Systems and Control
Spatial Characterization of Sub-Synchronous Oscillations Using Black-Box IBR Models
Muhammad Sharjeel Javaid, Gabriel Covarrubias Maureira, Ambuj Gupta et al. (+4 more)
Power systems with high penetration of inverter-based resources (IBRs) are prone to sub-synchronous oscillations (SSO). The opaqueness of vendor-specific IBR models limits the ability to predict the severity and the spread of SSO. This paper demonstrates that black-box IBR models estimated through frequency-domain identification techniques, along with dynamic network model can replicate the actual oscillatory behavior. The estimated IBR models are validated against actual IBR models in a closed-loop multi-IBR test system through modal analysis by comparing closed-loop
eigenvalues, and participation factors. Furthermore, using output-observable right eigenvectors, spatial heatmaps are developed to visualize the spread and severity of dominant SSO modes. The case studies on the 11-bus and 39-bus test systems confirm that even with the estimated IBR models, the regions susceptible to SSO can be identified in IBR-dominated power systems.
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All authors Muhammad Sharjeel Javaid, Gabriel Covarrubias Maureira, Ambuj Gupta, Debraj Bhattacharjee, Jianli Gao, Balarko Chaudhuri, Mark O'Malley
Systems and Control
Matched Filter-Based Molecule Source Localization in Advection-Diffusion-Driven Pipe Networks with Known Topology
Timo Jakumeit, Bastian Heinlein, Vukašin Spasojević et al. (+3 more)
Synthetic molecular communication (MC) has emerged as a powerful framework for modeling, analyzing, and designing communication systems where information is encoded into properties of molecules. Among the envisioned applications of MC is the localization of molecule sources in pipe networks (PNs) like the human cardiovascular system (CVS), sewage networks (SNs), and industrial plants. While existing
algorithms mostly focus on simplified scenarios, in this paper, we propose the first framework for source localization in complex PNs with known
topology, by leveraging the mixture of inverse Gaussians for hemodynamic transport (MIGHT) model as a closed-form representation for advection-
diffusion-driven MC in PNs. We propose a matched filter (MF)-based approach to identify molecule sources under realistic conditions such as unknown release times, random numbers of released molecules, sensor noise, and limited sensor sampling rate. We apply the
algorithm to localize a source of viral markers in a real-world SN and show that the proposed scheme outperforms randomly guessing sources even at low signal-to-noise ratios (SNRs) at the sensor and achieves error-free localization under favorable conditions, i.e., high SNRs and sampling rates. Furthermore, by identifying clusters of frequently confused sources, reliable cluster-level localization is possible at substantially lower SNRs and sampling rates.
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All authors Timo Jakumeit, Bastian Heinlein, Vukašin Spasojević, Vahid Jamali, Robert Schober, Maximilian Schäfer
Systems and Control · Cross-listed from math.OC
Unimodal self-oscillations and their sign-symmetry for discrete-time relay feedback systems with dead zone
Kang Tong, Christian Grussler and Michelle S. Chong
This paper characterizes self-oscillations in discrete-time linear time-invariant (LTI) relay feedback systems with nonnegative dead zone. Specifically, we aim to establish existence criteria for unimodal self-oscillations, defined as periodic solutions where the output exhibits a single-peaked period. Assuming that the linear part of system is stable, with a strictly monotonically decreasing impulse response on its infinite support, we propose a novel analytical framework based on the theory of total positivity to address this problem. We demonstrate that unimodal self-oscillations subject to mild variation-based constraints exist only if the number of positive and negative values of the system's loop gain coincides within a given strictly positive period, i.e., the self-oscillation is sign-symmetric. Building upon these findings, we derive conditions for the existence of such self-oscillations, establish tight bounds on their periods, and address the question of their uniqueness.
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Systems and Control · Cross-listed from math.OC
Mitigating Renewable-Induced Risks for Green and Conventional Ammonia Producers through Coordinated Production and Futures Trading
Huayan Geng, Yangjun Zeng and Yiwei Qiu
Renewable power-to-ammonia (ReP2A), which uses hydrogen produced from renewable electricity as feedstock, is a promising pathway for decarbonizing the energy, transportation, and chemical sectors. However, variability in renewable generation causes fluctuations in hydrogen supply and ammonia production, leading to revenue instability for both ReP2A producers and conventional fossil-based gray ammonia (GA) producers in the
market. Existing studies mainly rely on engineering measures, such as production scheduling, to manage this risk, but their effectiveness is constrained by physical system limits. To address this challenge, this paper proposes a financial instrument termed renewable ammonia futures and integrates it with production decisions to hedge ammonia output risk. Production and trading models are developed for both ReP2A and GA producers, with conditional value-at-risk (CVaR) used to represent risk preferences under uncertainty. A game-theoretic framework is established in which the two producers interact in coupled ammonia spot and futures
markets, and a Nash bargaining mechanism coordinates their production and trading strategies. Case studies based on a real-world system show that introducing renewable ammonia futures increases the CVaR utilities of ReP2A and GA producers by 5.103% and 10.14%, respectively, improving profit stability under renewable uncertainty. Sensitivity analysis further confirms the effectiveness of the mechanism under different levels of renewable variability and capacity configurations.
