Signal Processing
Deterministic Modeling of Dynamic ISAC Channels in RF Digital Twin Environments
Cesar Montaner, Saúl Fenollosa, Andres Ortega et al. (+2 more)
This paper introduces a methodology to calibrate
Radio-Frequency Digital Twins (RF-DTs) for Integrated Sensing and Communication (
ISAC) in dynamic wireless environments. The approach leverages high-resolution ray tracing in combination with wideband channel sounding to ensure consistency between simulated and measured propagation. The methodology is validated in urban scenarios featuring both mono-static and bi-static configurations, as well as moving user platforms and vehicles. Results show that the calibrated RF-DT reproduces key propagation effects, including multipath
evolution, dynamic scatterers, and Doppler-induced signatures, with close agreement to measurements. These findings confirm that accurate geometry, material modeling,
antenna patterns, and diffuse scattering are essential for realistic high-frequency
ISAC simulation. By bridging the gap between simulation and measurement, the proposed calibration framework provides a scalable tool for developing and evaluating
ISAC algorithms in complex, time-varying environments envisioned for 6G.
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All authors Cesar Montaner, Saúl Fenollosa, Andres Ortega, Hugo Beltrán, Narcis Cardona
Audio and Speech Processing
Acoustic-to-articulatory Inversion of the Complete Vocal Tract from RT-MRI with Various Audio Embeddings and Dataset Sizes
Sofiane Azzouz, Pierre-André Vuissoz and Yves Laprie
Articulatory-to-acoustic inversion strongly depends on the type of data used. While most previous studies rely on EMA, which is limited by the number of sensors and restricted to accessible articulators, we propose an approach aiming at a complete inversion of the vocal tract, from the glottis to the lips. To this end, we used approximately 3.5 hours of RT-MRI data from a single speaker. The innovation of our approach lies in the use of articulator contours automatically extracted from MRI images, rather than relying on the raw images themselves. By focusing on these contours, the model prioritizes the essential
geometric dynamics of the vocal tract while discarding redundant pixel-level information. These contours, alongside denoised audio, were then processed using a Bi-LSTM architecture. Two experiments were conducted: (1) the analysis of the impact of the audio
embedding, for which three types of
embeddings were evaluated as input to the model (MFCCs, LCCs, and HuBERT), and (2) the study of the influence of the
dataset size, which we varied from 10 minutes to 3.5 hours. Evaluation was performed on the test data using RMSE, median error, as well as Tract Variables, to which we added an additional measurement: the larynx height. The average RMSE obtained is 1.48\,mm, compared with the pixel size (1.62\,mm). These results confirm the feasibility of a complete vocal-tract inversion using RT-MRI data.
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Systems and Control
Alertness Optimization for Shift Workers Using a Physiology-based Mathematical Model
Zidi Tao, A. Agung Julius and John T Wen
Sleep is vital for maintaining cognitive function, facilitating metabolic waste removal, and supporting
memory consolidation. However, modern societal demands, particularly shift work, often disrupt natural sleep patterns. This can induce excessive sleepiness among shift workers in critical sectors such as healthcare and transportation and increase the risk of accidents. The primary contributors to this issue are misalignments of circadian rhythms and enforced sleep-wake schedules. Regulating circadian rhythms that are tied to alertness can be regarded as a control problem with control inputs in the form of light and sleep schedules. In this paper, we address the problem of optimizing alertness by optimizing light and sleep schedules to improve the cognitive performance of shift workers. A key tool in our approach is a mathematical model that relates the control input variables (sleep and lighting schedules) to the dynamics of the circadian clock and sleep. In the sleep and circadian modeling literature, the newer physiology-based model shows better accuracy in predicting the alertness of shift workers than the phenomenology-based model, but the dynamics of physiological-based model have differential equations with different time scales, which pose challenges in optimization. To overcome the challenge, we propose a hybrid version of the PR model by applying singular perturbation techniques to reduce the system to a non-stiff, differentiable hybrid system. This reformulation facilitates the application of the calculus of variation and the gradient descent method to find the optimal light and sleep schedules that maximize the subjective alertness of shift worker. Our approach is validated through numerical simulations, and the simulation results demonstrate improved alertness compared to other existing schedules.
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Audio and Speech Processing
Can Hierarchical Cross-Modal Fusion Predict Human Perception of AI Dubbed Content?
Ashwini Dasare, Nirmesh Shah, Ashishkumar Gudmalwar et al. (+1 more)
Evaluating AI generated dubbed content is inherently multi-dimensional, shaped by synchronization, intelligibility, speaker consistency, emotional
alignment, and semantic context. Human Mean Opinion Scores (MOS) remain the gold standard but are costly and impractical at scale. We present a hierarchical
multimodal architecture for perceptually meaningful dubbing evaluation, integrating complementary cues from audio,
video, and text. The model captures fine-grained features such as speaker identity, prosody, and content from audio, facial expressions and scene-level cues from
video and semantic context from text, which are progressively fused through intra and inter-modal layers. Lightweight LoRA adapters enable parameter-efficient fine-tuning across modalities. To overcome limited subjective labels, we derive proxy MOS by aggregating objective metrics with weights optimized via active learning. The proposed architecture was trained on 12k Hindi-English bidirectional dubbed clips, followed by fine-tuning with human MOS. Our approach achieves strong perceptual
alignment (PCC > 0.75), providing a scalable solution for automatic evaluation of AI-dubbed content.
