Systems and Control
Safety-Aware Performance Boosting for Constrained Nonlinear Systems
Danilo Saccani, Haoming Shen, Luca Furieri et al. (+1 more)
We study a control architecture for nonlinear constrained systems that integrates a performance-boosting (PB) controller with a scheduled Predictive Safety Filter (PSF). The PSF acts as a pre-stabilizing base controller that enforces state and input constraints. The PB controller, parameterized as a causal operator, influences the PSF in two ways: it proposes a performance input to be filtered, and it provides a scheduling signal to adjust the filter's Lyapunov-decrease rate. We prove two main results: (i) Stability by design: any controller adhering to this parametrization maintains closed-loop stability of the pre-stabilized system and inherits PSF safety. (ii) Trajectory-set expansion: the architecture strictly expands the set of safe, stable trajectories achievable by controllers combined with conventional PSFs, which rely on a pre-defined Lyapunov decrease rate to ensure stability. This scheduling allows the PB controller to safely execute complex behaviors, such as transient detours, that are provably unattainable by standard PSF formulations. We demonstrate this expanded capability on a constrained inverted pendulum task with a moving obstacle.
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All authors Danilo Saccani, Haoming Shen, Luca Furieri, Giancarlo Ferrari-Trecate
Systems and Control · Cross-listed from cs.RO
NavTrust: Benchmarking Trustworthiness for Embodied Navigation
Huaide Jiang, Yash Chaudhary, Yuping Wang et al. (+8 more)
There are two major categories of
embodied navigation:
Vision-
Language Navigation (VLN), where
agents navigate by following natural
language instructions; and Object-Goal
Navigation (OGN), where
agents navigate to a specified target object. However, existing work primarily evaluates model performance under nominal conditions, overlooking the potential corruptions that arise in real-world settings. To address this gap, we present NavTrust, a unified
benchmark that systematically corrupts input modalities, including RGB, depth, and instructions, in realistic scenarios and evaluates their impact on
navigation performance. To our best knowledge, NavTrust is the first
benchmark that exposes
embodied navigation agents to diverse RGB-Depth corruptions and instruction variations in a unified framework. Our extensive evaluation of seven state-of-the-art approaches reveals substantial performance degradation under realistic corruptions, which highlights critical robustness gaps and provides a roadmap toward more trustworthy
embodied navigation systems. Furthermore, we systematically evaluate four distinct mitigation strategies to enhance robustness against RGB-Depth and instruction corruptions. Our base models include Uni-NaVid and ETPNav. We deployed them on a real mobile
robot and observed improved robustness to corruptions. The project website is: https://navtrust.github.io/.
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All authors Huaide Jiang, Yash Chaudhary, Yuping Wang, Zehao Wang, Raghav Sharma, Manan Mehta, Yang Zhou, Lichao Sun, Zhiwen Fan, Zhengzhong Tu, Jiachen Li
Signal Processing
Outage Probability Analysis of NOMA Enabled Hierarchical UAV Networks with Non-Linear Energy Harvesting
Faicel Khennoufa, Khelil Abdellatif, Metin Ozturk et al. (+2 more)
Uncrewed aerial vehicles (
UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil domains. Although multi-
UAV systems enhance connectivity for IoT networks more than single-
UAV systems, energy-efficient communication systems and the integration of energy harvesting (EH) are crucial for their widespread adoption and effectiveness. In this regard, this paper proposes a hierarchical ad hoc
UAV network with non-linear EH and non-orthogonal multiple access (NOMA) to enhance both energy and cost efficiency. The proposed system consists of two
UAV layers: a cluster head
UAV (CHU), which acts as the source, and cluster member
UAVs (CMUs), which serve as relays and are capable of harvesting energy from a terrestrial power beacon. For the considered IoT network architecture, the outage probability expressions of ground Internet of things (IoT) devices, each CMU, and the overall outage probability of the proposed system are derived over Nakagami-m fading channels with practical constraints such as hardware impairments and non-linear EH. We compare the proposed system against a non EH system, and our findings indicate that the proposed system outperforms the
benchmark in terms of outage probability.
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All authors Faicel Khennoufa, Khelil Abdellatif, Metin Ozturk, Halim Yanikomeroglu, Safwan Alfattani
Audio and Speech Processing
How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation
Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang et al. (+13 more)
Large
language models (LLMs) have been widely used as knowledge backbones of Large Audio
Language Models (LALMs), yet how much auditory knowledge they encode through text-only pre-training and how this affects downstream performance remains unclear. We study this gap by comparing different LLMs under two text-only and one audio-grounded setting: (1) direct probing on AKB-2000, a curated
benchmark testing the breadth and depth of auditory knowledge; (2) cascade evaluation, where LLMs reason over text descriptions from an audio captioner; and (3) audio-grounded evaluation, where each LLM is fine-tuned into a Large Audio
Language Model (LALM) with an audio encoder. Our findings reveal that auditory knowledge varies substantially across families, and text-only results are strongly correlated with audio performance. Our work provides empirical grounding for a comprehensive understanding of LLMs in audio research.