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Audio and Speech Processing · Cross-listed from cs.SD
NV-Bench: Benchmark of Nonverbal Vocalization Synthesis for Expressive Text-to-Speech Generation
Qinke Ni, Huan Liao, Dekun Chen et al. (+2 more)
While recent text-to-
speech (TTS) systems increasingly integrate nonverbal vocalizations (NVs), their evaluations lack standardized metrics and reliable ground-truth references. To bridge this gap, we propose NV-Bench, the first
benchmark grounded in a functional taxonomy that treats NVs as communicative acts rather than acoustic artifacts. NV-Bench comprises 1,651 multi-lingual, in-the-wild utterances with paired human reference audio, balanced across 14 NV categories. We introduce a dual-dimensional evaluation protocol: (1) Instruction
Alignment, utilizing the proposed paralinguistic character error rate (PCER) to assess controllability, (2) Acoustic Fidelity, measuring the distributional gap to real recordings to assess acoustic realism. We evaluate diverse TTS models and develop two baselines. Experimental results demonstrate a strong correlation between our objective metrics and human perception, establishing NV-Bench as a standardized evaluation framework.
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All authors Qinke Ni, Huan Liao, Dekun Chen, Yuxiang Wang, Zhizheng Wu
Systems and Control · Cross-listed from math.OC
A superposition approach for the ISS Lyapunov-Krasovskii theorem with pointwise dissipation
Andrii Mironchenko, Fabian Wirth, Antoine Chaillet et al. (+1 more)
We show that the existence of a Lyapunov-Krasovskii functional (LKF) with pointwise dissipation (i.e. dissipation in terms of the current solution norm) suffices for input-to-state stability, provided that uniform global stability can also be ensured using the same LKF. To this end, we develop a stability theory, in which the behavior of solutions is not assessed through the classical norm but rather through a specific LKF, which may provide significantly tighter estimates. We discuss the advantages of our approach by means of an example.
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All authors Andrii Mironchenko, Fabian Wirth, Antoine Chaillet, Lucas Brivadis
Signal Processing
Near-field Boundary Distance in mmWave and THz Communications with Misaligned Antenna Arrays
Peng Zhang, Vitaly Petrov and Emil Björnson
Wireless communications in the millimeter wave (mmWave) and terahertz (THz) spectrum allow harnessing large frequency bands, thus achieving ultra-high data rates. However, the inherently short wavelengths of mmWave and THz signals lead to an extended radiative near-field region, where certain canonical far-field assumptions fail. Most prior works aimed to characterize this radiative near-field region either do not consider
antenna arrays on both communicating nodes or, if they do, assume perfect
alignment between the arrays. However, such assumptions break down in many realistic deployments, where both sides must employ large-scale mmWave/THz
antenna arrays to maintain the desired communication range, while perfect
antenna alignment cannot be guaranteed particularly under nodes mobility. In this work, a generalized mathematical framework is presented to characterize the radiative near-field distance in directional mmWave and THz communication systems under various realistic array rotations and misalignments. With the use of the developed framework, compact closed-form expressions are derived for the near-field boundary distance in a wide range of
antenna configurations, including array-to-array and array-to-point setups, considering both linear and planar arrays. Our numerical study reveals that the presence of
antenna misalignment may significantly adjust the boundaries of the near-field region in mmWave and THz communication systems.
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Signal Processing
On the CRLB for Blind Receiver I/Q Imbalance Estimation in OFDM Systems: Efficient Computation and Closed-Form Bounds
Moritz Tockner, Oliver Lang, Andreas Meingassner-Lang et al. (+1 more)
Modern mobile communication receivers are often implemented with a direct-conversion architecture, which features a number of advantages over competing designs. A notable limitation of direct-conversion architectures, however, is their sensitivity to amplitude and phase mismatches between the in-phase and quadrature signal paths. Such in-phase and quadrature-phase (I/Q) imbalances introduce undesired image components in the baseband signal, degrading link performance – most notably by increasing the bit-error ratio. Considerable research effort has therefore been devoted to digital techniques for estimating and mitigating these impairments. Existing approaches generally fall into two categories: data-aided methods that exploit known pilots, preambles, or training sequences, and blind techniques that operate without such prior information. For data-aided estimation, Cramér-Rao lower bounds (CRLBs) have been established in the literature. In contrast, the derivation of a CRLB for the blind I/Q-imbalance estimation case is considerably more challenging, since the received data is random and typically non-Gaussian in the frequency domain. This work extends our earlier conference contribution, which introduced a CRLB derivation for the blind estimation of frequency-independent (FID) receiver I/Q imbalance using central limit
theorem (CLT) arguments. The extensions include a computationally efficient method for calculating the bound, reducing complexity from cubic in the number of samples to linear in the fast-
Fourier transform (FFT) size, along with a simplified closed-form approximation. This approximation provides new insights into the allocation dependent performances of existing estimation methods, motivating a pre-estimation filtering modification that drastically improves their estimation performance in certain scenarios.