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All authors Ashwini Dasare, Nirmesh Shah, Ashishkumar Gudmalwar, Pankaj Wasnik
Audio and Speech Processing
VAANI: Capturing the language landscape for an inclusive digital India
Sujith Pulikodan, Abhayjeet Singh, Agneedh Basu et al. (+17 more)
Voice based technologies have the potential to bridge digital accessibility gaps; however, existing
datasets fail to capture the linguistic and regional diversity of Indic
languages. We present Project VAANI, a large scale
multimodal dataset designed to represent India's linguistic landscape across 165 districts.
Speech data is collected using image based
prompts to elicit spontaneous responses, while images are curated through a separate pipeline covering diverse themes across regions. The
dataset undergoes a rigorous multi stage quality control process, combining automated and manual evaluation to ensure high audio quality and transcription accuracy. We release approximately 289K images, 31,255 hours of
speech, and 2,043 hours of transcribed audio spanning 105
languages from 28 states and 3 union territories. Many of these
languages are represented at this scale for the first time, making VAANI a foundational resource for inclusive
speech technology. The
dataset enables the development of robust,
multilingual, and
multimodal models, and supports research in
speech recognition,
language understanding, and cross-modal learning for underrepresented
languages.
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All authors Sujith Pulikodan, Abhayjeet Singh, Agneedh Basu, Nihar Desai, Pavan Kumar J, Pranav D Bhat, Raghu Dharmaraju, Ritika Gupta, Sathvik Udupa, Saurabh Kumar, Sumit Sharma, Visruth Sanka, Dinesh Tewari, Harsh Dhand, Amrita Kamat, Sukhwinder Singh, Shikhar Vashishth, Partha Talukdar, Raj Acharya, Prasanta Kumar Ghosh
Image and Video Processing
Learning a dynamic four-chamber shape model of the human heart for 95,695 UK Biobank participants
Qiang Ma, Qingjie Meng, Yicheng Wu et al. (+6 more)
The human heart is a sophisticated system composed of four cardiac chambers with distinct shapes, which function in a coordinated manner. Existing shape models of the heart mainly focus on the ventricular chambers and they are derived from relatively small
datasets. Here, we present a spatio-temporal (3D+t) statistical shape model of all four cardiac chambers, learnt from a large population of nearly 100,000 participants from the UK Biobank. A deep learning-based pipeline is developed to reconstruct 3D+t four-chamber meshes from the cardiac
magnetic resonance images of the UK Biobank imaging population. Based on the reconstructed meshes, a 3D+t statistical shape model is learnt to characterise the shape variations and motion patterns of the four cardiac chambers. We reveal the associations of the four-chamber shape model with demographics, anthropometrics, cardiovascular risk factors, and cardiac diseases. Compared to conventional image-derived phenotypes, we validate that the four-chamber shape-derived phenotypes significantly enhance the performance in downstream tasks, including cardiovascular disease classification and heart age prediction. Furthermore, we demonstrate the effectiveness of shape-derived phenotypes in novel applications such as heart shape
retrieval and heart re-identification from longitudinal data. To facilitate future research, we will release the learning-based mesh reconstruction pipeline, the four-chamber cardiac shape model, and return all derived four-chamber meshes to the UK Biobank.