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All authors Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang, Zhehuai Chen, Sung-Feng Huang, Chih-Kai Yang, Yi-Cheng Lin, Chi-Yuan Hsiao, Wenze Ren, En-Pei Hu, Yu-Han Huang, An-Yu Cheng, Cheng-Han Chiang, Yu Tsao, Yu-Chiang Frank Wang, Hung-yi Lee
Systems and Control
Markov Potential Game and Multi-Agent Reinforcement Learning for Autonomous Driving
Huiwen Yan and Mushuang Liu
Autonomous driving (AD) requires safe and reliable decision-making among interacting
agents, e.g., vehicles, bicycles, and pedestrians. Multi-
agent reinforcement learning (MARL) modeled by Markov games (MGs) provides a suitable framework to characterize such
agents' interactions during decision-making. Nash
equilibria (NEs) are often the desired solution in an MG. However, it is typically challenging to compute an NE in general-sum games, unless the game is a Markov potential game (MPG), which ensures the NE attainability under a few learning
algorithms such as gradient play. However, it has been an open question how to construct an MPG and whether these construction rules are suitable for AD applications. In this paper, we provide sufficient conditions under which an MG is an MPG and show that these conditions can accommodate general driving objectives for autonomous vehicles (AVs) using highway forced merge scenarios as illustrative examples. A parameter-sharing neural network (NN) structure is designed to enable decentralized policy execution. The trained driving policy from MPGs is evaluated in both simulated and naturalistic traffic
datasets. Comparative studies with single-
agent RL and with human drivers whose behaviors are recorded in the traffic
datasets are reported, respectively.
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Image and Video Processing
GenMFSR: Generative Multi-Frame Image Restoration and Super-Resolution
Harshana Weligampola, Joshua Peter Ebenezer, Weidi Liu et al. (+4 more)
Camera pipelines receive raw Bayer-format frames that need to be denoised, demosaiced, and often super-resolved. Multiple frames are captured to utilize natural hand tremors and enhance resolution. Multi-frame super-resolution is therefore a fundamental problem in camera pipelines. Existing
adversarial methods are constrained by the quality of ground truth. We propose GenMFSR, the first Generative Multi-Frame Raw-to-RGB Super Resolution pipeline, that incorporates image priors from foundation models to obtain sub-pixel information for camera ISP applications. GenMFSR can align multiple raw frames, unlike existing single-frame super-resolution methods, and we propose a loss term that restricts generation to high-frequency regions in the raw domain, thus preventing low-frequency artifacts.
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All authors Harshana Weligampola, Joshua Peter Ebenezer, Weidi Liu, Abhinau K. Venkataramanan, Sreenithy Chandran, Seok-Jun Lee, Hamid Rahim Sheikh
Audio and Speech Processing · Cross-listed from cs.SD
Few-shot Acoustic Synthesis with Multimodal Flow Matching
Amandine Brunetto
Generating audio that is acoustically consistent with a scene is essential for immersive virtual environments. Recent neural acoustic field methods enable spatially continuous sound rendering but remain scene-specific, requiring dense audio measurements and costly training for each environment. Few-shot approaches improve scalability across rooms but still rely on multiple recordings and, being deterministic, fail to capture the inherent uncertainty of scene acoustics under sparse context. We introduce flow-matching acoustic generation (FLAC), a probabilistic method for few-shot acoustic synthesis that models the distribution of plausible room impulse responses (RIRs) given minimal scene context. FLAC leverages a
diffusion transformer trained with a flow-matching objective to generate RIRs at arbitrary positions in novel scenes, conditioned on spatial,
geometric, and acoustic cues. FLAC outperforms state-of-the-art eight-shot baselines with one-shot on both the AcousticRooms and Hearing Anything Anywhere
datasets. To complement standard perceptual metrics, we further introduce AGREE, a joint acoustic-geometry
embedding, enabling geometry-consistent evaluation of generated RIRs through
retrieval and distributional metrics. This work is the first to apply generative flow matching to explicit RIR synthesis, establishing a new direction for robust and data-efficient acoustic synthesis.