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All authors Moritz Tockner, Oliver Lang, Andreas Meingassner-Lang, Mario Huemer
Audio and Speech Processing
Neural Network-Based Time-Frequency-Bin-Wise Linear Combination of Beamformers for Underdetermined Target Source Extraction
Changda Chen, Yichen Yang, Wei Liu et al. (+1 more)
Extracting a target source from underdetermined mixtures is challenging for beamforming approaches. Recently proposed time-frequency-bin-wise switching (TFS) and linear combination (TFLC) strategies mitigate this by combining multiple beamformers in each time-frequency (TF) bin and choosing combination weights that minimize the output power. However, making this decision independently for each TF bin can weaken temporal-spectral coherence, causing discontinuities and consequently degrading extraction performance. In this paper, we propose a novel neural network-based time-frequency-bin-wise linear combination (NN-TFLC) framework that constructs minimum power distortionless response (MPDR) beamformers without explicit noise covariance estimation. The network encodes the mixture and beamformer outputs, and predicts temporally and spectrally coherent linear combination weights via a cross-
attention mechanism. On dual-microphone mixtures with multiple interferers, NN-TFLC-MPDR consistently outperforms TFS/TFLC-MPDR and achieves competitive performance with TFS/TFLC built on the minimum variance distortionless response (MVDR) beamformers that require noise priors.
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All authors Changda Chen, Yichen Yang, Wei Liu, Shoji Makino
Systems and Control
ReLU Barrier Functions for Nonlinear Systems with Constrained Control: A Union of Invariant Sets Approach
Pouya Samanipour and Hasan A. Poonawala
Certifying safety for nonlinear systems with polytopic input constraints is challenging because CBF synthesis must ensure control admissibility under saturation. We propose an approximation–verification pipeline that performs convex barrier synthesis on piecewise-affine (PWA) surrogates and certifies safety for the original nonlinear system via facet-wise verification. To reduce conservatism while preserving tractability, we use a two-slope Leaky ReLU surrogate for the extended class-$\mathcal{K}$ function $\alpha(\cdot)$ and combine multiple certificates using a Union of Invariant Sets (UIS). Counterexamples are handled through local uncertainty updates. Simulations on pendulum and cart-pole systems with input saturation show larger certified invariant sets than linear-$\alpha$ designs with tractable computation time.
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Signal Processing
Fast Volume Alignment by Frequency-Marched Newton Method
Fabian Kruse, Valentin Debarnot, Vinith Kishore et al. (+1 more)
We introduce Matcha, a fast method for rotational pose estimation in three-dimensional
alignment, and combine it with FFT-based translation updates for full pose estimation. Classical matched filtering evaluates cross-correlation over a large discretized transformation space; we instead treat rotational
alignment as a continuous optimization problem on SO(3). Matcha starts from a bandlimited Wigner-D expansion of the rotational correlation, which enables rapid objective evaluation together with analytic gradients and Hessians. A low-bandwidth SOFFT search provides robust candidate rotations, which are then refined by frequency marching: the angular bandwidth is progressively increased, and candidates are updated by Newton steps at each level. This confines exhaustive search to a single low-frequency stage while allowing the final accuracy to be determined by continuous refinement rather than by the grid spacing. We prove a deterministic conditional guarantee showing that, under reasonable assumptions, Matcha returns a near-optimal solution for the final bandlimited objective. On synthetic rotation-estimation
benchmarks, Matcha attains sub-degree accuracy while substantially reducing runtime relative to exhaustive SO(3) search. Integrated into a RELION-5 subtomogram-averaging workflow, it matches the baseline reconstruction quality on the tested
dataset, reaching the same Nyquist-limited local resolution while reducing rotational pose-refinement time by more than an order of magnitude.