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All authors Qiang Ma, Qingjie Meng, Yicheng Wu, Shuo Wang, Mengyun Qiao, Steven Niederer, Declan P. O'Regan, Paul M. Matthews, Wenjia Bai
Systems and Control · Cross-listed from cs.RO
Dynamic Lookahead Distance via Reinforcement Learning-Based Pure Pursuit for Autonomous Racing
Mohamed Elgouhary and Amr S. El-Wakeel
Pure Pursuit (PP) is a widely used path-tracking
algorithm in autonomous vehicles due to its simplicity and real-time performance. However, its effectiveness is sensitive to the choice of lookahead distance: shorter values improve cornering but can cause instability on straights, while longer values improve smoothness but reduce accuracy in curves. We propose a hybrid control framework that integrates Proximal Policy Optimization (PPO) with the classical Pure Pursuit controller to adjust the lookahead distance dynamically during racing. The PPO
agent maps vehicle speed and multi-horizon
curvature features to an online lookahead command. It is trained using Stable-Baselines3 in the F1TENTH Gym simulator with a KL penalty and learning-rate decay for stability, then deployed in a ROS2 environment to guide the controller. Experiments in simulation compare the proposed method against both fixed-lookahead Pure Pursuit and an adaptive Pure Pursuit baseline. Additional real-car experiments compare the learned controller against a fixed-lookahead Pure Pursuit controller. Results show that the learned policy improves lap-time performance and repeated lap completion on unseen tracks, while also transferring zero-shot to hardware. The learned controller adapts the lookahead by increasing it on straights and reducing it in curves, demonstrating effectiveness in augmenting a classical controller by online adaptation of a single interpretable parameter. On unseen tracks, the proposed method achieved 33.16 s on Montreal and 46.05 s on Yas Marina, while tolerating more aggressive speed-profile scaling than the baselines and achieving the best lap times among the tested settings. Initial real-car experiments further support sim-to-real transfer on a 1:10-scale autonomous racing platform
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Systems and Control
Fault-Tolerant MPC Control for Trajectory Tracking
David Laranjinho and Daniel Silvestre
An MPC controller uses a model of the dynamical system to plan an optimal control strategy for a finite horizon, which makes its performance intrinsically tied to the quality of the model. When faults occur, the compromised model will degrade the performance of the MPC with this impact being dependent on the designed cost function. In this paper, we aim to devise a strategy that combines active fault identification while driving the system towards the desired trajectory. The explored approaches make use of an exact formulation of the problem in terms of set-based propagation resorting to Constrained Convex Generators (CCGs) and a suboptimal version that resorts to the SVD decomposition to achieve the active fault isolation in order to adapt the model in runtime.
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Systems and Control · Cross-listed from cs.IT
Learning Where to Look: UCB-Driven Controlled Sensing for Quickest Change Detection
Yu-Han Huang, Argyrios Gerogiannis, Subhonmesh Bose et al. (+1 more)
We study the multichannel quickest change detection problem with bandit feedback and controlled sensing, in which an
agent sequentially selects one of the data streams to observe at each time-step and aims to detect an unknown change as quickly as possible while controlling false alarms. Assuming known pre- and post-change distributions and allowing an arbitrary subset of streams to be affected by the change, we propose two novel and computationally efficient detection procedures inspired by the Upper Confidence Bound (UCB) multi-armed bandit
algorithm. Our methods adaptively concentrate sensing on the most informative streams while preserving false-alarm guarantees. We show that both procedures achieve first-order asymptotic optimality in detection delay under standard false-alarm constraints. We also extend the UCB-driven controlled sensing approach to the setting where the pre- and post-change distributions are unknown, except for a mean-shift in at least one of the channels at the change-point. This setting is particularly relevant to the problem of learning in piecewise stationary environments. Finally, extensive simulations on synthetic
benchmarks show that our methods consistently outperform existing state-of-the-art approaches while offering substantial computational savings.
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All authors Yu-Han Huang, Argyrios Gerogiannis, Subhonmesh Bose, Venugopal V. Veeravalli
Systems and Control · Cross-listed from cs.GT
Coalition Formation with Limited Information Sharing for Local Energy Management
Luke Rickard, Paola Falugi and Eric C. Kerrigan
Distributed energy systems with prosumers require new methods for coordinating energy exchange among
agents. Coalitional control provides a framework in which
agents form groups to cooperatively reduce costs; however, existing bottom-up coalition-formation methods typically require full information sharing, raising
privacy concerns and imposing significant computational overhead. In this work, we propose a limited information coalition-formation
algorithm that requires only limited aggregate information exchange among
agents. By constructing an upper bound on the value of candidate coalitions, we eliminate the need to solve optimisation problems for each potential merge, significantly reducing computational complexity while limiting information exchange. We prove that the proposed method guarantees cost no greater than that of decentralised operation. Coalition strategies are optimised using a distributed approach based on the Alternating Direction Method of Multipliers (ADMM), further limiting information sharing within coalitions. We embed the framework within a model predictive control scheme and evaluate it on real-world data, demonstrating improved economic performance over decentralised control with substantially lower computational cost than full-information approaches.
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Signal Processing
Joint Energy Efficiency Optimization for Uplink Multiuser Movable Antenna-Based Wireless Systems Assisted by Movable-Element RIS
Ayda Nodel Hokmabadi, Mohamed Elhattab and Chadi Assi
This paper investigates energy efficiency (EE) optimization for an uplink multiuser system assisted by a movable-element reconfigurable intelligent surface (ME-RIS) and a base station equipped with movable
antennas (MA-BS). We jointly optimize the uplink postcoder vectors, user transmit powers, RIS phase shift, and the positions of both the BS
antennas and RIS elements to maximize the system EE. The resulting non-convex fractional problem is solved using an alternating optimization (AO) framework, where subproblems are handled via Dinkelbach's method combined with successive convex approximation (SCA). Simulation results show that the proposed scheme achieves significant EE gains over fixed-
antenna BS and fixed-element RIS
benchmarks.
