Audio and Speech Processing
SENSE: Semantic Neural Speech Synthesis from Brain Dynamics via Spatial Graph Encoding
Jisoo Park, Seonghak Lee, Hyojin Park et al. (+1 more)
Reconstructing
speech from non-invasive
brain signals offers a promising pathway for restoring communication in individuals who are cognitively intact but unable to speak. Existing
EEG-to-
speech approaches formulate this task as acoustic reconstruction, optimizing waveform fidelity while ignoring whether the generated
speech preserves high-level semantic content. In this work, we revisit this formulation and argue that
EEG signals carry not only acoustic but also semantic information. We identify two key limitations of prior methods: (1) the neglect of spatial relationships between
EEG electrodes, and (2) the failure to exploit the semantic structure of the N400 paradigm, where congruent and incongruent trials reflect distinct semantic processing. We propose SENSE(Semantic-
EEG Neural
Speech SynthEsis), which combines a graph-based
EEG encoder over electrode geometry with
EEG Semantic Conditioning (ESC), aligning
EEG to a pretrained semantic space using only congruent trials. On the N400
dataset, SENSE consistently outperforms prior methods on both acoustic and semantic metrics, and model-internal channel attribution suggests distributed reliance on auditory, sensorimotor, and centro-parietal regions, consistent with known
speech-perception neuroscience. In the unseen-subject setting, SENSE trained on only two subjects already surpasses the strongest baseline trained on all eighteen subjects in word error rate, and matches it on acoustic metrics with as few as eight subjects.
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All authors Jisoo Park, Seonghak Lee, Hyojin Park, Junseok Kwon
Systems and Control
Quantifying EnergyNet performance: a simulation-based framework for decentralized energy networks
Max Collins, Jonas Birgersson, Marc A. Weiss et al. (+10 more)
Power transmission and distribution infrastructure connects generation with consumers. Extreme weather, infrastructure failures, and geopolitical conflicts increasingly disrupt these connections, leaving large groups without electricity until service is restored. Local generation, storage, and controllable loads pose challenges but also enable new ways to organize energy systems. EnergyNet is a decentralized architecture of interconnected power electronic Energy Routers. Each coordinates local loads, generation, storage, and grid connections. Bidirectional Energy Links enable exchange between routers, while the Energy Protocol communicates needs and availability to coordinate transfers. EnergyNet can operate with or without the public grid. This paper develops a quantitative framework for modeling and evaluating EnergyNet. It represents
topology, prioritized loads, generation, storage, grid connections, links, and component limits. Receding-horizon optimization calculates energy allocation and exchange, supporting also disturbance and reliability analysis. A three-router case illustrates energy sharing and islanded operation. The framework then models a Stanford University campus microgrid and an off-grid microgrid in Half Moon Bay. Under low-solar conditions, electric bus visits support the off-grid microgrid while preserving transport service. Finally, the paper models an EnergyNet under construction in Lund, Sweden, comprising ten buildings and 278 apartments. Full service is maintained in summer and winter and after the loss of one grid interface or Energy Link. Losing two links partitions the network; service in the island depends on local resources and repair time. Reliability screening identifies partitioning as rare but credible. Monte Carlo analysis shows full critical service in sampled summer conditions and median continuous critical service of approximately 92% in winter.
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All authors Max Collins, Jonas Birgersson, Marc A. Weiss, Jimmy Chen, Mark Z. Jacobson, Newsha K. Ajami, Daniel Kammen, Tomas Kåberger, Franklin Carrero-Martínez, Michael Menser, Henrik Ny, Lou Riordan, Jill G. Ferguson
Image and Video Processing · Cross-listed from cs.IT
On Task Scope and Information Retention in Source Coding
Alireza Furutanpey and Kerstin Bunte
We argue that dividing codec design into Coding for Machines (CfM) and Coding for Humans (CfH) is a misleading distinction for deciding what information a codec may discard. Receiver identity does not determine admissible information loss. The required rate depends on task scope, including the predictions to support, their losses and tolerated risks, the encoder observation, and the permitted decoding procedures. Notably, a machine task may have a higher minimum rate than a restricted human decision. Rate savings on selected machine tasks apply only to the stated requirements, not to an intrinsic ordering by receiver type. We extend source and feature coding to finite task families, derive when restricting the encoder observation preserves the minimum rate, and show that equality between source and split-feature coding rates can no longer hold as the task scope expands.
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Signal Processing
Möbius Reparametrization of Multiport-Network Models of PIN-Diode-Programmable Metasurfaces for Accurate Low-Order Neumann Approximations
Philipp del Hougne
Accurate models of programmable metasurfaces based on multiport-network theory (MNT) account for mutual coupling (MC) through a configuration-dependent
matrix inversion. The latter's high computational cost during gradient-based optimization (GBO) can be alleviated via a finite-order Neumann approximation. We show that the accuracy of this approximation depends strongly on the (tacitly) chosen MNT parametrization. For 1-bit-programmable meta-elements (e.g., meta-elements based on PIN diodes), we identify a closed-form Möbius reparametrization that depends only on the meta-element's two load states and requires neither training data nor numerical optimization. For an experimentally estimated proxy MNT model of a fabricated 96-element 19-GHz dynamic metasurface
antenna with strong MC, a second-order Neumann approximation with our reparametrization achieves forward and control-gradient accuracies of 21.68 and 21.70 dB, respectively. Relative to the full proxy MNT, it reduces the runtime of a combined forward and control-gradient evaluation by a third and the saved-tensor
memory by a factor of ten. In a prototypical GBO problem of end-to-end optimization for DMA-based scene classification, it achieves 94.97% test accuracy vs. 95.57% with the full model. We further note that MC-strength metrics and MC-unaware
benchmarks should be parametrization-invariant, motivating, for instance, definitions based on the best directly fitted zeroth-order model.