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All authors Fabian Kruse, Valentin Debarnot, Vinith Kishore, Ivan Dokmanić
Systems and Control
Encirclement Guaranteed Finite-Time Capture against Unknown Evader Strategies
Dinesh Patra, Prajakta Surve, Ashish R. Hota et al. (+1 more)
We consider a pursuit-evasion scenario involving a group of pursuers and a single evader in a two-dimensional unbounded environment. The pursuers aim to capture the evader in finite time while ensuring the evader remains enclosed within the convex hull of their positions until capture, without knowledge of the evader's heading angle. Prior works have addressed the problem of encirclement and capture separately in different contexts. In this paper, we present a class of strategies for the pursuers that guarantee capture in finite time while maintaining encirclement, irrespective of the evader's strategy. Furthermore, we derive an upper bound on the time to capture. Numerical results highlight the effectiveness of the proposed framework against a range of evader strategies.
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All authors Dinesh Patra, Prajakta Surve, Ashish R. Hota, Shaunak D. Bopardikar
Signal Processing
RIS-Aided RSMA Improves the Latency vs. Energy Trade-off in the Finite Block Length MIMO Downlink
Mohammad Soleymani, Bruno Clerckx, Robert Schober et al. (+1 more)
We simultaneously minimize the latency and improve energy efficiency (EE) of the multi-user multiple-input multiple-output (MU-
MIMO) rate splitting multiple access (RSMA) downlink, aided by a reconfigurable intelligent surface (RIS). Our results show that RSMA improves the EE and may reduce the delay to 13% of that of spatial division multiple access (SDMA). Moreover, RIS and RSMA support each other synergistically, while an RIS operating without RSMA provides limited benefits in terms of latency and cannot effectively mitigate interference. {Furthermore, increasing the RIS size amplifies the gains of RSMA more significantly than those of SDMA, without altering the fundamental EE-latency trade-offs.} Results also show that latency increases with more stringent reliability requirements, and RSMA yields more significant gains under such conditions, making it eminently suitable for energy-efficient ultra-reliable low-latency communication (URLLC) scenarios.
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All authors Mohammad Soleymani, Bruno Clerckx, Robert Schober, Lajos Hanzo
Image and Video Processing · Cross-listed from cs.LG
CATFormer: When Continual Learning Meets Spiking Transformers With Dynamic Thresholds
Vaishnavi Nagabhushana, Kartikay Agrawal and Ayon Borthakur
Although deep neural networks perform extremely well in controlled environments, they fail in real-world scenarios where data isn't available all at once, and the model must adapt to a new data distribution that may or may not follow the initial distribution. Previously acquired knowledge is lost during subsequent updates based on new data. a phenomenon commonly known as catastrophic forgetting. In contrast, the
brain can learn without such catastrophic forgetting, irrespective of the number of tasks it encounters. Existing spiking neural networks (SNNs) for class-incremental learning (CIL) suffer a sharp performance drop as tasks accumulate. We here introduce CATFormer (Context Adaptive Threshold
Transformer), a scalable framework that overcomes this limitation. We observe that the key to preventing forgetting in SNNs lies not only in synaptic plasticity but also in modulating neuronal excitability. At the core of CATFormer is the Dynamic Threshold Leaky Integrate-and-Fire (DTLIF)
neuron model, which leverages context-adaptive thresholds as the primary mechanism for knowledge retention. This is paired with a Gated Dynamic Head Selection (G-DHS) mechanism for task-agnostic inference. Extensive evaluation on both static (CIFAR-10/100/Tiny-ImageNet) and neuromorphic (CIFAR10-DVS/SHD)
datasets reveals that CATFormer outperforms existing rehearsal-free CIL
algorithms across various task splits, establishing it as an ideal architecture for energy-efficient, true-class incremental learning.
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Systems and Control
Iterative Learning Control-Informed Reinforcement Learning for Batch Process Control
Runze Lin, Ziqi Zhuo, Junghui Chen et al. (+2 more)
A significant limitation of Deep Reinforcement Learning (DRL) is the
stochastic uncertainty in actions generated during exploration-exploitation, which poses substantial safety risks during both training and deployment. In industrial process control, the lack of formal stability and convergence guarantees further inhibits adoption of DRL methods by practitioners. Conversely, Iterative Learning Control (ILC) represents a well-established autonomous control methodology for repetitive systems, particularly in batch process optimization. ILC achieves desired control performance through iterative refinement of control laws, either between consecutive batches or within individual batches, to compensate for both repetitive and non-repetitive disturbances. This study introduces an Iterative Learning Control-Informed Reinforcement Learning (IL-CIRL) framework for training DRL controllers in dual-layer batch-to-batch and within-batch control architectures for batch processes. The proposed method incorporates Kalman filter-based state estimation within the iterative learning structure to guide DRL
agents toward control policies that satisfy operational constraints and ensure stability guarantees. This approach enables the systematic design of DRL controllers for batch processes operating under multiple disturbance conditions.