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Signal Processing
Channel Estimation via Tensor Decomposition for Dynamic Metasurface Antennas with Known Mutual Coupling: Algorithms and Experiments
Jean Tapie, Bruno Sokal, André L. F. de Almeida et al. (+1 more)
Dynamic metasurface
antennas (DMAs) are an emerging hybrid-
MIMO technology distinguished by an ultrathin form factor, low cost, and low power consumption. In real-world DMA prototypes, mutual coupling (MC) between meta-elements is generally non-negligible; some architectures even deliberately exploit strong MC to enhance wave-domain flexibility. In this paper, we address channel estimation (CE) for DMAs with known MC by formulating it as a tensor-decomposition problem. We develop a generalized block Tucker alternating least squares (BTALS)
algorithm, together with specialized variants for cases with known direct and/or feed channel. We also develop a reciprocity-aware bilinear factorization method for the case with known direct channel. We experimentally validate our
algorithms using an 18 GHz DMA prototype whose 7 feeds and 96 meta-elements are strongly coupled via a chaotic cavity. Our general BTALS
algorithm reaches an accuracy of 43.1 dB, only 0.3 dB below the upper bound imposed by experimental noise. All proposed
algorithms generally outperform the prior-art reference scheme thanks to the superior noise rejection enabled by the tensor-based framework. We further study the minimum number of required measurements as a function of the number of feeds and demonstrate the importance of MC awareness by comparison with an MC-unaware
benchmark. Finally, we apply BTALS to a second setup enabling the prediction of the DMA's full dual-polarization 3D radiation diagram. We also measure the latter for DMA configurations optimized for channel-gain enhancement based on the estimated channels. Altogether, our work establishes the practical relevance of MC-aware tensor methods; beyond DMAs, it applies to all wireless systems with wave-domain programmability enabled by tunable lumped elements.
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All authors Jean Tapie, Bruno Sokal, André L. F. de Almeida, Philipp del Hougne
Signal Processing
Mobile Radio Networks and Weather Radars Dualism: Rainfall Measurement Revolution in Densely Populated Areas
Davide Tornielli Bellini, Mario Montopoli, Dario Tagliaferri et al. (+5 more)
This study demonstrates, for the first time, how a network of cellular base stations (BSs) - the infrastructure of mobile
radio networks - can be used as a distributed opportunistic radar for rainfall remote sensing. By adapting signal-processing techniques traditionally employed in Doppler weather radar systems, we demonstrate that BS signals can be used to retrieve typical weather radar products, including reflectivity factor, mean Doppler velocity, and spectral width. Due to the high spatial density of BS infrastructure in urban environments, combined with intrinsic technical features such as electronically steerable
antenna arrays and wide receiver bandwidths, the proposed approach achieves unprecedented spatial and temporal resolutions, on the order of a few meters and several tens of seconds, respectively. Despite limitations related to low transmitted power, limited
antenna gain, and other system constraints, a major challenge arises from ground clutter contamination, which is exacerbated by the nearly horizontal orientation of BS
antenna beams. This work provides a thorough assessment of clutter impact and demonstrates that, through appropriate processing, the resulting clutter-filtered radar moments reach a satisfactory level of quality when compared with raw observations and with measurements from independent BSs with overlapped field-of-views. The findings highlight a transformative opportunity for urban hydrometeorology: leveraging existing telecommunications infrastructure to obtain rainfall information with a level of spatial granularity and temporal immediacy like never before.
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All authors Davide Tornielli Bellini, Mario Montopoli, Dario Tagliaferri, Luca Baldini, Elisa Adirosi, Sergi Duque, Laura Resteghini, Umberto Spagnolini
Systems and Control
Tutorial: Grid-Following Inverter for Electrical Power Grid
Muhammad Hamza Ali and Amritanshu Pandey
The growing use of inverter-based resources in modern power systems has made grid-following inverters a central topic in power-system modeling, control, and simulation. Despite their widespread deployment, introductory material that explains grid-following inverter operation from first principles and connects control design to time-domain simulation remains limited. To address this need, this tutorial presents a
circuit-theoretic introduction to the modeling and simulation of a grid- following inverter connected to an electrical power grid. We describe the inverter synchronization with the grid (PLL), power control, and current control structure and show how these elements can be represented within an electromagnetic transient (EMT) simulation framework using companion model-based formulations similar to those used in
circuit simulators such as SPICE and Cadence. In this tutorial, we use the grid-following inverter as the primary example to illustrate how its governing equations, control loops, and network interface can be formulated and simulated from first principles. By the end of the document, readers should gain a clear introductory understanding of how to model and simulate a grid-following inverter in an EMT platform.
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Systems and Control
Exact-Time Safety Recovery using Time-Varying Control Barrier Functions with Optimal Barrier Tracking
Yingqing Chen, Christos G. Cassandras, Wei Xiao et al. (+1 more)
This paper is motivated by controllers developed for autonomous vehicles which occasionally result into conditions where safety is no longer guaranteed. We develop an exact-time safety recovery framework for any control-affine nonlinear system when its state is outside a safe region using time-varying Control Barrier Functions (CBFs) with optimal barrier tracking. Unlike conventional formulations that provide only conservative upper bounds on recovery time convergence, the proposed approach guarantees recovery to the safe set at a prescribed time. The key mechanism is an active barrier tracking condition that forces the barrier function to follow exactly a designer-specified recovery trajectory. This transforms safety recovery into a trajectory design problem. The recovery trajectory is parameterized and optimized to achieve optimal performance while preserving feasibility under input constraints, avoiding the aggressive corrective actions typically induced by conventional finite-time formulations. The safety recovery framework is applied to the roundabout traffic coordination problem for Connected and Automated Vehicles (CAVs), where any initially violated safe merging constraint is replaced by an exact-time recovery barrier constraint to ensure safety guarantee restoration before CAV conflict points are reached. Simulation results demonstrate improved feasibility and performance.