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Signal Processing
End-to-End Optical Semantic Communication over a Nonlinear WDM Fiber Link
Hussein Jammal, Andrea Bianco and Cristina Rottondi
Emerging optical-network applications increasingly use received data for inference and control rather than exact source reproduction, creating an opportunity to trade bit-level fidelity for greater transmission reach and efficiency. We propose an end-to-end optical semantic communication system for joint image classification and reconstruction over a nonlinear wavelength-division multiplexed (WDM) fiber channel. The system maps each image directly into a fixed-length sequence of channel symbols that preserves task-relevant information, without explicit source compression or channel coding. Experiments on the MNIST
dataset cover launch powers from -9 to +3 dBm, fiber lengths up to 800 km, and 16-, 64-, and 256-Quadrature Amplitude Modulation (QAM) formats. At 0 dBm, classification accuracy remains between 98.92% and 99.31% across all tested link lengths and modulation orders, while requiring fewer transmitted symbols than a Low-Density Parity-Check (LDPC)-coded JPEG baseline at every tested modulation order. These results show that semantic communication can simultaneously extend optical reach and reduce transmission resources by conveying only task-relevant information.
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Signal Processing
AVSG: Accelerated Vectorized Sparse Gather for Efficient KV Cache Offload in Sparse-Attention LLM Serving
Wenwei Kuang, Xiangyu Wang, Chong Wu et al. (+5 more)
Dynamic sparse
attention reduces long-context
attention computation by selecting only a subset of
tokens, but still requires access to the full KV cache, leaving serving
memory-bound. Offloading the KV cache to host
memory reduces device
memory pressure but places H2D transfers on the decoding critical path. In DSA, substantial overlap in selected KV entries across decoding steps creates an opportunity for device-resident reuse. Exploiting this reuse efficiently, however, presents three critical challenges: costly matching of selected
tokens against entries retained in the HBM buffer, uneven distribution of the remaining H2D transfers across accelerator cores, and retaining frequently accessed KV entries within limited HBM capacity. We present AVSG, an operator that addresses these challenges for DSA while preserving its exact
token selections. AVSG uses vectorized hash matching to identify reusable HBM-buffer slots and reserve slots for entries requiring transfer, then evenly partitions these H2D transfers across accelerator cores. Lifetime-based buffer management retains frequently accessed KV entries in the device buffer across decoding steps, and shared slot reservations extend reuse across
tokens within a multi-
token prediction iteration. On a single NPU, vectorized hash matching is 2.80x faster than scalar dual-pointer matching, miss-only transfer raises effective H2D bandwidth by up to 34.44x over request-level assignment, and an 8K-entry buffer reaches a 94.83% hit rate on real requests. These gains translate into end-to-end improvement: on a serving stack processing a real-world production
dataset, AVSG reduces time per output
token by 39% and increases output throughput by 1.27x relative to the same KV offload layout without HBM-buffer reuse, demonstrating the benefit of efficient matching, balanced transfer, and effective HBM residency in production-scale serving.
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All authors Wenwei Kuang, Xiangyu Wang, Chong Wu, Jun Wang, Weijie Zhang, Brian K Chen, Longwen Lan, Ken Zhang
Signal Processing
A Low-Complexity Message-Passing Receiver for FTN-WAFT under Nonlinear Distortion
Xianle Dai, Qu Luo, Jianguo Li et al. (+4 more)
This letter proposes a faster-than-Nyquist (FTN) weighted affine
Fourier transform (WAFT) transmission scheme, termed FTN-WAFT, which combines the peak-to-average power ratio (PAPR) flexibility of WAFT with FTN time compression to improve spectral efficiency and power amplifier (PA) efficiency over high-mobility channels. However, operating the PA at a reduced input back-off (IBO) inevitably introduces nonlinear distortion, which further couples with FTN-induced inter-symbol interference (FTN-ISI) and doubly selective channel dispersion. To address this practical impairment, a multilayer message-passing (MLMP) receiver aided by PA interference cancellation (PA-IC), termed PA-IC-MLMP, is proposed. The proposed receiver reconstructs the dominant PA distortion from soft symbol estimates, cancels the channel-propagated nonlinear interference, and performs MLMP over the WAFT, FTN, channel, and constellation layers. Simulation results show that the proposed receiver improves the bit error rate (BER) and effective throughput under nonlinear PA constraints while maintaining low complexity.
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All authors Xianle Dai, Qu Luo, Jianguo Li, Bowei Xue, Kai Yang, Luciano Leonel Mendes, Pei Xiao
Systems and Control · Cross-listed from math.OC
Information Limits of Multistage Inventory Control: Learning, Valuation, and Censoring
Hanzhang Qin, David Simchi-Levi and Ruihao Zhu
Learning how to replenish inventory and estimating the cost of doing so require different information. We establish information-theoretic lower bounds for offline multistage inventory control with independent bounded demand. For fixed additive error in total expected cost, policy learning requires cubically many scalar observations in the horizon in the worst case, even under full observation and adaptive sample allocation. This matches the known empirical-risk upper order. Stationarity permits a quadratic policy-learning guarantee, yet valuation with inherited stock can remain cubically difficult even when the optimal policy is known. Censoring sharpens this distinction: optimal decisions can be learnable while absolute costs remain unidentified. We give a carrysafe coverage condition under which truncation preserves inventory transitions and policy gaps exactly. Combining this reduction with a matching lower bound yields the sharp censored-learning rate, with raw sample requirements inversely proportional to the usable fraction of sales logs. The same qualified observations can check coverage and fit the policy under a joint error guarantee. The results isolate information limits for decisions and valuation under an explicit conditionally independent logging protocol.