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All authors Runze Lin, Ziqi Zhuo, Junghui Chen, Lei Xie, Hongye Su
Systems and Control
Multi-Scale Control of Large Agent Populations: From Density Dynamics to Individual Actuation
Mario di Bernardo
We review a body of recent work by the author and collaborators on controlling the spatial organisation of large
agent populations across multiple scales. A central theme is the systematic bridging of microscopic
agent-level dynamics and macroscopic density descriptions, enabling control design at the most natural level of abstraction and subsequent translation across scales. We show how this multi-scale perspective provides a unified approach to both direct control, where every
agent is actuated, and indirect control, where few leaders or herders steer a larger uncontrolled population. The review covers continuification-based control with robustness under limited sensing and decentralised implementation via distributed density estimation; leader–follower density regulation with dual-feedback stability guarantees and bio-inspired plasticity; optimal-transport methods for coverage control and macro-to-micro discretisation; nonreciprocal field theory for collective decision-making; mean-field control barrier functions for population-level safety; and hierarchical reinforcement learning for settings where closed-form solutions are intractable. Together, these results demonstrate the breadth and versatility of a multi-scale control framework that integrates analytical methods, learning, and physics-inspired approaches for large
agent populations.
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Image and Video Processing
Vision-Language Model Based Multi-Expert Fusion for CT Image Classification
Jianfa Bai, Kejin Lu, Runtian Yuan et al. (+5 more)
Robust detection of COVID-19 from chest CT remains challenging in multi-institutional settings due to substantial source shift, source imbalance, and hidden test-source identities. In this work, we propose a three-stage source-aware multi-expert framework for multi-source COVID-19 CT classification. First, we build a lung-aware 3D expert by combining original CT volumes and lung-extracted CT volumes for volumetric classification. Second, we develop two MedSigLIP-based experts: a slice-wise representation and probability learning module, and a
Transformer-based inter-slice context modeling module for capturing cross-slice dependency. Third, we train a source classifier to predict the latent source identity of each test scan. By leveraging the predicted source information, we perform model fusion and voting based on different experts. On the validation set covering all four sources, the Stage 1 model achieves the best macro-F1 of 0.9711, ACC of 0.9712, and AUC of 0.9791. Stage~2a and Stage~2b achieve the best AUC scores of 0.9864 and 0.9854, respectively. Stage~3 source classifier reaches 0.9107 ACC and 0.9114 F1. These results demonstrate that source-aware expert modeling and hierarchical voting provide an effective solution for robust COVID-19 CT classification under heterogeneous multi-source conditions.
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All authors Jianfa Bai, Kejin Lu, Runtian Yuan, Qingqiu Li, Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng
Image and Video Processing
Clinical Priors Guided Lung Disease Detection in 3D CT Scans
Kejin Lu, Jianfa Bai, Qingqiu Li et al. (+5 more)
Accurate classification of lung diseases from chest CT scans plays an important role in computer-aided diagnosis systems. However, medical imaging
datasets often suffer from severe class imbalance, which may significantly degrade the performance of deep learning models, especially for minority disease categories. To address this issue, we propose a gender-aware two-stage lung disease classification framework. The proposed approach explicitly incorporates gender information into the disease recognition pipeline. In the first stage, a gender classifier is trained to predict the patient's gender from CT scans. In the second stage, the input CT image is routed to a corresponding gender-specific disease classifier to perform final disease prediction. This design enables the model to better capture gender-related imaging characteristics and alleviate the influence of imbalanced data distribution. Experimental results demonstrate that the proposed method improves the recognition performance for minority disease categories, particularly squamous
cell carcinoma, while maintaining competitive performance on other classes.
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All authors Kejin Lu, Jianfa Bai, Qingqiu Li, Runtian Yuan, Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng
Audio and Speech Processing
Marc Casals-Salvador, Federico Costa, Rodolfo Zevallos et al. (+1 more)
Speech Emotion Recognition (SER) plays a key role in advancing human-computer interaction.
Attention mechanisms have become the dominant approach for modeling emotional
speech due to their ability to capture long-range dependencies and emphasize salient information. However, standard self-
attention suffers from quadratic computational and
memory complexity, limiting its scalability. In this work, we present a systematic
benchmark of optimized
attention mechanisms for SER, including RetNet, LightNet, GSA, FoX, and KDA. Experiments on both MSP-Podcast
benchmark versions show that while standard self-
attention achieves the strongest recognition performance across test sets, efficient
attention variants dramatically improve scalability, reducing inference latency and
memory usage by up to an order of magnitude. These results highlight a critical trade-off between accuracy and efficiency, providing practical insights for designing scalable SER systems.
