuda, Noboru Harada, Romain Serizel, Mayank Mishra, Marc Delcroix, Carlos Hernandez-Olivan, Shoko Araki, Daiki Takeuchi, Tomohiro Nakatani, Nobutaka Ono
Systems and Control
Neural Vector Lyapunov-Razumikhin Certificates for Delayed Interconnected Systems
Jingyuan Zhou, Yuexuan Wang and Kaidi Yang
Ensuring scalable input-to-state stability (sISS) is critical for the safety and reliability of large-scale interconnected systems, especially in the presence of communication delays. While learning-based controllers can achieve strong empirical performance, their black-box nature makes it difficult to provide formal and scalable stability guarantees. To address this gap, we propose a framework to synthesize and verify neural vector Lyapunov-Razumikhin certificates for discrete-time delayed interconnected systems. Our contributions are three-fold. First, we establish a sufficient condition for discrete-time sISS via vector Lyapunov-Razumikhin functions, which enables certification for large-scale delayed interconnected systems. Second, we develop a scalable synthesis and verification framework that learns the neural certificates and verifies the certificates on reachability-constrained delay domains with scalability analysis. Third, we validate our approach on mixed-autonomy platoons, drone formations, and microgrids against multiple baselines, showing improved verification efficiency with competitive control performance.
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Systems and Control · Cross-listed from cs.IT
Optimal Sampling and Actuation for Real-Time Monitoring of Markov Sources
Mehrdad Salimnejad, Anthony Ephremides, Marios Kountouris et al. (+1 more)
This paper studies efficient data management and timely information dissemination for real-time monitoring of an N-state Markov process, with the objective of enabling accurate state estimation and reliable actuation decisions. We analyze the real-time reconstruction error and the Age of Incorrect Information (AoII), and derive closed-form expressions for their time-averaged values under several sampling and transmission policies. We then formulate and solve constrained optimization problems to minimize the time-averaged reconstruction error and the average AoII under a time-averaged sampling frequency constraint. The resulting optimal sampling and transmission policies are compared to identify the conditions under which each policy is most effective. We further show that directly using the reconstructed state for actuation can degrade system performance, especially when the receiver is uncertain about the state estimate or when actuation is costly. These findings reveal that accurate state estimation alone does not necessarily lead to effective actuation, highlighting the importance of incorporating uncertainty into the decision-making process. To address this issue, we introduce a cost function, termed the Cost of Actions under Uncertainty (CoAU), which characterizes correct and incorrect actuation decisions under receiver-side uncertainty. We propose a randomized actuation policy and derive a closed-form expression for the probability of a correct actuation decision, defined as the event in which the CoAU equals zero. Finally, we formulate an optimization problem to find the optimal randomized actuation policy that maximizes this probability. The results show that the resulting policy substantially reduces incorrect actuator actions.
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All authors Mehrdad Salimnejad, Anthony Ephremides, Marios Kountouris, Nikolaos Pappas
Signal Processing
Learning Laplacian Forms for Graph Signal Processing via the Deformed Laplacian
Stefania Sardellitti
Learning the graph
Laplacian from observed data is one of the most investigated and fundamental tasks in Graph Signal Processing (GSP). Different variants of the
Laplacian, such as the combinatorial, signless or signed
Laplacians have been considered depending on the type of features to be extracted from the data. The main contribution of this paper is the introduction of a parametric
Laplacian, called the deformed
Laplacian, defined as a quadratic
matrix polynomial that provides a parametric dictionary for graph signal processing. The deformed
Laplacian can be interpreted as the generator of a parametric linear reaction-
diffusion dynamics on graphs, capturing the interplay between diffusive coupling and nodal reaction effects. It is a parametric
polynomial matrix that enables the design of novel topological operators tailored to both the underlying graph structure and the observed signals. Interestingly, we show that several
Laplacian variants proposed in the literature arise as special cases of the deformed
Laplacian. We then develop a method to jointly learn the deformed
Laplacian and the graph signals from data, showing how its use improves signal representation across a broad class of graphs compared to standard
Laplacian forms. Through extensive numerical experiments on both synthetic and real-world
datasets, including financial and communication networks, we assess the benefits of the proposed method in terms of graph signal reconstruction error and sparsity of the representation.