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All authors Yingqing Chen, Christos G. Cassandras, Wei Xiao, Anni Li
Systems and Control
Assessment of Analog Time Multiplexing in SDM Digital to Analog Converters
Alfredo P. Vega-Leal and Jose L. Mora
Analog multiplexing for sigma delta modulated Digital to Analog Converters has been recently proposed as a means of achieving robustness. This preprint analyses said scheme via simulations. The main limitation introduced by the proposed architecture comes from mismatch in the DACs gain, which can drastically impact performances. A new technique of dynamic elements matching is proposed here to overcome this problem.
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Signal Processing · Cross-listed from cs.LG
Communication-Efficient and Robust Multi-Modal Federated Learning via Latent-Space Consensus
Mohamed Badi, Chaouki Ben Issaid and Mehdi Bennis
Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, but applying FL to multi-modal settings introduces significant challenges. Clients typically possess heterogeneous modalities and model architectures, making it difficult to align feature spaces efficiently while preserving
privacy and minimizing communication costs. To address this, we introduce CoMFed, a Communication-Efficient Multi-Modal
Federated Learning framework that uses learnable projection
matrices to generate compressed latent representations. A latent-space regularizer aligns these representations across clients, improving cross-modal consistency and robustness to outliers. Experiments on human activity recognition
benchmarks show that CoMFed achieves competitive accuracy with minimal overhead.
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Signal Processing
Radar Detection through Rectified Flow Matching
P. Meena, Y. A. Rouzoumka, J. Pinsolle et al. (+3 more)
Radar target detection in the presence of a mixture of non-Gaussian clutter and white thermal noise is a challenging problem. This paper proposes a Rectified Flow Matching-based method for radar detection, termed D-RFM. Unlike existing detectors, D-RFM learns a mapping from a standard Gaussian distribution to radar observations by capturing the underlying velocity field. Detection is then performed by inverse mapping test samples into the latent Gaussian space using the learned velocity field, with targets identified as deviations from the learned distribution. Experimental results demonstrate the efficacy of the proposed method under both Gaussian and non-Gaussian clutter plus additive white Gaussian noise, highlighting its accuracy, robustness, and computational efficiency.
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All authors P. Meena, Y. A. Rouzoumka, J. Pinsolle, C. Ren, M. N. El Korso, J.-P. Ovarlez
Systems and Control
Heart Artifact Removal in Electrohysterography Measurements Using Algebraic Differentiators
Amine Othmane, Maria Camila Bustos Vivas, Johannes Steuer et al. (+1 more)
Electrohysterography (EHG) enables non-invasive monitoring of uterine contractions but can be contaminated by electrocardiogram (ECG) artifacts. This work presents an ECG removal method using algebraic differentiators, a control-theoretic tool for model-free derivative estimation, that preserves signal shape outside the detected cardiac pulse locations. The differentiator parameters are designed to simultaneously suppress slow physiological artifacts and powerline interference. Cross-channel clustering distinguishes cardiac pulses from localized artifacts, enabling accurate pulse subtraction without auxiliary ECG references. Implemented as a causal FIR filter, the method is validated as a proof of concept on multichannel EHG recordings from one female and one male healthy volunteer and compared to the template subtraction method.
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All authors Amine Othmane, Maria Camila Bustos Vivas, Johannes Steuer, Jana Hutter
Systems and Control · Cross-listed from cs.CE
On the Minimum Number of Control Laws for Nonlinear Systems with Input-Output Linearisation Singularities
Nikolaos D. Tantaroudas
We study how many distinct feedback-linearising control laws are needed for a single-input single-output nonlinear system whose exact input-output linearising law is singular on a nonempty subset of the state space. The question is posed as a coverage problem: a family of exact and approximate (Hauser-Sastry-Kokotovic) linearising laws covers a region if every state of the region lies in the validity domain of at least one law. Using transversality arguments we prove that, for $p$ laws whose singular hypersurfaces are in general position, the uncovered set is a finite union of embedded submanifolds of codimension exactly $p$. Hence $n+1$ laws always suffice for complete coverage of an $n$-dimensional state space, two laws already reduce the uncovered set to a thin set that generic trajectories never meet, and complete coverage with fewer than $n+1$ laws depends on specific intersections rather than on the number of components of the singularity
manifold. The theory is applied exhaustively to the ball-and-beam system, whose exact linearising law is singular on the union of two transverse hypersurfaces $\{r=0\}\cup\{\dot\theta=0\}$. We construct three explicit laws (the exact law and the two canonical Hauser-Sastry-Kokotovic approximations), compute their validity domains in closed form and prove that (i) every periodic tracking task forces the state through the singular set, so the exact law can never be used alone; (ii) a single approximate law covers the entire admissible region $|\theta|<\pi/2$, and any two of the three laws cover $\mathbb{R}^4$ up to a codimension-two set; (iii) the three laws together cover $\mathbb{R}^4$ minus an explicit obstructed set on which the output is not linearisable by any static feedback, because the ball motion there is invariant under reversal of the input. Simulations confirm each statement.