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Signal Processing
Bias-Compensated Complex-Valued Bilinear Filtering
Bernhard Plaimer, Andreas Meingassner-Lang, Yannis Kowalewski et al. (+3 more)
Complex-valued optimal and adaptive filters are widely used to identify unknown systems in a broad range of practical applications. Recently, a number of complex-valued bilinear filters, such as the complex-valued bilinear Wiener filter (C-BWF), and the complex-valued bilinear least mean squares (C-BLMS) filter, have been proposed to model and identify complex-valued systems, which are bilinear with respect to their coefficients. However, if the input signals are contaminated with noise, the performance of these complex-valued bilinear filters degrades significantly due to an additional noise-induced bias. To overcome this issue, we propose novel bias-compensated complex-valued bilinear filters that estimate and account for input-noise statistics. Specifically, a bias-compensated C-BWF and a bias-compensated C-BLMS filter are derived in this work. We further include a convergence analysis for the latter. Since practical applications require knowledge of the input-noise variance, we briefly present an adapted method from literature to estimate this quantity. Finally, simulation results demonstrate the superior performance of the proposed filters compared to their standard (non-bias-compensated) counterparts and several state-of-the-art methods.
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All authors Bernhard Plaimer, Andreas Meingassner-Lang, Yannis Kowalewski, Matthias Wagner, Oliver Lang, Mario Huemer
Signal Processing · Cross-listed from cs.IT
Rotatable Antenna-Enabled Space-Air-Ground Integrated Networks: Opportunities and Challenges
Yanhua Tan, Beixiong Zheng, Tiantian Ma et al. (+6 more)
Space-air-ground integrated networks (SAGINs) are expected to support ubiquitous three-dimensional (3D) communication and sensing services in future sixth-generation (6G) wireless networks. However, heterogeneous mobility and service requirements across the space, air, and ground segments challenge fixed-boresight or fixed-sector
antennas in supporting wide-area coverage, maintaining directional
alignment over time-varying communication links, and flexibly adjusting observation directions for sensing tasks. To address these limitations, non-fixed flexible
antenna architectures, such as fluid
antenna system (FAS), movable
antenna (MA), and pinching
antenna, have garnered significant interest in recent years. Among them, rotatable
antenna (RA) technology has emerged as a promising solution by enabling mechanical or electronic boresight adjustment while keeping the
antenna positions fixed. This article investigates the roles of RA technology in enabling communication and sensing in SAGINs. Specifically, we first explain how RA boresight control complements platform mobility and supports wide-area coverage, dynamic directional transmission, cross-segment cooperative operation, and cooperative sensing. We then discuss the main design challenges, potential approaches, and future research directions, including channel acquisition and predictive tracking, joint orientation control and access/handover coordination, cross-segment coordination and resource management, as well as deployment tradeoffs and practical implementation. Finally, an RA array prototype and two representative simulation studies are presented to illustrate the feasibility and potential performance gains of RA-enabled SAGINs.
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All authors Yanhua Tan, Beixiong Zheng, Tiantian Ma, Qingjie Wu, Lipeng Zhu, Wenyan Ma, Cheng-Xiang Wang, Robert Schober, Rui Zhang
Image and Video Processing
CASR: Content-Adaptive Neural Super-Resolution Post-Filter for Versatile Video Coding via Low-Rank Overfitting
Khoa Pham-Dinh, Francesco Cricri, Maria Santamaria et al. (+5 more)
The use of super-resolution as a post-processing step following a
video codec allows
videos to be encoded at reduced spatial resolution at the encoder side and to be reconstructed and upsampled to the original resolution at the decoder side. In this way, the required bitrate is reduced and the quality of the reconstructed frames is improved without modifying the core coding architecture. However, generic SR models are typically trained offline and lack sufficient adaptability to the diverse content characteristics and compression artifacts produced by
video codecs, which limits their effectiveness during test time. To address the limitation, this paper proposes CASR (Content-Adaptive Super-Resolution), a content-adaptive SR post-filter framework for Versatile
Video Coding (VVC), based on encoder-side overfitting on each input sequence. In order to limit the bitrate overhead required for signalling the content adaptation signal, i.e. the weight-update, Low-Rank Adaptation (LoRA) is leveraged. The method freezes the convolution kernels of a pretrained SR network and fine-tunes only lightweight rank-r
matrices attached to selected convolution layers, using VVC decoded frames and quantization-parameter (QP) maps of test sequences as supervision. The resulting low-rank update is compressed with the MPEG Neural Network Compression and Representation (NNR) standard. Experiments on the JVET common test conditions (CTC) class A1 and A2 sequences indicate that LoRA-based content adaptation provides bitrate savings over a non-adapted SR post-filter at a small signalling cost. Compared with the VVC Test Model (VTM21), the proposed method achieves BD-rate savings of -10.93% (Y), -15.39% (U), -24.41% (V) under random access and -13.43% (Y), -5.75% (U), -22.94% (V) under all-intra. An ablation of the LoRA rank r further shows that r=4 provides the best trade-off between coding gain and signalling cost.