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Signal Processing
3D User Localization for Planar Arrays in LoS Near- and Far-Fields via Summed Phase Differences
Sergey Isaev and Nikola Zlatanov
This letter presents a phase-difference-based scheme for three-dimensional (3D) line-of-sight (LoS) user localization using a uniform planar array (UPA), applicable to both near-field and far-field regimes under the exact spherical-wave model. Unlike the previously studied two-dimensional (2D) uniform linear array (ULA) case, the 3D UPA case requires jointly exploiting the two array axes in order to recover the user's range, azimuth, and zenith angle. Adjacent-
antenna phase-differences are first estimated from uplink pilots and then summed along the array axes to obtain unwrapped phase-differences between widely separated
antenna elements. These summed phase-differences enable the construction of multiple three-equation systems whose solutions yield the user's range, azimuth, and zenith angle. We quantify the number of such equation systems, provide a representative closed-form
estimator that uses only three phase-difference sums, and propose an all-data nonlinear least-squares
estimator that exploits all available sums. Numerical results include a classical Fraunhofer near-field case and absolute 3D position RMSE in meters, and show near-CRB performance when the closed-form initialization is reliable. Moreover, unlike state-of-the-art baseline schemes, whose performance depends on well-tuned hyperparameters, the proposed
estimators are hyperparameter-free.
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Audio and Speech Processing · Cross-listed from cs.CL
OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models
Han Zhu, Lingxuan Ye, Wei Kang et al. (+7 more)
We present OmniVoice, a massively
multilingual zero-shot text-to-
speech (TTS) model that scales to over 600
languages. At its core is a novel
diffusion language model-style discrete non-autoregressive (NAR) architecture. Unlike conventional discrete NAR models that suffer from performance bottlenecks in complex two-stage (text-to-semantic-to-acoustic) pipelines, OmniVoice directly maps text to multi-codebook acoustic
tokens. This simplified approach is facilitated by two key technical innovations: (1) a full-codebook random masking strategy for efficient training, and (2) initialization from a pre-trained LLM to ensure superior intelligibility. By leveraging a 581k-hour
multilingual dataset curated entirely from open-source data, OmniVoice achieves the broadest
language coverage to date and delivers state-of-the-art performance across Chinese, English, and diverse
multilingual benchmarks. Our code and pre-trained models are publicly available at https://github.com/k2-fsa/OmniVoice.
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All authors Han Zhu, Lingxuan Ye, Wei Kang, Zengwei Yao, Liyong Guo, Fangjun Kuang, Zhifeng Han, Weiji Zhuang, Long Lin, Daniel Povey
Signal Processing
DF-3DRME: A Data-Friendly Learning Framework for 3D Radio Map Estimation based on Super-Resolution Technique
Lin Zhu, Weifeng Zhu, Shuowen Zhang et al. (+2 more)
High-Resolution three-dimensional (3D)
radio maps (RMs) provide rich information about the
radio landscape that is essential to a myriad of wireless applications in the future wireless networks. Although deep learning (DL) methods have shown their effectiveness in RM construction, existing approaches require massive high-resolution 3D RM samples in the training
dataset, the acquisition of which is labor-intensive and time-consuming in practice. In this paper, our goal is to devise a data-friendly high-resolution 3D RM construction solution via training over a hybrid
dataset, wherein the RMs associated with a small fraction of environment maps (EMs) are of high-resolution, while those corresponding to the majority of EMs are of low-resolution. To this end, we propose a Data-Friendly 3D
Radio Map
Estimator (DF-3DRME), which comprises two processing stages. Specifically, in the first stage, we leverage the abundant low-resolution 3D RM samples to train a neural network, termed the LR-Net, for predicting the low-resolution 3D RM from the input EM, which provides a coarse characterization of the spatial
radio propagation. In the second stage, we employ an advanced super-resolution network, termed the SR-Net, to upscale the predicted low-resolution 3D RM to its high-resolution counterpart. Unlike the LR-Net, the SR-Net can be effectively trained with only the limited high-resolution 3D RM samples available in the hybrid
dataset. Experimental results demonstrate that the proposed framework achieves compelling reconstruction performance with only 4% of the EMs in the
dataset having high-resolution 3D RM labels, which significantly reduces data acquisition overhead and facilitates practical deployment.