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Systems and Control
Measuring Cross-Jurisdictional Transfer of Medical Device Risk Concepts with Explainable AI
Yu Han and Aaron Ceross
Medical device regulators in the United States(FDA), China (NMPA), and Europe (EU MDR) all use the
language of risk, but classify devices through structurally different mechanisms. Whether these apparently shared concepts carry transferable classificatory signal across jurisdictions remains unclear. We test this by reframing explainable AI as an empirical probe of cross-jurisdictional regulatory overlap. Using 141,942 device records, we derive seven EU MDR risk factors, including implantability, invasiveness, and duration of use, and evaluate their contribution across a three-by-three transfer
matrix. Under a symmetric extraction pipeline designed to remove jurisdiction-specific advantages, factor contribution is negligible in all jurisdictions, indicating that clean cross-jurisdictional signal is at most marginal. Under jurisdiction specific pipelines, a modest gain appears only in the EU MDR-to-NMPA direction, but sensitivity analyses show that this effect is weak, context-dependent, and partly confounded by extraction and representation choices. Reverse direction probes show strong asymmetry: FDA-derived factors do not transfer meaningfully in any direction, and NMPA-derived factors do not carry signal back to EU MDR. Zero-shot transfer further fails on EU MDR Class I, consistent with a mismatch between residual and positional class definitions. Overall, cross-jurisdictional transfer is sparse, asymmetric, and weak. Shared regulatory vocabulary does not, under this operationalisation, translate into strong portable classification logic. The findings challenge a common assumption in cross-jurisdictional regulatory AI and show how explainable AI can be used to measure, rather than assume, regulatory overlap.
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Systems and Control
Intelligent Radio Resource Slicing for 6G In-Body Subnetworks
Samira Abdelrahman and Hossam Farag
6G In-body Subnetworks (IBSs) represent a key enabler for supporting standalone eXtended Reality (XR) applications. IBSs are expected to operate as an underlay to existing cellular networks, giving rise to coexistence challenges when sharing
radio resources with other cellular users, such as enhanced Mobile Broadband (eMBB) users. Such resource allocation problem is highly dynamic and inherently non-convex due to heterogeneous service demands and fluctuating channel conditions. In this paper, we propose an intelligent
radio resource slicing strategy based on the Soft Actor-Critic (SAC) deep reinforcement learning
algorithm. The proposed SAC-based slicing method addresses the coexistence challenge between IBSs and eMBB users by optimizing a refined reward function that explicitly incorporates XR cross-modal delay
alignment to ensure immersive experience while preserving eMBB service guarantees. Extensive system-level simulations are performed under realistic network conditions and the results demonstrate that the proposed method can enhance user experience by 12-85% under different network densities compared to baseline methods while maintaining the target data rate for eMBB users.
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Image and Video Processing
MRI-to-CT synthesis using drifting models
Qing Lyu, Jianxu Wang, Jeremy Hudson et al. (+2 more)
Accurate MRI-to-CT synthesis could enable MR-only pelvic workflows by providing CT-like images with bone details while avoiding additional ionizing radiation. In this work, we investigate recently proposed drifting models for synthesizing pelvis CT images from MRI and
benchmark them against convolutional neural networks (UNet, VAE), a generative
adversarial network (WGAN-GP), a physics-inspired probabilistic model (PPFM), and
diffusion-based methods (FastDDPM, DDIM, DDPM). Experiments are performed on two complementary
datasets: Gold Atlas Male Pelvis and the SynthRAD2023 pelvis subset. Image fidelity and structural consistency are evaluated with SSIM, PSNR, and RMSE, complemented by qualitative assessment of anatomically critical regions such as cortical bone and pelvic soft-tissue interfaces. Across both
datasets, the proposed drifting model achieves high SSIM and PSNR and low RMSE, surpassing strong
diffusion baselines and conventional CNN-, VAE-, GAN-, and PPFM-based methods. Visual inspection shows sharper cortical bone edges, improved depiction of sacral and femoral head geometry, and reduced artifacts or over-smoothing, particularly at bone-air-soft tissue boundaries. Moreover, the drifting model attains these gains with one-step inference and inference times on the order of milliseconds, yielding a more favorable accuracy-efficiency trade-off than iterative
diffusion sampling while remaining competitive in image quality. These findings suggest that drifting models are a promising direction for fast, high-quality pelvic synthetic CT generation from MRI and warrant further investigation for downstream applications such as MRI-only radiotherapy planning and PET/MR attenuation correction.
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All authors Qing Lyu, Jianxu Wang, Jeremy Hudson, Ge Wang, Chirstopher T. Whitlow
Image and Video Processing
Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms
Muyang He, Hanzhong Guo, Junxiong Lin et al. (+1 more)
The rapid
evolution of
video generation has enabled models to simulate complex physical dynamics and long-horizon causalities, positioning them as potential world simulators. However, a critical gap still remains between the theoretical capacity for world simulation and the heavy computational costs of spatiotemporal modeling. To address this, we comprehensively and systematically review
video generation frameworks and techniques that consider efficiency as a crucial requirement for practical world modeling. We introduce a novel taxonomy in three dimensions: efficient modeling paradigms, efficient network architectures, and efficient inference
algorithms. We further show that bridging this efficiency gap directly empowers interactive applications such as autonomous driving,
embodied AI, and game simulation. Finally, we identify emerging research frontiers in efficient
video-based world modeling, arguing that efficiency is a fundamental prerequisite for evolving
video generators into general-purpose, real-time, and robust world simulators. A curated GitHub repository of the reviewed literature can be found at https://github.com/Isaachhh/Efficient-VWM-Survey.