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Systems and Control · Cross-listed from cs.RO
Lightweight Model Predictive Control for Spacecraft Rendezvous Attitude Synchronization
Peter Stadler, Alexander Meinert, Niklas Baldauf et al. (+1 more)
This work introduces two lightweight model predictive control (MPC) approaches for attitude tracking with reaction wheels during spacecraft rendezvous synchronization. Both approaches are based on a novel attitude deviation formulation, which enables the use of inherently linear constraints on angular velocity. We develop a single-loop and a dual-loop MPC; the latter embeds a stabilizing feedback controller within the inner loop, yielding a linear time-invariant system. Both controllers are implemented with CasADi - including automatic code generation - evaluated across various solvers, and validated within the Basilisk astrodynamics simulation framework. The experimental results demonstrate improved tracking accuracy alongside reductions in computational effort and
memory consumption. Finally, embedded delivery to an ARM Cortex-M7 - representative of commercial off-the-shelf devices used in New Space platforms - confirms the real-time feasibility of these approaches and highlights their suitability for onboard attitude control in resource-constrained spacecraft rendezvous missions.
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All authors Peter Stadler, Alexander Meinert, Niklas Baldauf, Alen Turnwald
Systems and Control · Cross-listed from cs.RO
Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations
Alexander Meinert, Niklas Baldauf, Peter Stadler et al. (+1 more)
This paper presents a safety-guaranteed, runtime-efficient imitation learning framework for spacecraft close proximity control. We leverage Control Barrier Functions (CBFs) for safety certificates and Control Lyapunov Functions (CLFs) for stability as unified design principles across data generation, training, and deployment. First, a nonlinear Model Predictive Control (NMPC) expert enforces CBF constraints to provide safe reference trajectories. Second, we train a neural policy with a novel CBF-CLF-informed loss and DAgger-like rollouts with curriculum weighting, promoting data-efficiency and reducing future safety filter interventions. Third, at deployment a lightweight one-step CBF-CLF quadratic program minimally adjusts the learned control input to satisfy hard safety constraints while encouraging stability. We validate the approach for ESA-compliant close proximity operations, including fly-around with a spherical keep-out zone and final approach inside a conical approach corridor, using the Basilisk high-fidelity simulator with nonlinear dynamics and perturbations. Numerical experiments indicate stable convergence to decision points and strict adherence to safety under the filter, with task performance comparable to the NMPC expert while significantly reducing online computation. A runtime analysis demonstrates real-time feasibility on a commercial off-the-shelf processor, supporting onboard deployment for safety-critical on-orbit servicing.
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All authors Alexander Meinert, Niklas Baldauf, Peter Stadler, Alen Turnwald
Systems and Control
RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction
Xiucheng Wang, Zixuan Guo and Nan Cheng
Radio maps (RMs) provide spatially continuous propagation characterizations essential for 6G network planning, but high-fidelity RM construction remains challenging. Rigorous electromagnetic solvers incur prohibitive computational latency, while data-driven models demand massive labeled
datasets and generalize poorly from simplified simulations to complex multipath environments. This paper proposes RadioDiff-FS, a few-shot
diffusion framework that adapts a pretrained main-path generator to multipath-rich target domains with only a small number of high-fidelity samples. The adaptation is grounded in a theoretical decomposition of the multipath RM into a dominant main-path component and a directionally sparse residual. This decomposition shows that the cross-domain shift corresponds to a bounded and geometrically structured feature translation rather than an arbitrary distribution change. A direction-consistency loss (DCL) is then introduced to constrain
diffusion score updates along physically plausible propagation directions, thereby suppressing phase-inconsistent artifacts that arise in the low-data regime. Experiments show that RadioDiff-FS reduces NMSE by 59.5% on static RMs and by 74.0% on dynamic RMs relative to the vanilla
diffusion baseline, achieving an SSIM of 0.9752 and a PSNR of 36.37 dB under severely limited supervision. Even in a one-shot setting with a single target-domain sample per scene, RadioDiff-FS outperforms all fully supervised baselines, confirming that the directional constraint provides an effective inductive bias under extreme data scarcity. Code is available at https://github.com/UNIC-Lab/RadioDiff-FS.
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Systems and Control · Cross-listed from cs.RO
A Passive Elastic-Folding Mechanism for Stackable Airdrop Sensors
Damyon Kim, Yuichi Honjo, Tatsuya Iizuka et al. (+6 more)
Air-dispersed sensor networks deployed from aerial robotic systems (e.g.,
UAVs) provide a low-cost approach to wide-area environmental monitoring. However, existing methods often rely on active actuators for mid-air shape or trajectory control, increasing both power consumption and system cost. Here, we introduce a passive elastic-folding hinge mechanism that transforms sensors from a flat, stackable form into a three-dimensional structure upon release. Hinges are fabricated by laminating commercial sheet materials with rigid printed
circuit boards (PCBs) and programming fold angles through a single oven-heating step, enabling scalable production without specialized equipment. Our
geometric model links laminate geometry, hinge mechanics, and resulting fold angle, providing a predictive design methodology for target configurations. Laboratory tests confirmed fold angles between 10 degrees and 100 degrees, with a standard deviation of 4 degrees and high repeatability. Field trials further demonstrated reliable data collection and LoRa transmission during dispersion, while the Horizontal Wind Model (HWM)-based trajectory simulations indicated strong potential for wide-area sensing exceeding 10 km.