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All authors Khoa Pham-Dinh, Francesco Cricri, Maria Santamaria, Honglei Zhang, Hamed R. Tavakoli, Moncef Gabbouj, Juho Kannala, Miska M. Hannuksela
Signal Processing
Koopman-Predictive Coefficient Tracking for Vehicle-Mounted Monostatic ISAC
Anh Tuyen Le, Xiaojing Huang and Y. Jay Guo
This paper studies predictive coefficient tracking for digitally generated self-interference cancellation (SIC) in vehicle-mounted monostatic integrated sensing and communication (
ISAC) systems. In such systems, the canceller must suppress strong transmitter leakage and nearby unintended reflections while preserving delayed sensing echoes. On mobile platforms, mechanical vibration from engines, rotors, or nearby structures can induce quasi-periodic variation in the self-interference (SI) channel. The optimal cancellation coefficients may then vary within one adaptation period, causing the applied coefficients of a conventional adaptive filter to lag behind the moving SI channel and increasing the residual SI. To address this problem, we propose a Koopman-predictive SIC (KP-SIC) module that augments a conventional adaptive filter with a predicted coefficient trajectory. KP-SIC uses recent effective SI-channel coefficient snapshots to identify structured vibration-induced dynamics and predict a sample-varying coefficient sequence for the next adaptation period. The predicted coefficients place the canceller closer to the expected time-varying optimum, while the adaptive filter corrects residual prediction errors and unstructured variation. For evaluation, the module is integrated with a widely linear normalized least mean-square (WL-NLMS) filter under a flexible analog SIC (FASIC) framework. Simulations show that KP-SIC improves SI suppression by $6.3$–$10.2$~dB over conventional FASIC-WL-NLMS once the observation window spans at least about $1.5$ vibration cycles. Compared with a non-causal performance bound, KP-SIC achieves $60$–$74\%$ of the available prediction gain.
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Systems and Control · Cross-listed from cs.MA
PowerMarketJax: A JAX Benchmark Suite for Multi-Agent Reinforcement Learning in Power Markets
Zhanhua Pan, Xin Qin, Xiao Liu et al. (+3 more)
Power
markets are a natural testbed for multi-
agent reinforcement learning (MARL), where multiple self-interested participants repeatedly submit bids. A
market-clearing mechanism then determines dispatch and prices subject to power grid constraints and
market settlement rules. However, existing MARL environments typically focus on a single
market setting, implement simplified clearing mechanisms, or rely on CPU-based optimization solvers that slow large-scale training and limit the systematic study of bidding strategies and
market behavior. We introduce PowerMarketJax, a
benchmark suite for MARL across five power
markets: day-ahead wholesale, real-time balancing, ancillary services, peer-to-peer double auctions, and local flexibility. Each environment implements its own clearing, pricing, and settlement rules while providing a common framework for learning and evaluation. We find that learned bidding behavior depends strongly on the
market design: independent learners can miss better strategies when gains require many
agents to change together, when more profitable strategies lie beyond a region of lower profit, or when profits disappear as more
agents adopt the same strategy. PowerMarketJax implements both
market simulation and policy training in JAX, allowing the entire pipeline to run on the GPU with 1,024 X 1,200 parallelisms across both environments and
market participants, achieving up to 33X speedup over CPU-based baselines. Our open-source
benchmark is available at: https://github.com/powermarketjax/PowerMarketJax.
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All authors Zhanhua Pan, Xin Qin, Xiao Liu, Zhilong Cao, Jianhong Wang, Dawei Qiu
Signal Processing
Communication-Oriented Channel Characteristics of Holographic Curvature-Reconfigurable Apertures
Hang Lin, Shu Sun, Hangsong Yan et al. (+1 more)
This letter investigates the joint impact of array
curvature and mutual coupling (MC) on holographic
curvature-reconfigurable apertures. An analytical framework is developed to characterize spatial correlation, coupling-aware reference directivity, and spectral efficiency. Under the small-
curvature regime, we establish a relation linking inter-element distances to coupling perturbations and spatial-mode
eigenvalues. Simulation results reveal asymmetric spectral-efficiency variations between transmit- and receive-side array
curvature with MC, while their no-MC difference is consistent with zero under hemispherical isotropic scattering. Results based on the 3rd Generation Partnership Project clustered delay line-B channel angular/power profile, together with colored-noise sensitivity analysis, further show that the effects of
curvature and receive-side coupling depend on the propagation angular distribution and receiver-noise covariance. These findings highlight the importance of jointly accounting for array geometry, mutual coupling, and receiver noise when evaluating curved holographic multiple-input multiple-output links.