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All authors Marc Casals-Salvador, Federico Costa, Rodolfo Zevallos, Javier Hernando
Signal Processing
Dual-Domain Sparse Adaptive Filtering: Exploiting Error Memory for Improved Performance
Mohammad Salman, Hadi Zayyani, Felipe A. P. de Figueiredo et al. (+2 more)
Many signal processing applications such as acoustic echo cancellation and wireless channel estimation require identifying systems where only a small fraction of coefficients are actually active, i.e. sparse systems. Zero-attracting adaptive filters tackle this by adding a penalty that pulls inactive coefficients toward zero, speeding up convergence. However, these
algorithms determine which coefficients to penalize based solely on their current size. This creates a problem during early adaptation since active coefficients that should eventually grow large start out small, making them look identical to truly inactive coefficients. The
algorithm ends up applying strong penalties to the very coefficients it needs to develop, slowing down the initial convergence. This paper provides a solution to this problem by introducing a dual-domain approach that looks at coefficients from two perspectives simultaneously. Beyond just tracking coefficient magnitude, we introduce an error-
memory vector that monitors how persistently each coefficient contributes to the adaptation error over time. If a coefficient keeps showing up in the error signal, it is probably active even if it is still small. By combining both views, the proposed dual-domain sparse adaptive filter (DD-SAF) can identify active coefficients early and eliminate penalties accordingly. Moreover, complete theoretical analysis is derived. The analysis shows that DD-SAF maintains the same stability properties as standard least-mean-square (LMS) while achieves provably better steady-state performance than existing methods. Simulations demonstrate that the DD-SAF converges to the steady-state faster and/or convergences to a lower mean-square-deviation (MSD) than the standard LMS and the reweighted zero-attracting LMS (RZA-LMS)
algorithms for sparse system identification settings.
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All authors Mohammad Salman, Hadi Zayyani, Felipe A. P. de Figueiredo, Hasan Abu Hilal, Mostafa Rashdan
Signal Processing
Beam Prediction Based on Multimodal Large Language Models
Tianhao Mao, Le Liang, Jie Yang et al. (+3 more)
Accurate beam prediction is a key enabler for next-generation wireless communication systems. In this paper, we propose a
multimodal large
language model (LLM)-based beam prediction framework that effectively utilizes contextual information, provided by sensory data including RGB camera images and
LiDAR point clouds. To effectively fuse heterogeneous modalities, we design specialized modality encoders together with a beam-guided
attention masking mechanism and a high-frequency temporal
alignment strategy, enabling robust cross-modal feature integration under dynamic environments. Furthermore, we construct a large-scale
multimodal dataset for communication, named
Multimodal-Wireless, which covers diverse weather and traffic conditions with high-fidelity ray-tracing labels. Extensive simulation results demonstrate that the proposed approach significantly reduces the reliance on oracle angle-of-departure knowledge and consistently outperforms state-of-the-art
multimodal LLM-based beam prediction methods in terms of beam accuracy and communication performance, improving the average Top-1 accuracy to 80.8% and the average normalized gain to 89.1%.
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All authors Tianhao Mao, Le Liang, Jie Yang, Xiao Li, Shi Jin, Geoffrey Ye Li
Signal Processing
Generative Semantic HARQ: Latent-Space Text Retransmission and Combining
Bin Han, Yulin Hu and Hans D. Schotten
Semantic communication conveys meaning rather than raw bits, but reliability at the semantic level remains an open challenge. We propose a semantic-level hybrid automatic repeat request (HARQ) framework for text communication, in which a
Transformer-variational autoencoder (VAE) codec operates as a lightweight overlay on the conventional protocol stack. The
stochastic encoder inherently generates diverse latent representations across retransmissions-providing incremental knowledge (IK) from a single model without dedicated protocol design. On the receiver side, a soft quality
estimator triggers retransmissions and a quality-aware combiner merges the received latent vectors within a consistent latent space. We systematically
benchmark six semantic quality metrics and four soft combining strategies under hybrid semantic distortion that mixes systematic bias with additive noise. The results suggest combining Weighted-Average or MRC-Inspired combining with self-consistency-based HARQ triggering for the best performance.
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Systems and Control
Data-Driven Robust Predictive Control with Interval Matrix Uncertainty Propagation
Renato Quartullo, Andrea Garulli and Mirko Leomanni
This paper presents a new data-driven robust predictive control law, for linear systems affected by unknown-but-bounded process disturbances. A sequence of input-state data is used to construct a suitable uncertainty representation based on interval
matrices. Then, the effect of uncertainty along the prediction horizon is bounded through an operator leveraging
matrix zonotopes. This yields a tube that is exploited within a variable-horizon optimal control problem, to guarantee robust satisfaction of state and input constraints. The resulting data-driven predictive control scheme is proven to be recursively feasible and practically stable. A numerical example shows that the proposed approach compares favorably to existing methods based on zonotopic tubes.