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All authors Damyon Kim, Yuichi Honjo, Tatsuya Iizuka, Naomi Okubo, Naoto Endo, Hiroshi Matsubara, Yoshihiro Kawahara, Naoto Morita, Takuya Sasatani
Systems and Control · Cross-listed from cs.IT
BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding
Xiucheng Wang, Yue Zhang and Nan Cheng
Integrating large
language models (LLMs) into wireless communication optimization is a promising yet challenging direction. Existing approaches either use LLMs as black-box solvers or code generators, tightly coupling them with numerical computation. However, LLMs lack the precision required for physical-layer optimization, and the scarcity of wireless training data makes domain-specific fine-tuning impractical. We propose BeamAgent, an LLM-aided
MIMO beamforming framework that explicitly decouples semantic intent parsing from numerical optimization. The LLM serves solely as a semantic translator that converts natural
language descriptions into structured spatial constraints. A dedicated gradient-based optimizer then jointly solves the discrete base station site selection and continuous precoding design through an alternating optimization
algorithm. A scene-aware
prompt enables grounded spatial
reasoning without fine-tuning, and a multi-round interaction mechanism with dual-layer intent classification ensures robust constraint verification. A penalty-based loss function enforces dark-zone power constraints while releasing optimization degrees of freedom for bright-zone gain maximization. Experiments on a ray-tracing-based urban
MIMO scenario show that BeamAgent achieves a bright-zone power of 84.0\,dB, outperforming exhaustive zero-forcing by 7.1 dB under the same dark-zone constraint. The end-to-end system reaches within 3.3 dB of the expert upper bound, with the full optimization completing in under 2 s on a laptop.
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Systems and Control
Learn for Variation: Efficient AAV Trajectory Learning through a Differentiable Wireless World Model
Xiucheng Wang, Zhenye Chen, Nan Cheng et al. (+2 more)
Autonomous aerial vehicles (AAVs) enable data collection for sixth-generation Internet-of-Things networks, but their trajectories couple nonlinear wireless rates with long-horizon service progress. This paper views the
evolution of AAV kinematics, channel state, and user backlog as a structured differentiable world model and develops Learn for Variation (L4V) to exploit that model efficiently. L4V replaces a discontinuous completion-time objective with a cumulative-backlog surrogate, unrolls the mission dynamics, and propagates pathwise sensitivities to a neural policy through the discrete adjoint recursion. The resulting derivative is exact conditional on a fixed exogenous-noise realization;
stochastic expected-objective optimization still requires sampling. We show that the structured adjoint grows at most polynomially with the horizon and establish a stationary-point rate for fixed-step full-gradient descent under standard smoothness assumptions. The framework also learns shared OFDMA allocation under reparameterized shadowing and Rician fading, while distributional pretraining amortizes model-based optimization into forward-only deployment on unseen layouts. Paired stress tests cover channel-generator mismatch, noisy partial observations, a fixed-resource two-AAV extension, and a circular no-fly region. Code and configurations are available at https://github.com/UNIC-Lab/L4V-AAV. Against genetic-
algorithm, DQN, A2C, DDPG, and differentiable model-predictive-control implementations, L4V reduces mission time by up to $65\%$, executes a default mission in $53$ ms, and completes all $60$ frozen-policy tests after pretraining on $1{,}600$ layouts.
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All authors Xiucheng Wang, Zhenye Chen, Nan Cheng, Zhisheng Yin, Xuemin Shen
Signal Processing
PAPR-Aware Waveform Design for Energy-Efficient MIMO-OFDM SWIPT
Chongda Huang, Yue Xiao, Qianzhen Zhang et al. (+3 more)
Simultaneous wireless information and power transfer (SWIPT) critically depends on waveform design, which governs both reliable data delivery and efficient energy harvesting. Among waveform characteristics, the peak-to-average power ratio (PAPR) plays a pivotal role: low-PAPR signals improve power amplifier (PA) efficiency, while high-PAPR signals exploit rectifier nonlinearities to boost harvested energy. This duality makes PAPR a fundamental design challenge in SWIPT systems. To tackle this issue, we establish a unified analytical framework that characterizes the PAPR-dependent behaviors of both the PA and the rectifier, thereby revealing how waveform statistics determine end-to-end energy transfer efficiency. Building on this insight, we propose a frequency-domain resource allocation strategy for power-splitting SWIPT, where spectral segments are adaptively assigned to balance communication throughput with energy harvesting performance. Here, a key contribution is to extend SWIPT to
MIMO-OFDM architectures. Despite concerns over excessive PAPR in large-scale
antenna-subcarrier configurations, we demonstrate that appropriate waveform adaptation and resource optimization can transform
MIMO-OFDM into an energy-efficient platform for joint data and power transfer. Finally, simulation results confirm significant improvements in PA efficiency, rectifier output, and overall energy transfer, thereby validating the practical benefits of the proposed approach.