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All authors Lin Zhu, Weifeng Zhu, Shuowen Zhang, Giuseppe Caire, Liang Liu
Systems and Control · Cross-listed from physics.soc-ph
Managing the Mismatch: The Role of Flexibility on the Path to a Carbon-Neutral Energy System
Julian Geis, Michael Lindner and Tom Brown
A rapid expansion of system flexibility is essential to integrate increasing shares of renewable energy into future energy systems. However, flexibility needs and technology-specific contributions to flexibility remain poorly quantified in energy system modelling. Existing methods are not widely applied, leaving key questions unanswered: which flexibility technologies are critical for
climate neutrality, and what are the cost implications of alternative deployment strategies? To address this gap, we apply a correlation-based flexibility metric to a high-resolution, sector-coupled model of the German energy system, covering its transformation towards
climate neutrality. For our default scenario, we find that daily flexibility needs increase by a factor of 3.7 between 2025 and 2045, driven primarily by the expansion of
solar PV. By 2045, stationary batteries provide 38% of daily flexibility, while flexible electric vehicle charging contributes 30%. Systems with constrained flexibility increase system costs by 6.9%, electricity prices by 14 EUR/MWh and trigger 47% higher hydrogen and e-fuel imports compared to an unconstrained system in 2045. In contrast, scenarios with high shares of flexible electric vehicle charging, vehicle-to-grid, and industrial demand-side management achieve system cost reductions of 3.3%, while also reducing import dependence. Higher flexibility also reduces electricity price ranges, decreases average electricity prices by 3 EUR/MWh, and reduces backup capacity by 22% (22 GW). Overall, our results highlight the decisive role of specific flexibility technologies in achieving cost-efficient and energy-secure
climate-neutral energy systems, providing quantitative guidance for policy and investment decisions.
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Systems and Control
Estimating Density Functions for Probabilistic Power Flow Using Invertible Neural Networks
Weijie Xia, James Ciyu Qin, Edgar Mauricio Salazar Duque et al. (+4 more)
Probabilistic power flow (PPF) is essential for quantifying operational uncertainty in modern power systems with high penetrations of renewable generation and flexible loads. Conventional PPF methods primarily rely on Monte Carlo (MC)based power flow (PF) simulations or simplified approximations of voltage probability density functions. Although MC methods provide high accuracy, they incur substantial computational and data-storage costs, whereas simplified approximations, though lightweight, often sacrifice accuracy due to the reliance of certain assumption. In this paper, we propose a novel PPF density approximation framework that leverages the expressive power of neural networks (NNs) and can, in principle, approximate complex voltage distributions directly without restrictive assumptions. The core idea is to learn an explicit mapping between
stochastic power injections and system voltages using invertible neural networks (INNs), and then to apply the change-of-variables
theorem to obtain the voltage densities. Extensive numerical studies demonstrate that the proposed framework achieves stateof-the-art performance both as an accurate PF solver and as an efficient PPF density
estimator
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All authors Weijie Xia, James Ciyu Qin, Edgar Mauricio Salazar Duque, Hongjin Du, Peter Palensky, Giovanni Sansavini, Pedro P. Vergara
Systems and Control
Explicit MPC for Parameter Dependent Linear Systems
Carlos J. G. Rojas, Esteban Lage Cano and Leyla Özkan
This paper presents two explicit Model Predictive Control formulations for linear systems parameterized in terms of design variables. Such parameter dependent behavior commonly arises from operating point dependent linearization of nonlinear systems as well as from variations in mechanical, electrical, or thermal properties associated with material selection in the design of the process or system components. In contrast to explicit MPC approaches that treat design parameter variations and dependencies as disturbances, the proposed methods incorporate the parameters directly into the system
matrices in an affine manner. However, explicitly incorporating these dependencies significantly increases the complexity of explicit MPC formulations due to resulting nonlinear terms involving decision variables and parameters. We address this complexity by proposing two approximation methods. Both methods are applied to two examples, and their performances are compared with respect to the exact eMPC implementation.