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All authors Hang Lin, Shu Sun, Hangsong Yan, Qiuming Zhu
Signal Processing
Pinching-Antenna Differential Spatial Modulation with RSSI-Assisted Selection and Low-Complexity Detection: Methods and Performance Analysis
Yusuf Akar, Mahmoud Raeisi, Henk Wymeersch et al. (+2 more)
Pinching
antenna (PA) systems provide macroscopic spatial reconfigurability by enabling signal radiation from favorable locations along a dielectric waveguide. Exploiting this flexibility, however, typically requires channel knowledge for PA configuration, which can introduce substantial overhead in dynamic propagation environments. This paper proposes PA-assisted differential spatial modulation (PA-DSM), enabling data transfer with spatial index selection and non-coherent data detection without instantaneous channel state information (CSI) at the receiver. To exploit PA reconfigurability while preserving this CSI-efficient operation, we introduce a slow-timescale received signal strength indicator (RSSI)-assisted selection strategy that identifies a favorable subset of PAs from scalar power measurements. We further specialize a two-stage low-complexity maximum-likelihood (LC-ML) detector to phase shift keying (PSK) signaling, obtaining closed-form symbol decisions that remove the joint modulation-combination search while preserving the minimizer of exhaustive differential detection. Furthermore, a moment generating function (MGF)-based pairwise error analysis and the corresponding bit error probability (BEP) bound are developed under normalized common-K Rician fading, revealing how the difference rank and spectrum, number of receive
antennas, and previous-state-dependent line-of-sight (LoS)
alignment affect the error performance. Numerical results across representative indoor-office, indoor-factory, and street-canyon sixth-generation (6G) Frequency Range 3 (FR3) scenarios validate the analytical trends and quantify when PA-DSM outperforms coherent
benchmark schemes whose channel estimates age between pilot transmissions.
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All authors Yusuf Akar, Mahmoud Raeisi, Henk Wymeersch, Mikko Valkama, Ertugrul Basar
Systems and Control
From Demand to System Co-Shaping: A Review of User Roles in Transportation Systems
Fangting Zhou, Balazs Kulcsar and Jelena Andric
Users are central to transportation systems, yet their roles are often simplified in transportation modeling and decision-making. Conventional approaches primarily represent users through demand-related inputs, such as trip flows, delivery requests, and charging loads. However, digital, electrified, and platform-based services create more direct user-system interactions, with users responding to prices, incentives, service availability, and information in ways that can influence operational and planning decisions. This review develops a concept-oriented, taxonomy-based synthesis of how user-related information is represented and used in transportation decision-making. Rather than organizing the literature primarily by transport domain, we distinguish three user roles: passive demand, operational
agents, and system co-shapers. Drawing on studies across transportation electrification, urban logistics, shared mobility,
multimodal transport, and Mobility-as-a-Service (MaaS), the review examines how user preferences, flexibility, willingness to pay, acceptance, participation, and feedback are incorporated into modeling and decision frameworks. The synthesis shows that progress has been made in measuring and predicting user behavior, but user-related variables are still often used mainly for behavioral analysis rather than as actionable inputs to system decisions. Their integration into operational decision-making remains incomplete, and systematic feedback from operational user responses and outcomes to long-term planning is even more limited. Key challenges include closing the loop between operation and planning, modeling heterogeneous and strategic users, integrating optimization with user acceptance, and ensuring
privacy, equity, and deployability. Together, these findings point toward a shift from user-behavior analysis to user-informed and user-driven transportation system design.
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Systems and Control
Vertiport Design Methodology and Capacity Analysis
Emin Burak Onat, Jean-Luc Lupien and Raja Sengupta
This paper presents a comprehensive methodology for vertiport design and capacity analysis using
stochastic queueing models. Vertiport capacity is defined as the maximum steady-state throughput of aircraft per hour while maintaining a specified maximum queuing delay with high probability. We introduce a two-node queueing network model with a finite buffer to analytically derive mean queue lengths and delays at singleTLOF vertiports, supported by a simulation model that validates these findings under various operational conditions. Our analyses reveal that vertiport capacity is significantly constrained by the interactions between takeoff/landing operations and turnaround activities. Capacity is further reduced by the variance in these operational parameters, with deterministic times substantially enhancing capacity. The study shows that vertiport throughput is typically around 50% of the theoretical maximum departure capacity in a Markovian model, decreasing sharply as confidence in maximum queuing delay increases. Non-Markovian models with adequate parking pads achieve full TLOF capacity. Additionally, our methodology identifies the optimal number of parking pads required to maximize throughput per unit area. The findings provide insights for designing vertiport infrastructure to support scalable and efficient UAM deployment.
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Signal Processing
Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin
Hongning Ruan, Zhaoyang Zhang, Zirui Chen et al. (+3 more)
The acquisition of high-dimensional channel state information (CSI) in wireless
MIMO-OFDM communications usually requires high pilot overhead, or relies on accurate and complete positional or environmental information. In this paper, we propose a geometry-aided channel deduction (GCD) approach, which utilizes an uncalibrated digital twin (DT) with only approximate environmental geometry and positions to assist the channel acquisition. The key rationale behind is that, even imprecise
geometric information, which can be easily obtained in advance through
radio sensing technologies or existing geographic databases, provides certain structural features about the current channel; meanwhile, the coarse instantaneous channel estimates using only a small amount of pilots provide dedicated information that aligns with the channel structure and further compensates for the geometry inaccuracy and other channel unknowns. To this end, we first extract
geometric features from the DT, which contain only simple structural information of the channel. Then we propose random
prompt augmentation, a novel method to generate an appropriate
prompt that converts
geometric multi-path structure into a CSI-like representation while suppressing the disturbance of other unknown channel parameters. The
prompt is then fused with the pilot-based instantaneous channel estimate via a channel deduction network. To further enhance the network's versatility, we incorporate pilot configurations into the existing learning architecture to support variable pilot patterns. Comprehensive experiments validate the superiority of the proposed method, which demonstrates high channel acquisition quality, low pilot overhead, and strong robustness. Furthermore, the structural
prompt also serves as scenario-related context, enabling our approach to generalize well in new scenarios.