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All authors Muyang He, Hanzhong Guo, Junxiong Lin, Yizhou Yu
Systems and Control
An Accurate and Fast Start-up Scheme for Power System Real-time Emergency Control
Songhao Yang, Zhiguo Hao, Baohui Zhang et al. (+1 more)
With the development of PMUs in power systems, the response-based real-time emergency control becomes a promising way to prevent power outages when power systems are subjected to large disturbances. The first step in the emergency control is to start up accurately and fast when needed. To this end, this paper proposes a well-qualified start-up scheme for the power system real-time emergency control. Three key technologies are proposed to ensure the effectiveness of the scheme. They are an instability index, a Critical Machines (CMs) identification
algorithm and a two-layer Single Machine Infinite Bus (SMIB) equivalence framework. The concave-convex area based instability index shows good accuracy and high reliability, which is used to identify the transient instability of the system. The CMs identification
algorithm can track the changes of CMs and form the proper SMIB system at each moment. The new two-layer SMIB equivalence framework, compared with conventional ones, can significantly reduce the communication burden and improve the computation efficiency. The simulations in two test power systems show that the scheme can identify the transient instability accurately and fast to restore the system to stability after the emergency control. Besides, the proposed method is robust to measurement errors, which enhances its practicality.
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All authors Songhao Yang, Zhiguo Hao, Baohui Zhang, Masahide Hojo
Systems and Control
A System-View Optimal Additional Active Power Control of Wind Turbines for Grid Frequency Support
Yubo Zhang, Zhiguo Hao, Songhao Yang et al. (+1 more)
Additional active power control (AAPC) of wind turbines (WTs) is essential to improve the transient frequency stability of low-inertia power systems. Most of the existing research has focused on imitating the frequency response of the synchronous generator (SG), known as virtual inertia control (VIC), but are such control laws optimal for the power systems? Inspired by this question, this paper proposes an optimal AAPC of WTs to maximize the frequency nadir post a major power deficit. By decoupling the WT response and the frequency dynamics, the optimal frequency trajectory is solved based on the trajectory model, and its universality is strictly proven. Then the optimal AAPC of WTs is constructed reversely based on the average system frequency (ASF) model with the optimal frequency trajectory as the desired control results. The proposed method can significantly improve the system frequency nadir. Meanwhile, the event insensitivity makes it can be deployed based on the on-line rolling update under a hypothetic disturbance, avoiding the heavy post-event computational burden. Finally, simulation results in a two-machine power system and the IEEE 39 bus power system verify the effectiveness of the optimal AAPC of WTs.
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All authors Yubo Zhang, Zhiguo Hao, Songhao Yang, Baohui Zhang
Signal Processing · Cross-listed from cs.CV
SVH-BD : Synthetic Vegetation Hyperspectral Benchmark Dataset for Emulation of Remote Sensing Images
Chedly Ben Azizi, Claire Guilloteau, Gilles Roussel et al. (+1 more)
This
dataset provides a large collection of 10,915 synthetic hyperspectral image cubes paired with pixel-level vegetation trait maps, designed to support research in radiative transfer emulation, vegetation trait
retrieval, and uncertainty quantification. Each hyperspectral cube contains 211 bands spanning 400–2500 nm at 10 nm resolution and a fixed spatial layout of 64 \times 64 pixels, offering continuous simulated surface reflectance spectra suitable for emulator development and machine-learning tasks requiring high spectral detail. Vegetation traits were derived by inverting Sentinel-2 Level-2A surface reflectance using a PROSAIL-based lookup-table approach, followed by forward PROSAIL simulations to generate hyperspectral reflectance under physically consistent canopy and illumination conditions. The
dataset covers four ecologically diverse regions – East Africa, Northern France, Eastern India, and Southern Spain – and includes 5th and 95th percentile uncertainty maps as well as Sentinel-2 scene classification layers. This resource enables benchmarking of inversion methods, development of fast radiative transfer emulators, and studies of spectral–biophysical relationships under controlled yet realistic environmental variability.