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Systems and Control
Battery Electric Truck Infrastructure Co-design via Joint Optimization and Agent-based Simulation
Juan Pablo Bertucci, Mauro Salazar and Theo Hofman
As zero-emission zones emerge in European cities, fleet operators are shifting to electric vehicles. To maintain their current operations, a clear understanding of the charging infrastructure required and its relationship to existing power grid limitations is needed. This study presents an optimization frame-work for jointly designing charging infrastructure and schedules within a logistics distribution network, validated through
agent-based simulations. We formulate the problem as a mixed-integer linear program and develop an
agent-based model to evaluate various designs and operations under
stochastic conditions. Our experiments compare rule-based and optimized strategies in a case study of the Netherlands. Results show that current commercial solutions suffice for middle-mile logistics, with central co-design yielding average cost reductions of 5.2% to 6.4% and an average 20.1% decrease in total installed power. While rule-based control effectively manages charging operations and mitigates delays, optimizing charge scheduling significantly reduces queuing times (99%), charging costs (13.5%), and time spent near capacity (10.9%). Our optimization-simulation framework paves the way for combining optimized infrastructure planning and realistic fleet operations in digital-twin environments.
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Systems and Control
Optimal GNSS Time Tracking for Long-term Stable Time Realisation in Synchronised Atomic Clocks
Maitreyee Dutta, Jiayu Chen, Masakazu Koike et al. (+3 more)
In this manuscript, we propose a novel optimal Global
Navigation Satellite System (GNSS) time tracking
algorithm to collectively steer an ensemble consisting of synchronising miniature atomic clocks towards standard GNSS time. The synchronising miniature atomic clocks generate a common synchronised time which has good short term performance but its accuracy and precision, which is measured by Allan variance, deteriorates in the long run. So, a supervisor designs and periodically broadcasts the proposed GNSS time tracking control to the ensemble miniature atomic clocks that steer the average of ensemble towards the average of GNSS receivers, which are receivers of GNSS time. The tracking control is constructed using a Kalman filter estimation process that estimates the difference in average of GNSS receivers and average of ensemble clocks by using relative clock readings between GNSS receivers and their adjacent ensemble clock. Under the influence of the periodically received tracking control, the stabilised ensemble clocks have better long term accuracy and precision over long averaging periods. Since the tracking control is designed to solely influence the average of the ensemble, the tracking process does not interfere with the synchronisation process and vice versa. The feedback
matrix associated with the tracking control is obtained from an optimisation problem that minimises steady-state Allan variance. Numerical results are provided to show the efficacy of the proposed
algorithm for enhancing long term performance.
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All authors Maitreyee Dutta, Jiayu Chen, Masakazu Koike, Yuichiro Yano, Yuko Hanado, Takayuki Ishizaki
Signal Processing
Toward Robust Semantic Communications: Proactive Importance-Ordered Restructuring for Enhanced Unequal Error Protection
Xunyang Zhan, Jie Cao, Xu Zhu et al. (+3 more)
Semantic communications (SemCom) is a promising task-oriented paradigm in which semantic features exhibit non-uniform importance. Consequently, unequal error protection (UEP), which allocates resources based on semantic importance, plays a pivotal role in maximizing system utility. However, most existing schemes adopt passive importance evaluation, which neither proactively reshapes the importance distribution nor explores its impact on UEP performance. In this paper, we propose a novel importance-ordered semantic feature restructuring (ISFR) scheme that proactively enforces a descending importance hierarchy and jointly optimizes multi-dimensional resources to improve system utility. Specifically, modules with decreasing retention probabilities and increasing distortion levels are employed, which drive the model to concentrate key semantics into front-end features and thus strengthen importance differentiation. Moreover, a joint optimization problem that jointly optimizes channel matching, feature selection, modulation schemes, and power allocation is formulated to minimize the importance-weighted total semantic distortion. To solve this non-convex problem, a hierarchical decoupling strategy is proposed, which decomposes it into four tractable subproblems. This approach leverages the ordered prior to drastically prune the search space for feature selection and modulation, while integrating greedy-based channel matching and convex power allocation. Simulation results demonstrate that the proposed ISFR scheme outperforms traditional uniform importance-based schemes under harsh channel conditions and limited resources, validating the significant robustness improvement enabled by the concentration of key semantic information.