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All authors Chongda Huang, Yue Xiao, Qianzhen Zhang, Lilin Dan, Xianfu Lei, Kai-Kit Wong
Systems and Control · Cross-listed from cs.NI
Holistic Energy Performance Management: Enablers, Capabilities, and Features
Meysam Masoudi, Milad Ganjalizadeh, Tahar Zanouda et al. (+1 more)
Energy consumption is a significant concern for mobile network operators, and to enable further network energy improvements it is also an important target when developing the emerging 6G standard. In this paper we show that, despite the existence of many energy-saving features in 5G new
radio (NR) networks, activating them in isolation yields only suboptimal savings and often compromises other network key performance indicators (KPIs) such as coverage or latency. We first introduce a compact taxonomy that distinguishes hardware capabilities from higher-layer features. Features fall into two classes: (i) signaling and scheduling mechanisms that create idle windows, and (ii) features that utilize those windows to save energy. We then present a feature orchestrator as a logical node to coordinate between features to maximize the gain. Using a 3GPP-aligned simulator with product-realistic parameters, we show that coordinating lean NR, scheduling, and advanced sleep modes significantly reduces gNodeB (gNB) energy consumption with negligible throughput loss, compared to the uncoordinated scenario. We conclude by outlining open issues in observability, system dynamics, coordination, and intelligent automation for energy performance management.
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All authors Meysam Masoudi, Milad Ganjalizadeh, Tahar Zanouda, Pal Frenger
Signal Processing
Microdiversity and Vegetation Influence on Forward Scattering at 60 GHz and 80 GHz
Radek Zavorka, Ondrej Zeleny, Jiri Blumenstein et al. (+9 more)
Understanding the impact of vegetation and small-scale
antenna movements on signal propagation is important for the design and optimization of high-frequency wireless communication systems. This paper presents an experimental study analyzing signal propagation at 60 GHz and 80 GHz in the presence of vegetation, with a focus on forward scattering and microdiversity effects. A controlled measurement campaign was conducted in an indoor environment, where the influence of a potted plant placed in the line-of-sight (LOS) path between the transmitter and receiver was investigated. The study examines the effects of
antenna micro-shifts on the channel impulse response (CIR), highlighting variations in received power due to small positional changes of the
antennas. The results indicate that the 80 GHz band exhibits higher sensitivity to micro-movements compared to the 60 GHz band, leading to greater fluctuations in received power.
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All authors Radek Zavorka, Ondrej Zeleny, Jiri Blumenstein, Tomas Mikulasek, Rajeev Shukla, Josef Vychodil, Jaroslaw Wojtun, Niraj Narayan, Aniruddha Chandra, Jan M. Kelner, Cezary Ziolkowski, Ales Prokes
Signal Processing
Stochastic 3-D Foliage Modeling at 80 GHz: Experimental Validation and Ray-Tracing Simulations
Jiri Blumenstein, Radek Zavorka, Josef Vychodil et al. (+10 more)
A
stochastic modeling methodology for 3-D foliage is presented, aimed at enhancing ray-tracing simulations. The model supports adjustable
stochastic geometry, density, and shape to capture variability in foliage structures. The model is validated through experimental measurements of representative vegetation. The influence of foliage density and size on path loss and root mean square delay spread is analyzed to demonstrate the applicability of the model in the 80 GHz frequency band.
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All authors Jiri Blumenstein, Radek Zavorka, Josef Vychodil, Tomas Mikulasek, Jaroslaw Wojtun, Jan M. Kelner, Cezary Ziolkowski, Rajeev Shukla, Markus Hofer, Thomas Zemen, Christoph Mecklenbrauker, Aniruddha Chandra, Ales Prokes
Signal Processing
Angularly-Resolved 3D Foliage Modeling and Measurements at 60 and 80 GHz: From Stochastic Geometry to Deterministic Channel Characterization
Jiri Blumenstein, Radek Zavorka, Josef Vychodil et al. (+10 more)
In this paper, we show a
stochastic approach to generate a 3D model of a foliage, which is then used for deterministic ray-tracing channel modeling. This approach is verified by a measurement campaign at 60 and 80 GHz with 2 GHz bandwidth. The wireless channel is characterized by path-loss and RMS delay spread and we show the angular dependency of those parameters when the receiver is placed on a half-circle around the tree. Besides electromagnetic material properties, the 3D model is characterized by several interpretable parameters, including tree volume, leaf size, leaf density, and the tree crown shape parameter.