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All authors Chedly Ben Azizi, Claire Guilloteau, Gilles Roussel, Matthieu Puigt
Systems and Control · Cross-listed from cs.IT
Age of Incorrect Information for Generic Discrete-Time Markov Sources
Konstantinos Bountrogiannis, Anthony Ephremides, Panagiotis Tsakalides et al. (+1 more)
This work introduces a framework for analyzing the Age of Incorrect Information (AoII) in a real-time monitoring system with a generic discrete-time Markov source. We study a noisy communication system employing a hybrid automatic repeat request (HARQ) protocol, subject to a transmission rate constraint. The optimization problem is formulated as a constrained Markov decision process (CMDP), and it is shown that there exists an optimal policy that is a randomized mixture of two stationary policies. To overcome the intractability of computing the optimal stationary policies, we develop a multiple-threshold policy class where thresholds depend on the source, the receiver, and the packet count. By establishing a Markov renewal structure induced by threshold policies, we derive closed-form expressions for the long-term average AoII and transmission rate. The proposed policy is constructed via a relative value iteration
algorithm that leverages the threshold structure to skip computations, combined with a bisection search to satisfy the rate constraint. To accommodate scenarios requiring lower computational complexity, we adapt the same technique to produce a simpler single-threshold policy that trades optimality for efficiency. Numerical experiments exhibit that both thresholdbased policies outperform periodic scheduling, with the multiplethreshold approach matching the performance of the globally optimal policy.
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All authors Konstantinos Bountrogiannis, Anthony Ephremides, Panagiotis Tsakalides, George Tzagkarakis
Systems and Control
Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Control
Ján Boldocký, Cary Faulkner, Elad Michael et al. (+3 more)
We present a computationally tractable framework for real-time predictive control of multi-chiller plants that involve both discrete and continuous control decisions coupled through nonlinear dynamics, resulting in a mixed-integer optimal control problem. To address this challenge, we extend Differentiable Predictive Control (DPC) – a self-supervised, model-based learning methodology for approximately solving parametric optimal control problems – to accommodate mixed-integer control policies. We
benchmark the proposed framework against a state-of-the-art Model Predictive Control (MPC) solver and a fast heuristic Rule-Based Controller (RBC). Simulation results demonstrate that our approach achieves significant energy savings over the RBC while maintaining orders-of-magnitude faster computation times than MPC, offering a scalable and practical alternative to conventional combinatorial mixed-integer control formulations.
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All authors Ján Boldocký, Cary Faulkner, Elad Michael, Martin Gulan, Aaron Tuor, Ján Drgoňa
Signal Processing
Integrated sensing and communications in the 3GPP New Radio: sensing limits
Santiago Fernández, Javier Giménez, Mari Carmen Aguayo-Torres et al. (+1 more)
Integrated Sensing and Communications (
ISAC) is regarded as a key element of the beyond-fifth-generation (5G) and sixth-generation (6G) systems, raising the question of whether current 5G New
Radio (NR) signal structures can meet the sensing accuracy requirements specified by the Third Generation Partnership Project (3GPP). This paper addresses this issue by analyzing the fundamental limits of range and velocity estimation through the Cramér-Rao lower bound (CRLB) for a monostatic unmanned aerial vehicle (
UAV) sensing use case currently under consideration in the 3GPP standardization process. The study focuses on standardized signals and also evaluates the potential performance gains achievable with reference signals specifically designed for sensing purposes. The compact CRLB expressions derived in this work highlight the fundamental trade-offs between estimation accuracy and system parameters. The results further indicate that information from multiple slots must be exploited in the estimation process to attain the performance targets defined by the 3GPP. As a result, the 5G NR positioning reference signal (PRS), whose patterns may be suboptimal for velocity estimation when using single-slot resources, becomes suitable when multislot estimation is employed. Finally, we propose a two-step iterative range and radial-velocity
estimator that attains the CRLB over a significantly wider range of distances than conventional maximum-likelihood (ML)
estimators, for which the well-known threshold effect severely limits the distance range over which the accuracy requirements imposed by the 3GPP are satisfied.
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All authors Santiago Fernández, Javier Giménez, Mari Carmen Aguayo-Torres, José A. Cortés
Systems and Control · Cross-listed from quant-ph
Compact Continuous-Variable Quantum Key Distribution System Employing Monolithically Integrated Silicon Photonic Transceiver
Denis Fatkhiev, João dos Reis Frazão, Alireza H. Derkani et al. (+4 more)
We demonstrate the first CV-QKD system featuring a custom-designed monolithic silicon photonic dual-polarisation transceiver. Leveraging PS-64-QAM, we achieved 1.9 Mbit/s secret key rate across 25 km of standard single-mode fibre, highlighting the potential of electronic-photonic integration for practical QKD.