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All authors Xunyang Zhan, Jie Cao, Xu Zhu, Nikolaos Pappas, Zhijin Qin, Shaohan Feng
Signal Processing
Single-Waveguide Multiple-Pinching-Antenna Systems: OMA versus NOMA
Yanyu Cheng, Hao Li, Zhen Wang et al. (+1 more)
This paper investigates the performance of a pinching-
antenna (PA) system with a signal waveguide and multiple pinching
antennas to serve users distributed across multiple rooms. The performance of the system is evaluated through a comparative analysis under both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. Specifically, this paper derives closed-form expressions for the outage probability (OP) and ergodic rate (ER) in each scheme. Furthermore, asymptotic analyses are conducted to characterize the system behavior in the high signal-to-noise ratio (SNR) regime. Extensive Monte Carlo simulations are utilized to validate the accuracy of the analytical derivations. The comparative results can be summarized as follows: 1) in the downlink fixed-rate scenario, whether OMA or NOMA achieves better outage performance depends on system parameters, such as the number of users and power allocation coefficients; 2) in the uplink fixed-rate scenario, the outage performance of NOMA is inferior to that of OMA in the high-SNR regime, and the decay rate of the OP for NOMA users depends on the rate thresholds; and 3) for both uplink and downlink adaptive-rate scenarios, the rate performance comparison of the two schemes depends on system parameters in the low-SNR regime, whereas OMA generally outperforms NOMA in the high-SNR regime.
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All authors Yanyu Cheng, Hao Li, Zhen Wang, Zhiguo Ding
Signal Processing
SAR/ISAR Imaging in 6G Network
Yanmo Hu, Shuowen Zhang, Ross Murch et al. (+1 more)
Imaging is a crucial sensing function that finds wide applications in environmental reconstruction, autonomous driving, etc. However, the signal processing methods for existing
radio imaging techniques, such as millimeter wave (mmWave) imaging, require high-resolution range estimation enabled by Gigahertz-level or even Terahertz-level bandwidth, and cannot be applied in 6G integrated sensing and communication (
ISAC) network with Megahertz-level bandwidth. This paper proposes two novel high-resolution
radio imaging schemes that can work on the 6G signals with limited bandwidth - bandwidth-independent synthetic aperture radar (BI-SAR), where the movable base station (BS) revolves along the static targets by 360 degrees; as well as bandwidth-independent inverse synthetic aperture radar (BI-ISAR), where the BS is static and the targets revolve along an axis by 360 degrees. Different from conventional SAR and ISAR counterparts that rely on range estimation, our proposed imaging schemes solely utilize Doppler information to perform imaging without any range information. The main technical challenge of our schemes lies in the anisotropic scattering functions over different directions, which hinder the coherent synthesis of the backscattered signals from all directions. We design an iterative adaptive approach-based Doppler association (IAA-DA)
algorithm to tackle the above issue. Moreover, we also derive the imaging resolution to characterize the reconstruction quality. Real-world experiments are provided to show the feasibility and the effectiveness of our proposed 6G imaging schemes.
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All authors Yanmo Hu, Shuowen Zhang, Ross Murch, Liang Liu
Systems and Control · Cross-listed from math.AP
Verifying Well-Posedness of Linear PDEs using Convex Optimization
Declan S. Jagt and Matthew M. Peet
Ensuring that a PDE model is well-posed is a necessary precursor to any form of analysis, control, or numerical simulation. Although the Lumer–Phillips
theorem provides necessary and sufficient conditions for well-posedness of dissipative PDEs, these conditions must hold only on the domain of the PDE—a proper subspace of $L_{2}$—which can make them difficult to verify in practice. In this paper, we show how the Lumer–Phillips conditions for PDEs can be tested more conveniently using the equivalent Partial Integral Equation (PIE) representation. This representation introduces a fundamental state in the
Hilbert space $L_{2}$ and provides a bijection between this state space and the PDE domain. Using this bijection, we reformulate the Lumer–Phillips conditions as operator inequalities on $L_{2}$. We show how these inequalities can be tested using convex optimization methods, establishing an upper bound on the exponential growth rate of solutions. We demonstrate the effectiveness of the proposed approach by verifying well-posedness for several classical examples of
parabolic and
hyperbolic PDEs.