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All authors Hongning Ruan, Zhaoyang Zhang, Zirui Chen, Ziqing Xing, Zhaohui Yang, Mérouane Debbah
Signal Processing · Cross-listed from math.OC
TAPS: Target-Aware Permanent Sampling for Graph Diffusion
Michelle Lin and Laura P. Schaposnik
Permanent air-quality networks are expensive to install and maintain, yet many decisions depend on one future regional exposure rather than the complete pollution field. We introduce Target-Aware Permanent Sampling (TAPS), a graph-
diffusion framework for selecting permanent locations observed repeatedly over time, that can reduce unnecessary sensor installations, maintenance, and cost while preserving the information needed for future air-quality decisions, subject to validation with the
estimator that will use the network. We formulate the permanent space-time sampling problem and show that, in the noiseless model, recovery of one prescribed target can require less information than full-state identification, for which we give an explicit lower bound on the permanent-location count. We also derive a greedy rule whose marginal gain factors into raw target response and a finite-update correction. We evaluate TAPS on regulatory air-quality networks in California, Canada, and England. In each case the prescribed regional target becomes numerically recoverable at permanent-location budgets well below those required to identify the retained state, and TAPS attains lower regularized target risk than target-weight,
geometric, and design-based placements. A blind prospective study further shows that the criterion can be applied before candidate sites have any ground-monitor history, using exogenous environmental
covariates alone.
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Systems and Control
Ancillary Services in High-Renewable Power Systems: Comparing Market Design, Emerging Trends, and Future Challenges
Andreas Makrides, Andrey Churkin, Fei Teng et al. (+1 more)
Rapid growth in renewable generation is changing power-system operation and increasing the importance of ancillary services for security and reliability. As inverter-based resources displace conventional synchronous generation, system operators must procure a broader set of services spanning five categories: frequency control and reserves, voltage and reactive power support, system stability, restoration, and congestion management. This paper compares ancillary-service
market design in Great Britain, Germany, Denmark, Italy, California, Texas, and Australia. It examines how these systems define, procure, remunerate, and coordinate services as variable renewable generation increases. The review develops a consistent product-level classification, provides comparable timing visualisations of frequency-control and reserve products, and maps implementation modalities across services and systems. Four trends emerge: faster and more granular frequency-response products, more explicit procurement of stability capabilities including inertia, system strength, and dynamic voltage support, broader participation by storage, demand-side resources, and other inverter-based resources, and greater use of locational, targeted, and technology-neutral procurement. Challenges include limited harmonisation, incomplete valuation of flexibility and stability capabilities whose value depends on location and operating conditions, barriers to participation by distributed and emerging resources, and insufficient coordination between
market and operational arrangements. The comparison finds no convergence towards a single
market design and supports differentiated but coordinated ancillary-service frameworks reflecting each service's technical, temporal, and locational characteristics.
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All authors Andreas Makrides, Andrey Churkin, Fei Teng, Pierre Pinson
Systems and Control
Decentralized Continuous-Time Power Dispatch for Integrated Heat and Power Systems via Bernstein-Galerkin Equivalent Projection
Jie Deng, Zhigang Li, J. H. Zheng et al. (+1 more)
Decentralized power dispatch for integrated heat and power systems (IHPSs) involves coordinating electric power systems (EPSs) and district heating networks (DHNs) while preserving
privacy. The existing methods rely mainly on discretetime DHN models and constant-flow operations, limiting the ability to exploit thermal flexibility. Enabling variable-flow and variable-temperature (VF-VT) operations requires thermal-hydraulic coupling to be addressed together with continuous-time thermal dynamics. However, the simultaneous variations in mass flows and temperature lead to a nonconvex IHPS model, for which conventional decomposition methods are difficult to apply. This paper proposes a decentralized continuous-time dispatch framework using Bernstein-Galerkin equivalent projection. For a prescribed mass flow trajectory, DHN thermal dynamics are formulated in the Bernstein space and projected onto boundary variables, yielding an equivalent feasible-region model without disclosing the internal DHN
topology or states. The equivalent model and DHN subproblem are derived from a unified Bernstein-Galerkin formulation, ensuring a consistent thermal state representation throughout the EPS-DHN coordination procedure. A safeguarded Anderson prediction scheme further accelerates the alternating coordination process by using historical updates and fixed-point residuals to predict the next input without additional subproblem solving steps. Numerical results show the proposed method preserves thermal state consistency, exploits VF-VT flexibility, and improves economic and computational performance.
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All authors Jie Deng, Zhigang Li, J. H. Zheng, Yue Chen
Signal Processing · Cross-listed from cs.MA
LLM-Based Multi-Agent Systems over Wireless Networks: A Joint Agent–Network Design Perspective
Chao Hu, Yuan Guo, Guanlin Wu et al. (+3 more)
As large
language models (LLMs) evolve from standalone models into collaborative
agents embedded in physical systems, their
reasoning and execution are increasingly distributed across wireless edge nodes. In this setting, wireless networks are experiencing a paradigm shift from only providing data connectivity to supporting the multi-
agent reasoning workflow itself. The task performance of such network-constrained LLM-based multi-
agent systems (MASs) is jointly affected by the multi-
agent reasoning dependencies as well as the underlying network connectivity and edge resources. This coupling gives rise to various technical challenges, including the metric misalignment and message redundancy, state inconsistency and
topology mismatch, as well as resource limitation and trust discontinuity. To address these challenges, this article develops a novel joint
agent–network design perspective that coordinates decisions on both sides of the system. Specifically, we present the joint design of
agent–interaction scheduling and resource allocation, the message selection-transmission co-design, as well as the joint
agent–network
topology design and workload–resource allocation. Furthermore, we consider the network-verified provenance that is linked with
agent-side information-flow control to constrain how received information affects subsequent operations. An illustrative vehicle-to-everything (V2X) case study shows that jointly adapting
agent-side interaction decisions and network operations improves task completion under communication and edge-resource constraints, outperforming the conventional
agent-only and wireless-only separate designs.