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All authors Denis Fatkhiev, João dos Reis Frazão, Alireza H. Derkani, Kadir Gümüş, Menno van den Hout, Aaron Albores-Mejia, Chigo Okonkwo
Signal Processing
Toward Distributed User Scheduling and Coordinated Beamforming in Multi-Cell mmWave Networks: A Sensing-Assisted Framework
Tenghao Cai, Lei Li, Shutao Zhang et al. (+1 more)
Providing guaranteed quality of service for
cell-edge users remains a longstanding challenge in wireless networks. While coordinated interference management was proposed decades ago, its potential has been limited by computational complexity and backhaul resource constraints. Distributed user scheduling and coordinated beamforming (D-USCB) offers a scalable solution but faces practical challenges in acquiring inter-
cell channel state information (CSI), as base stations (BSs) are often restricted to signal strength measurements, and high-dimensional CSI exchange incurs substantial overhead. Inspired by integrated sensing and communication (
ISAC), this paper proposes a sensing-assisted D-USCB (SD-USCB) framework to maximize the network throughput of multi-
cell mmWave networks. Firstly, the framework leverages channel knowledge maps (CKMs) that map user locations to CSI estimates, where user locations are proactively sensed via
ISAC echoes. Secondly, we employ a signal-to-average-leakage-plus-interference-plus-noise ratio (SALINR) metric for distributed
ISAC beamforming optimization, in which BSs simultaneously communicate with users and sense their locations. These two components jointly enable distributed coordinated transmission with only user location information exchanged among BSs, thereby substantially reducing backhaul overhead. In addition, we devise efficient distributed user scheduling and
ISAC beamforming
algorithms to jointly optimize communication and sensing performance. Extensive numerical results demonstrate significant improvements in network throughput, validating the efficacy of the proposed framework.
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All authors Tenghao Cai, Lei Li, Shutao Zhang, Tsung-Hui Chang
Systems and Control
Competitor-aware Race Management for Electric Endurance Racing
Wytze de Vries, Erik van den Eshof, Jorn van Kampen et al. (+1 more)
Electric endurance racing is characterized by severe energy constraints and strong aerodynamic interactions. Determining race-winning policies therefore becomes a fundamentally multi-
agent, game-theoretic problem. These policies must jointly govern low-level driver inputs as well as high-level strategic decisions, including energy management and charging. This paper proposes a bi-level framework for competitor-aware race management that combines game-theoretic optimal control with reinforcement learning. At the lower level, a multi-
agent game-theoretic optimal control problem is solved to capture aerodynamic effects and asymmetric collision-avoidance constraints inspired by motorsport rules. Using this single-lap problem as the environment, reinforcement learning
agents are trained to allocate battery energy and schedule pit stops over an entire race. The framework is demonstrated in a two-
agent, 45-lap simulated race. The results show that effective exploitation of aerodynamic interactions is decisive for race outcome, with strategies that prioritize finishing position differing fundamentally from single-
agent, minimum-time approaches.
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All authors Wytze de Vries, Erik van den Eshof, Jorn van Kampen, Mauro Salazar
Signal Processing
Distributed User Scheduling in Multi-Cell MIMO O-RAN with QoS Constraints
Tenghao Cai, Lei Li and Tsung-Hui Chang
Distributed scheduling is essential for open
radio access network (O-RAN) employing advanced physical-layer techniques such as multi-user
MIMO (MU-
MIMO), carrier aggregation (CA), and joint transmission (JT). This work investigates the multi-component-carrier (multi-CC) resource block group (RBG) scheduling in MU-
MIMO O-RAN with both JT and non-JT users. We formulate a scheduling optimization problem to maximize throughput subject to user-specific quality of service (QoS) requirements while ensuring consistent allocations across cooperating O-RAN
radio units (O-RUs) required by JT transmission. The strong variable coupling, non-convexity, and combinatorial complexity make the problem highly challenging. To tackle this, we extend the eigen-based zero-forcing transceiver design to JT users and leverage massive
MIMO asymptotic properties to derive a tractable, separable rate approximation. Building on this, we develop two solutions: a centralized block coordinate descent
benchmark and a distributed scheduler aligned with the O-RAN architecture. The proposed distributed scheme achieves near-centralized performance with only one round of lightweight coordination among
cells, significantly reducing complexity and delay. Extensive simulations validate that our distributed scheduler achieves high scalability, fast convergence, and better QoS satisfaction rate in large-scale MU-
MIMO networks.
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Signal Processing
Multimodal-NF: A Wireless Dataset for Near-Field Low-Altitude Sensing and Communications
Mengyuan Li, Qianfan Lu, Jiachen Tian et al. (+5 more)
Environment-aware 6G wireless networks demand the deep integration of
multimodal and wireless data. However, most existing
datasets are confined to 2D terrestrial far-field scenarios, lacking the 3D spatial context and near-field characteristics crucial for low-altitude extremely large-scale multiple-input multiple-output (XL-
MIMO) systems. To bridge this gap, this letter introduces
Multimodal-NF, a large-scale
dataset and specialized generation framework. Operating in the upper midband, it synchronizes high-fidelity near-field channel state information (CSI) and precise wireless labels (e.g., Top-5 beam indices, LoS/NLoS) with comprehensive sensory modalities (RGB images,
LiDAR point clouds, and GPS). Crucially, these
multimodal priors provide spatial semantics that help reduce the near-field search space and thereby lower the overhead of wireless sensing and communication tasks. Finally, we validate the
dataset through representative case studies, demonstrating its utility and effectiveness. The open-source generator and
dataset are available at https://lmyxxn.github.io/6GXLMIMODatasets/.