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Signal Processing
CRLB Minimization for ISAC Systems with Segmented Waveguide-Enabled Pinching Antenna
Yue Geng, Tee Hiang Cheng, Kah Chan Teh et al. (+1 more)
Pinching-
antenna (PA) has recently attracted considerable research
attention in wireless systems, realized by attaching small dielectric particles along a waveguide. Building upon which, the segmented waveguide-enabled pinching-
antenna system (SWAN) has been proposed to mitigate the inter-
antenna radiation problem in uplink transmissions of conventional PA systems. In this work, SWAN-assisted integrated sensing and communication (
ISAC) is investigated, where a base station (BS) equipped with SWAN provides downlink communications for multiple communication users (CUs) and performs sensing for multiple targets. The dual-functional signals transmitted by the BS are radiated by the SWAN, and the echo signals reflected by the targets are captured by the SWAN and relayed to the BS for estimating the locations of the targets. We formulate a Cramér-Rao lower bound (CRLB) minimization problem to evaluate the performance of the
ISAC system, where the CRLB of the location estimation is minimized under communication rate constraints. To jointly optimize the beamforming and the PA positions of the SWAN, we develop a Riemannian
manifold optimization (RMO) method, where each variable is constrained on its corresponding Riemannian
manifold, and a Riemannian product
manifold (RPM) is constructed as the solution space. A penalty method combined with Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS)
algorithm is applied to obtain a feasible solution. Simulation results show that the proposed SWAN-assisted
ISAC system yields superior CRLB performance for target localization compared with existing schemes including the multi-waveguide-enabled pinching-
antenna-assisted
ISAC systems.
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All authors Yue Geng, Tee Hiang Cheng, Kah Chan Teh, Zhiguo Ding
Systems and Control
Toward Efficient Deployment and Synchronization in Digital Twins-Empowered Networks
Hossam Farag and Cedomir Stefanovic
Digital twins (DTs) are envisioned as a key enabler of the cyber-physical continuum in future wireless networks. However, efficient deployment and synchronization of DTs in dynamic multi-access edge computing (MEC) environments remains challenging due to time-varying communication and computational resources. This paper investigates the joint optimization of DT deployment and synchronization in dynamic MEC environments. A deep reinforcement learning (DRL) framework is proposed for adaptive DT placement and association to minimize interaction latency between physical and digital entities. To ensure semantic freshness, an update scheduling policy is further designed to minimize the long-term weighted sum of the Age of Changed Information (AoCI) and the update cost. A relative policy iteration
algorithm with a threshold-based structure is developed to derive the optimal policy. Simulation results show that the proposed methods achieve lower latency, enhanced information freshness, and reduced system cost compared with
benchmark schemes
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Systems and Control
Typical Scenarios Generation Method Considering System-level Characteristics of Power System
Tao Li and Chen Shen
This paper proposes a method for generating typical scenarios based on system-level macroscopic characteristics of power system and considering its stability properties. First, considering uncertainties such as renewable energy generation in power-electronics-dominated power systems, multidimensional scaling is used to construct an electrical coordinate system. Based on this, system-level characteristics of the distribution of physical quantities, such as power generation and load, are characterized. Furthermore, a method for generating typical scenarios based on the system's system-level characteristics and stability properties is proposed. For the obtained joint probability distribution of system-level characteristics, weighted Mahalanobis distance can be used to predict the stability properties of random scenarios. Finally, the typicality and representativeness of the scenarios generated by the proposed method with respect to stability properties are verified on the CSEE
benchmark case, and stability prediction for random scenarios is achieved using a probabilistic testing method.
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Systems and Control
Robust IMMPC: An Offset-free MPC for Rejecting Unknown Disturbances
Felix Brändle and Frank Allgöwer
Output regulation is the problem of finding a control input to asymptotically track reference trajectories and reject disturbances. This can be addressed by using the internal model principle to embed a model of the disturbance in the controller. In this work, we present a Model Predictive Control scheme to achieve offset-free control. To do so, we extend Internal Model MPC to general bounded disturbances that must not be generated by the disturbance model. We show recursive feasibility, constraint satisfaction, and provide convergence conditions for the optimal reachable output. The proposed controller is validated on a four-tank system.
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