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All authors Chao Hu, Yuan Guo, Guanlin Wu, Yueling Che, Han Hu, Jie Xu
Systems and Control · Cross-listed from quant-ph
Towards Scalable Quantum Unit Commitment in Power Systems: Encoding and Complexity Reduction Techniques
Marc Carrillo-MuÑoz, Sergio MartÍnez-Hermida, Sara Barja-MartÍnez et al. (+2 more)
This paper develops a
Quantum optimisation Unit Commitment (UC) framework that benefits from Pauli Correlation Encoding (PCE) as a
qubit reduction preprocessing technique and Sample-based
Quantum Diagonalisation (SQD) as a hybrid post-processing refinement method. The UC problem is formulated in binary form, then transformed into a QUBO problem and mapped onto an Ising representation suitable for adiabatic
quantum evolution within the Qibo framework. This study analyses how PCE and SQD techniques affect
qubit scalability, computational effort, feasibility, and probability of recovering the optimal solution as the number of generators increases. The results show that PCE reduces the effective size of the encoded problem, SQD improves the concentration of probability on feasible and optimal solutions, and their combined application provides the best overall behaviour in several scalable case studies, achieving more reliable solutions with lower effective computational cost than the unreduced formulation. The repository is available open source in Github (link is not yet available until work is published. However, if requested by the reviewers, we can make it open for a scific time window, if requested).
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All authors Marc Carrillo-MuÑoz, Sergio MartÍnez-Hermida, Sara Barja-MartÍnez, Antonio E. SaldaÑa GonzÁlez, MÒnica AragÜÉs-PeÑalba
Image and Video Processing · Cross-listed from cs.CV
Salt++: Context-Aligned Post-Training for Few-Step Streaming Multimodal Generation
Xingtong Ge, Yutong Wang, Lunjie Zhu et al. (+7 more)
Few-step streaming audio–
video generation requires both causal modeling and step
distillation, yet standard training recipes face two context-related challenges. Teacher forcing pairs clean history with a noisy target, but supervises predictive contextual representations only indirectly through velocity prediction. Meanwhile, directly reusing bidirectional score models in causal Distribution Matching
Distillation (DMD) creates a mismatch between generation and scoring contexts. We address these challenges with Salt++, a two-stage post-training framework comprising Causal Self-Flow (CSF) and context-aligned autoregressive DMD. CSF exploits contextual information asymmetry by varying the history while keeping the noisy target fixed: a noise-mixed-history student aligns its intermediate representations with those of a clean-history exponential-moving-average teacher. This self-supervised signal encourages the student to extract semantic information and improves cross-modal
alignment. Context-aligned AR DMD shares the causal mask and prefix across generator sampling, fake-score training, and real-score evaluation to match generated and reference distributions under a block-conditional KL objective. With calibrated teacher guidance, it performs clean-prefix few-step
distillation and then adapts to generated histories without switching objectives or requiring separate consistency
distillation. At 480p, Salt++ improves visual and motion quality by 57% and 45% over OmniForcing on JavisBench under the same 4-step causal setting. A separate scale-wise post-training stage extends Salt++ to 4-step $1664\times960$ generation, outperforming bidirectional LTX-2 on six of seven reported metrics. Project page: https://xingtongge.github.io/Saltpp
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All authors Xingtong Ge, Yutong Wang, Lunjie Zhu, Haitao Lin, Fangyu Lin, Yushi Huang, Xin Zhang, Yi Zhang, Yu Liu, Jun Zhang
Signal Processing
Periodicity and image registration for yarn path extraction in large 3D textiles
Hafsa El Herichi, Arturo Mendoza, Yanneck Wielhorski et al. (+2 more)
Accurate identification of yarns in X-ray computed
tomography volumes remains a critical and complex step in generating high-fidelity numerical models of woven composites. This work introduces a tracking framework that leverages the intrinsic periodicity of woven architectures to transform a complex, large-scale segmentation problem into the annotation of a single representative unit
cell. The approach first exploits the periodic nature of the weave to extract a representative unit
cell from the volumetric data and segment it to provide a reference description of the yarn geometry. Digital Volume Correlation (DVC) is then performed between an idealised periodic volume obtained by replication of the unit
cell and the real composite volume, yielding a three-dimensional displacement field that captures
geometric deviations from ideal periodicity. The unit-
cell yarn segmentation is propagated to the full volume by exploiting the periodicity of the reference volume, and the annotations are transported to the actual volume using the DVC-derived displacement field. Results obtained on real composite
datasets demonstrate the ability of the method to accurately recover warp and weft yarn architectures with minimal input, opening new perspectives for efficient and scalable textile composite characterisation.