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All authors Mengyuan Li, Qianfan Lu, Jiachen Tian, Hongjun Hu, Yu Han, Xiao Li, Chao-Kai Wen, Shi Jin
Signal Processing
Clustered Movable Pinching Antennas: Realizing Beamforming Gains and Target Diversity in ISAC Systems with Look-Angle-Dependent RCS
Ata Khalili, Brikena Kaziu, Vasilis K. Papanikolaou et al. (+1 more)
We investigate a novel integrated sensing and communication (
ISAC) system enabled by pinching
antennas (PAs), which are dynamically activated along a dielectric waveguide. Unlike prior designs, the PAs are organized into multiple clusters of movable
antennas. The movement of the
antennas within each cluster enables transmit beamforming, while the spatial separation of different clusters allows the system to illuminate the target from multiple angular perspectives.
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All authors Ata Khalili, Brikena Kaziu, Vasilis K. Papanikolaou, Robert Schober
Signal Processing
Spectral Segmented Linear Regression for Coarse Carrier Frequency Offset Estimation in Optical LEO Satellite Communications
I. P. Vieira, G. V. Serra, R. A. Colares et al. (+1 more)
Carrier frequency offset estimation (CFOE) is a critical stage in modern coherent optical communication systems. Although conventional all-digital techniques perform reliably in typical fiber-optic communication links, CFOE can become a major bottleneck in low-symbol-rate scenarios with large carrier frequency offsets (CFOs) approaching the signal bandwidth and severe additive noise levels. These conditions are particularly prevalent in links between optical ground stations (OGSs) and low Earth orbit (LEO) satellites, where Doppler-induced frequency shifts of several gigahertz and atmospheric attenuation can significantly degrade CFOE performance and can render conventional methods ineffective. In this paper, we propose a robust non-data-aided (NDA) scheme designed for wide-range CFOE. The proposed coarse CFOE (C-CFOE)
algorithm partially compensates the CFO, enabling the operation of a subsequent fine CFOE stage. By applying low-complexity operations to the spectrum of the received signal, we recast the frequency estimation task as a segmented linear regression (SLR) problem. Numerical simulations in stress-test scenarios involving large CFOs, low SNR, and low symbol rates show that the proposed approach achieves good estimation accuracy and robust convergence. Offline experimental validation further confirms the practical feasibility of the method.
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All authors I. P. Vieira, G. V. Serra, R. A. Colares, D. A. A. Mello
Signal Processing · Cross-listed from cs.IT
Secret Key Rate Analysis of RIS-Assisted THz MIMO CV-QKD Systems under Access-Constrained Eavesdropping
Sushil Kumar, Soumya P. Dash and George C. Alexandropoulos
A reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (
MIMO) continuous-variable
quantum key distribution (CV-QKD) system operating at terahertz (THz) frequencies, in which a transmitter, Alice, encodes secret keys using Gaussian-modulated coherent states and communicates them to a legitimate receiver, Bob, is considered in this paper. The composite wireless channel, comprising the direct Alice-Bob link and the RIS-assisted reflected link, is modeled as a passive linear Gaussian
quantum channel, enabling a
unitary dilation that preserves the canonical commutation relations. The security of the considered system is investigated under collective Gaussian entangling attacks by introducing a practical access-constrained eavesdropping model, in which an eavesdropper, Eve, is assumed to access only the environmental modes associated with physically accessible propagation segments. A unified analytical framework is developed to derive the achievable secret key rate (SKR) across all single-segment, pairwise-segment, and full-segment access scenarios, assuming homodyne detection and reverse reconciliation at Bob. Furthermore, an optimization framework is developed to determine the optimal RIS phase configuration
matrix and transmitter/receiver beam-splitter parameters that maximize the SKR performance. The resulting optimization problem is efficiently solved using particle swarm optimization. Numerical results are presented to demonstrate the system's performance with respect to various free parameters. It is showcased that the secrecy performance strongly depends on Eve's accessible propagation segments, with the Alice-RIS channel constituting the most security-critical segment.
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Systems and Control · Cross-listed from cs.RO
Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion
Wenqi Cai, Kyriakos G. Vamvoudakis, Sébastien Gros et al. (+1 more)
In this paper, we propose a cost-matching approach for optimal
humanoid locomotion within a Model Predictive Control (MPC)-based Reinforcement Learning (RL) framework. A parameterized MPC formulation with centroidal dynamics is trained to approximate the action-value function obtained from high-fidelity closed-loop data. Specifically, the MPC cost-to-go is evaluated along recorded state-action trajectories, and the parameters are updated to minimize the discrepancy between MPC-predicted values and measured returns. This formulation enables efficient gradient-based learning while avoiding the computational burden of repeatedly solving the MPC problem during training. The proposed method is validated in simulation using a commercial
humanoid platform. Results demonstrate improved locomotion performance and robustness to model mismatch and external disturbances compared with manually tuned baselines.
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All authors Wenqi Cai, Kyriakos G. Vamvoudakis, Sébastien Gros, Anthony Tzes