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All authors Jiri Blumenstein, Radek Zavorka, Josef Vychodil, Tomas Mikulasek, Jaroslaw Wojtun, Jan M. Kelner, Cezary Ziolkowski, Rajeev Shukla, Markus Hofer, Thomas Zemen, Christoph F Mecklenbrauker, Aniruddha Chandra, Ales Prokes
Systems and Control · Cross-listed from math.AP
Physics-grounded Mechanism Design for Spectrum Sharing between Passive and Active Users
Jiguang Yu, Nicholas Brendle, Joel T. Johnson et al. (+1 more)
We propose a physics-grounded mechanism design for dynamic spectrum sharing that bridges the gap between radiometric
retrieval constraints and economic incentives. We formulate the active and passive users coexistence problem as a Vickrey-Clarke-Groves (VCG) auctions mechanism, where the radiometer dynamically procures “quiet” time-frequency tiles from active users based on the marginal reduction in
retrieval error variance. This approach ensures allocative efficiency and dominant-strategy incentive compatibility (DSIC). To overcome the computational intractability of exact VCG on large grids, we derive an approximation
algorithm by using the monotone submodularity induced by the radiometer equation. AMSR-2-based simulations show that the approach avoids high-cost tiles by aggregating low-cost spectrum across time and frequency. In an interference-trap case study, the proposed framework reduces procurement costs by about 60% over a fixed-band baseline while satisfying accuracy targets.
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All authors Jiguang Yu, Nicholas Brendle, Joel T. Johnson, David Starobinski
Image and Video Processing
A Hybrid Physical–Digital Framework for Annotated Fracture Reduction Data Evaluated using Clinically Relevant 3D metrics
Basile Longo, Paul-Emmanuel Edeline, Hoel Letissier et al. (+4 more)
A major bottleneck in Computer-Assisted Preoperative Planning (CAPP) for fracture reduction is the limited availability of annotated data. While annotated
datasets are now available for evaluating bone fracture
segmentation algorithms, there is a notable lack of annotated data for the evaluation of automatic fracture reduction methods. Obtaining precise annotations, which are essential for training and evaluating automatic CAPP
algorithm, of the reduced bone therefore remains a critical and underexplored challenge. Existing approaches to assess reduction methods rely either on synthetic fracture simulation which often lacks realism, or on manual virtual reductions, which are complex, time-consuming, operator-dependant and error-prone. To address these limitations, we propose a hybrid physical-digital framework for generating annotated fracture reduction data. Based on fracture CTs, fragments are first 3D printed, physically reduced, fixed and CT scanned to accurately recover transformation
matrix applied to each fragment. To quantitatively assess reduction quality, we introduce a reproducible formulation of clinically relevant 3D fracture metrics, including 3D gap, 3D step-off, and total gap area. The framework was evaluated on 11 clinical acetabular fracture cases reduced by two independent operators. Compared to preoperative measurements, the proposed approach achieved mean improvements of 168.85 mm 2 in total gap area, 1.82 mm in 3D gap, and 0.81 mm in 3D step-off. This hybrid physical–digital framework enables the efficient generation of realistic, clinically relevant annotated fracture reduction data that can be used for the development and evaluation of automatic fracture reduction
algorithms.
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All authors Basile Longo, Paul-Emmanuel Edeline, Hoel Letissier, Marc-Olivier Gauci, Aziliz Guezou-Philippe, Valérie Burdin, Guillaume Dardenne
Signal Processing
Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping
Donglin Xie, Qingshuo Zhao, Jingyu Wang et al. (+9 more)
Sleep disturbances are tightly linked to cardiovascular risk, yet polysomnography (PSG)-the clinical reference standard-remains resource-intensive and poorly suited for multi-night, home-based, and large-scale screening. Single-lead electrocardiography (ECG), already ubiquitous in Holter and patch-based devices, enables comfortable long-term acquisition and encodes sleep-relevant physiology through autonomic modulation and cardiorespiratory coupling. Here, we present a proof-of-concept Holter-to-Sleep framework that, using single-lead ECG as the sole input, jointly supports overnight sleep phenotyping and Holter-grade cardiac phenotyping within the same recording, and further provides an explicit analytic pathway for scalable cardio-sleep association studies. The framework is developed and validated on a pooled multi-center PSG sample of 10,439 studies spanning four public cohorts, with independent external evaluation to assess cross-cohort generalizability, and additional real-world feasibility assessment using overnight patch-ECG recordings via objective-subjective consistency analysis. This integrated design enables robust extraction of clinically meaningful overnight sleep phenotypes under heterogeneous populations and acquisition conditions, and facilitates systematic linkage between ECG-derived sleep metrics and arrhythmia-related Holter phenotypes. Collectively, the Holter-to-Sleep paradigm offers a practical foundation for low-burden, home-deployable, and scalable cardio-sleep monitoring and research beyond traditional PSG-centric workflows.
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All authors Donglin Xie, Qingshuo Zhao, Jingyu Wang, Shijia Geng, Jiarui Jin, Jun Li, Rongrong Guo, Guangkun Nie, Gongzheng Tang, Yuxi Zhou, Thomas Penzel, Shenda Hong