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Audio and Speech Processing
LLMs and Speech: Integration vs. Combination
Robin Schmitt, Albert Zeyer, Mohammad Zeineldeen et al. (+2 more)
In this work, we study different approaches to utilize large
language models (LLMs) for automatic
speech recognition (ASR). Specifically, we compare the tight integration of an acoustic model (AM) with the LLM ("
speech LLM") to the traditional way of combining AM and LLM via shallow fusion and provide ablations on the effect of different label units and LLM sizes. For tight integration, we further examine the effect of
attention interfaces, encoder downsampling, and length normalization. Furthermore, we investigate joint recognition with a CTC model to mitigate
hallucinations of
speech LLMs and present effective optimizations. We train and evaluate on LibriSpeech and Loquacious and additionally evaluate on the HuggingFace ASR leaderboard. Across model sizes, we find that shallow fusion consistently outperforms tight integration of AM and LLM on in-domain data, highlighting the importance of strong shallow-fusion baselines when evaluating
speech LLMs for ASR. On the more heterogeneous HuggingFace ASR leaderboard, however, the integrated prefix LLM achieves lower average WER than shallow fusion, with gains concentrated on out-of-domain corpora.
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All authors Robin Schmitt, Albert Zeyer, Mohammad Zeineldeen, Ralf Schlüter, Hermann Ney
Systems and Control
Chattering Reduction for a Second-Order Actuator via Dynamic Sliding Manifolds
Patricia Nöther, Lars Watermann and Johann Reger
We analyze actuator chattering in a scalar integrator system subject to second-order actuator dynamics with an unknown time constant and first-order sliding-mode control, using both a conventional static sliding
manifold and a dynamic sliding
manifold. Using the harmonic balance method, we prove that it is possible to adjust the parameters of the dynamic sliding
manifold for the specified system class so as to reduce the amplitude of the chattering in comparison to the static
manifold. We illustrate our results with a simulation example. This contribution serves as a proof of concept to motivate further investigations in chattering reduction via dynamic sliding
manifolds.
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Audio and Speech Processing
Deep Filter Estimation from Inter-Frame Correlations for Monaural Speech Dereverberation
Ui-Hyeop Shin, Jun Hyung Kim, Jangyeon Kim et al. (+2 more)
Speech dereverberation with a distant microphone is challenging because reverberation is correlated with the target
speech, and models trained on simulated data often generalize poorly to real recordings. We propose IF-CorrNet, a correlation-to-filter architecture for monaural dereverberation. Instead of feeding raw complex STFT coefficients to the network, IF-CorrNet computes inter-frame correlations among neighboring frames at each time-frequency bin and estimates multi-frame deep filters from these features with a dual-path
Transformer backbone. This design makes inter-frame dependencies explicit at the network input while retaining a multi-frame filtering output, a pairing motivated by the normal equation of linear multi-frame filtering. On the REVERB Challenge corpus, IF-CorrNet achieves the best CD, LLR, SNRfw, and PESQ among the compared dereverberation baselines on SimData, and the highest SRMR among the compared systems on RealData. The ablation shows higher RealData SRMR with correlation inputs for both filtering and masking, with filtering adding a further gain.
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All authors Ui-Hyeop Shin, Jun Hyung Kim, Jangyeon Kim, Wooseok Kim, Hyung-Min Park
Signal Processing
Cyclic Delay-Doppler Shift: A Simple Transmit Diversity Technique for Ultra-Reliable Communications in Doubly Selective Channels
Haoran Yin, Yu Zhou, Yanqun Tang et al. (+7 more)
Affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) are two promising advanced waveforms proposed for reliable communications in high-mobility scenarios. In this paper, we introduce a simple transmit diversity technique, termed cyclic delay-Doppler shift (CDDS), for these two advanced waveforms to achieve ultra-reliable communications in doubly selective channels (DSCs). Two simple CDDS schemes, named modulation-domain CDDS (MD-CDDS) and time-domain CDDS (TD-CDDS), are proposed, which perform CDDS in advance at the transmitter before and after the modulation, respectively. We demonstrate that both of the two proposed CDDS schemes can be implemented efficiently and flexibly by multiplying the transmit vector with a well-designed precoding
matrix, which is nothing but a sparse phase-compensated permutation
matrix. Moreover, we theoretically and numerically prove that CDDS can provide
MIMO-AFDM and
MIMO-OTFS with optimal transmit diversity gain when a proper CDDS step is adopted. Compared to the conventional transmit diversity techniques, the proposed CDDS scheme enjoys the advantages of lower channel estimation overhead, implementation complexity, and signal processing latency, making it particularly suitable for ultra-reliable communications in high-mobility scenarios.