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All authors Hafsa El Herichi, Arturo Mendoza, Yanneck Wielhorski, Hugues Talbot, Stéphane Roux
Audio and Speech Processing
Louder, Longer, Livelier: Acoustic Shortcuts and Underspecified Rationales in Speech LLM Judges
Mingyue Huo, Shivam Mehta, Bhavin Jawade et al. (+2 more)
LLM-as-a-judge is widely used for evaluating text, but extending this paradigm to
speech requires models to interpret acoustic as well as linguistic evidence. This introduces a modality-specific risk: a
speech judge may treat a perceptually salient cue as evidence of quality even when that cue is irrelevant to the target criterion or receives more weight than human listeners give it. We call this behavior an acoustic shortcut. To study it, we audit six
speech LLM judges using controlled manipulations of intensity, content richness, and emotional delivery. We evaluate both pointwise scoring and pairwise comparison, using human preference calibration to interpret the results. The judges consistently reward louder audio, prefer content-rich
speech more strongly than human listeners do, and map emotional delivery into quality preferences. These effects are most visible in pairwise comparison, while pointwise scores often obscure them. More concerningly, the accompanying rationales rarely identify the acoustic cue that changes a judgment and instead repeatedly rely on a limited vocabulary, leaving them acoustically underspecified. Together, these findings show that reliable
speech judges must both resist acoustic shortcuts and ground their rationales in the acoustic evidence behind their decisions. To support reproducibility and future audits, we also release SpeechJudgeAudit, the controlled stimuli and evaluation tools used in this study.
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All authors Mingyue Huo, Shivam Mehta, Bhavin Jawade, Yinghong Lan, Haoqi Li
Systems and Control
An Energy-Based Framework for Transient Stability of Grid-Forming Converter Networks With Current Limiting
Wei Zhou and Yunjie Gu
Existing analyses for the transient stability of grid-forming (GFM) converters under current limiting mainly address single-converter systems, while network-level transient stability analysis remains challenging. This paper develops a network energy construction that incorporates current limiting while retaining a provable dissipation property. This structure enables Lyapunov-based analysis consistent with classical direct method for synchronous-machine based systems, without switching between mode-dependent energy functions. A two-GFM example using a reactive circular current limiter and impedance insertion illustrates the proposed framework. Electromagnetic transient simulations support the theoretical results.
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Systems and Control · Cross-listed from cs.LG
Safe-by-Design Learning via Energy-based Neural Networks
Simone Betteti, Morteza Lahijanian and Luca Laurenti
Learning neural-network models of dynamical systems with safety guarantees is a fundamental requirement for their deployment in safety-critical settings. Safety is commonly established by proving the invariance of a desired subset in state-space, ensuring that every trajectory initialized in this subset remains confined to it for all time under admissible inputs. Existing frameworks, however, either rely on computationally expensive post-hoc verification or employ safety-enforcing mechanisms without formal correctness guarantees. In this paper, we introduce a novel neural architecture grounded in energy-based modern Hopfield networks to guarantee safety-by-design while retaining sufficient expressiveness to model complex nonlinear dynamics. Specifically, we integrate modern Hopfield networks with a port-
Hamiltonian neural ODE, enabling by design the construction of barrier functions yielding explicit admissible-input sets and quantitative robustness radii. Across several
benchmarks, including an 12-dimensional nanodrone model, our framework achieves state-of-the-art performance while producing certified invariant sets that are more robust to external solicitations than comparable existing approaches.
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Image and Video Processing
Ternary Visible Light Communication Using Event-Based Vision Sensors
Sotaro Kuremoto, Junya Hara, Hiroshi Higashi et al. (+1 more)
This paper proposes a ternary visible light communication method using an event-based
vision sensor (EVS) and a
liquid crystal display (LCD). The throughput of optical camera communication (OCC) is limited by the frame rates of conventional frame-based cameras, and EVSs are expected to overcome this limitation because their pixels asynchronously trigger events with polarity in response to brightness changes, with a temporal resolution on the order of microseconds. However, existing event-based OCC relies on binary signaling that uses only the presence or absence of events and leaves the polarity unused. The proposed method maps the three brightness transitions (increase, decrease, and no change) to ternary symbols, thereby increasing the information carried per symbol. At the transmitter, the LCD displays a marker in which the transmitted data are encoded; at the receiver, the EVS captures the marker and records the brightness changes as events with positive and negative polarities. To suppress errors caused by the timing mismatch between display updates at the transmitter and demodulation at the receiver, we also propose a demodulation
algorithm based on frame-transition detection. Experimental results demonstrate that the proposed method achieves a throughput of 336.5 kbps, approximately 150% higher than that of its binary counterpart.
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All authors Sotaro Kuremoto, Junya Hara, Hiroshi Higashi, Yuichi Tanaka
Systems and Control
Receding Horizon Control and Dissipativity - Optimal Control, Games and Uncertainty
Sophie Hall, Jonas Schießl, Sergio Grammatico et al. (+1 more)
We provide a tutorial overview of receding horizon control across deterministic,
stochastic, multi-
agent, and game-theoretic settings. We contrast the minimizer of an optimal control problem with the
equilibrium of a game in terms of cost and constraint handling to motivate the difference of MPC schemes involving multiple
agents/players. Interestingly, dissipativity theory and the turnpike property have turned out to be a unifying thread and system-theoretic backbone across all these schemes. This paper is the first to give an overview of results, summarizing fundamental insights, drawing parallels, and highlighting technical differences in dissipativity and turnpike analysis across economic, game theoretic, and
stochastic settings. We also explore recent trends in numerical methods for games. We conclude by pointing to a list of open challenges.
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All authors Sophie Hall, Jonas Schießl, Sergio Grammatico, Timm Faulwasser