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All authors Haoran Yin, Yu Zhou, Yanqun Tang, Di Zhang, Chi Zhang, Xizhang Wei, Jiaojiao Xiong, Fan Liu, Marwa Chafii, Mérouane Debbah
Signal Processing
RF-Fencing: A Novel RIS-Based Service for Proactive Covert Communications
Alexandros I. Papadopoulos, Dimitrios Tyrovolas, Alexandros Pitilakis et al. (+6 more)
Programmable wireless environments (PWEs), empowered by reconfigurable intelligent surfaces (RISes), have emerged as a transformative paradigm for next-generation networks, enabling deterministic control over electromagnetic (EM) propagation to enhance both performance and security. In this work, we introduce RF-Fencing, a novel RIS-enabled PWE service that enforces spatially selective control over wireless transmissions, simultaneously suppressing unwanted signal exposure while sustaining robust connectivity for legitimate users. To realize this
vision, we develop SHIELD, a lightweight and scalable
algorithm that orchestrates multiple RIS units by multiplexing precompiled codebook entries with real-time, low-complexity optimization. Through extensive evaluations across diverse frequencies, RIS configurations, and deployment scenarios, SHIELD demonstrates both far-field directional control and near-field quiet-zone creation, thereby enhancing network security. Our findings reveal that SHIELD effectively balances proactive covert communication with service delivery by dynamically managing multiple signal suppression and delivery areas, while enabling the realization of EM quiet zones with minimal impact on surrounding regions, ultimately establishing RF-Fencing as a practical RIS-based foundation for
privacy-preserving and adaptive wireless environments in future 6G networks.
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All authors Alexandros I. Papadopoulos, Dimitrios Tyrovolas, Alexandros Pitilakis, Panagiotis D. Diamantoulakis, Antonios Lalas, Konstantinos Votis, Nikolaos V. Kantartzis, Sotiris Ioannidis, Christos Liaskos
Systems and Control
A System-Theoretic Approach to Hawkes Process Identification with Guaranteed Positivity and Stability
Xinhui Rong and Girish N. Nair
The Hawkes process models self-exciting event streams, requiring a strictly non-negative and stable
stochastic intensity. Standard identification methods enforce these properties using non-negative causal bases, yielding conservative parameter constraints and severely ill-conditioned least-squares Gram
matrices at higher model orders. To overcome this, we introduce a system-theoretic identification framework utilizing the sign-indefinite orthonormal Laguerre basis, which guarantees a well-conditioned asymptotic Gram
matrix independent of model order. We formulate a constrained least-squares problem enforcing the necessary and sufficient conditions for positivity and stability. By constructing the empirical Gram
matrix via a Lyapunov equation and representing the constraints through a sum-of-squares trace equivalence, the proposed
estimator is efficiently computed via semidefinite programming.
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Systems and Control
Intelligent Control of Differential Drive Robots Subject to Unmodeled Dynamics with EKF-based State Estimation
Amos Alwala, Yuchen Hu, Gabriel da Silva Lima et al. (+1 more)
Reliable control and state estimation of differential drive
robots (DDR) operating in dynamic and uncertain environments remains a challenge, particularly when system dynamics are partially unknown and sensor measurements are prone to degradation. This work introduces a unified control and state estimation framework that combines a Lyapunov-based nonlinear controller and Adaptive Neural Networks (ANN) with Extended Kalman Filter (EKF)-based multi-sensor fusion. The proposed controller leverages the universal approximation property of neural networks to model unknown nonlinearities in real time. An online adaptation scheme updates the weights of the radial basis function (RBF), the architecture chosen for the ANN. The learned dynamics are integrated into a feedback linearization (FBL) control law, for which theoretical guarantees of closed-loop stability and asymptotic convergence in a trajectory-tracking task are established through a Lyapunov-like stability analysis. To ensure robust state estimation, the EKF fuses inertial measurement unit (IMU) and odometry from
monocular, 2D-
LiDAR and wheel encoders. The fused state estimate drives the intelligent controller, ensuring consistent performance even under drift, wheel slip, sensor noise and failure. Gazebo simulations and real-world experiments are done using DDR, demonstrating the effectiveness of the approach in terms of improved velocity tracking performance with reduction in linear and angular velocity errors up to $53.91\%$ and $29.0\%$ in comparison to the baseline FBL.
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All authors Amos Alwala, Yuchen Hu, Gabriel da Silva Lima, Wallace Moreira Bessa