Papers

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Electrical Engineering and Systems Science

eess Watch Electrical Engineering and Systems Science

Friday 16 January

Signal Processing

Hybrid Resource Allocation Scheme for Bistatic ISAC with Data Channels

Marcus Henninger, Lucas Giroto, Ahmed Elkelesh et al. (+1 more)

Bistatic integrated sensing and communication (ISAC) enables efficient reuse of the existing cellular infrastructure and is likely to play an important role in future sensing networks. In this context, ISAC using the data channel is a promising approach to improve the bistatic sensing performance compared to relying solely on pilots. One of the challenges associated with this approach is resource allocation: the communication link aims to transmit higher modulation order (MO) symbols to maximize the throughput, whereas a lower MO is preferable for sensing to achieve a higher signal-to-noise ratio in the radar image. To address this conflict, this paper introduces a hybrid resource allocation scheme. By placing lower MO symbols as pseudo-pilots on a suitable sensing grid, we enhance the bistatic sensing performance while only slightly reducing the spectral efficiency of the communication link. Simulation results validate our approach against different baselines and provide practical insights into how decoding errors affect the sensing performance.

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All authors Marcus Henninger, Lucas Giroto, Ahmed Elkelesh, Silvio Mandelli

Image and Video Processing

Generation of Chest CT pulmonary Nodule Images by Latent Diffusion Models using the LIDC-IDRI Dataset

Kaito Urata, Maiko Nagao, Atsushi Teramoto et al. (+3 more)

Recently, computer-aided diagnosis systems have been developed to support diagnosis, but their performance depends heavily on the quality and quantity of training data. However, in clinical practice, it is difficult to collect the large amount of CT images for specific cases, such as small cell carcinoma with low epidemiological incidence or benign tumors that are difficult to distinguish from malignant ones. This leads to the challenge of data imbalance. In this study, to address this issue, we proposed a method to automatically generate chest CT nodule images that capture target features using latent diffusion models (LDM) and verified its effectiveness. Using the LIDC-IDRI dataset, we created pairs of nodule images and finding-based text prompts based on physician evaluations. For the image generation models, we used Stable Diffusion version 1.5 (SDv1) and 2.0 (SDv2), which are types of LDM. Each model was fine-tuned using the created dataset. During the generation process, we adjusted the guidance scale (GS), which indicates the fidelity to the input text. Both quantitative and subjective evaluations showed that SDv2 (GS = 5) achieved the best performance in terms of image quality, diversity, and text consistency. In the subjective evaluation, no statistically significant differences were observed between the generated images and real images, confirming that the quality was equivalent to real clinical images. We proposed a method for generating chest CT nodule images based on input text using LDM. Evaluation results demonstrated that the proposed method could generate high-quality images that successfully capture specific medical features.

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All authors Kaito Urata, Maiko Nagao, Atsushi Teramoto, Kazuyoshi Imaizumi, Masashi Kondo, Hiroshi Fujita

Image and Video Processing

Visual question answering-based image-finding generation for pulmonary nodules on chest CT from structured annotations

Maiko Nagao, Kaito Urata, Atsushi Teramoto et al. (+3 more)

Interpretation of imaging findings based on morphological characteristics is important for diagnosing pulmonary nodules on chest computed tomography (CT) images. In this study, we constructed a visual question answering (VQA) dataset from structured data in an open dataset and investigated an image-finding generation method for chest CT images, with the aim of enabling interactive diagnostic support that presents findings based on questions that reflect physicians' interests rather than fixed descriptions. In this study, chest CT images included in the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) datasets were used. Regions of interest surrounding the pulmonary nodules were extracted from these images, and image findings and questions were defined based on morphological characteristics recorded in the database. A dataset comprising pairs of cropped images, corresponding questions, and image findings was constructed, and the VQA model was fine-tuned on it. Language evaluation metrics such as BLEU were used to evaluate the generated image findings. The VQA dataset constructed using the proposed method contained image findings with natural expressions as radiological descriptions. In addition, the generated image findings showed a high CIDEr score of 3.896, and a high agreement with the reference findings was obtained through evaluation based on morphological characteristics. We constructed a VQA dataset for chest CT images using structured information on the morphological characteristics from the LIDC-IDRI dataset. Methods for generating image findings in response to these questions have also been investigated. Based on the generated results and evaluation metric scores, the proposed method was effective as an interactive diagnostic support system that can present image findings according to physicians' interests.

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All authors Maiko Nagao, Kaito Urata, Atsushi Teramoto, Kazuyoshi Imaizumi, Masashi Kondo, Hiroshi Fujita

Image and Video Processing

Convolutions Need Registers Too: HVS-Inspired Dynamic Attention for Video Quality Assessment

Mayesha Maliha R. Mithila and Mylene C.Q. Farias

No-reference video quality assessment (NR-VQA) estimates perceptual quality without a reference video, which is often challenging. While recent techniques leverage saliency or transformer attention, they merely address global context of the video signal by using static maps as auxiliary inputs rather than embedding context fundamentally within feature extraction of the video sequence. We present Dynamic Attention with Global Registers for Video Quality Assessment (DAGR-VQA), the first framework integrating register-token directly into a convolutional backbone for spatio-temporal, dynamic saliency prediction. By embedding learnable register tokens as global context carriers, our model enables dynamic, HVS-inspired attention, producing temporally adaptive saliency maps that track salient regions over time without explicit motion estimation. Our model integrates dynamic saliency maps with RGB inputs, capturing spatial data and analyzing it through a temporal transformer to deliver a perceptually consistent video quality assessment. Comprehensive tests conducted on the LSVQ, KonVid-1k, LIVE-VQC, and YouTube-UGC datasets show that the performance is highly competitive, surpassing the majority of top baselines. Research on ablation studies demonstrates that the integration of register tokens promotes the development of stable and temporally consistent attention mechanisms. Achieving an efficiency of 387.7 FPS at 1080p, DAGR-VQA demonstrates computational performance suitable for real-time applications like multimedia streaming systems.

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Audio and Speech Processing · Cross-listed from cs.SD

WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem

Chengyou Wang, Mingchen Shao, Jingbin Hu et al. (+11 more)

Speech processing for low-resource dialects remains a fundamental challenge in developing inclusive and robust speech technologies. Despite its linguistic significance and large speaker population, the Wu dialect of Chinese has long been hindered by the lack of large-scale speech data, standardized evaluation benchmarks, and publicly available models. In this work, we present WenetSpeech-Wu, the first large-scale, multi-dimensionally annotated open-source speech corpus for the Wu dialect, comprising approximately 8,000 hours of diverse speech data. Building upon this dataset, we introduce WenetSpeech-Wu-Bench, the first standardized and publicly accessible benchmark for systematic evaluation of Wu dialect speech processing, covering automatic speech recognition (ASR), Wu-to-Mandarin translation, speaker attribute prediction, speech emotion recognition, text-to-speech (TTS) synthesis, and instruction-following TTS (instruct TTS). Furthermore, we release a suite of strong open-source models trained on WenetSpeech-Wu, establishing competitive performance across multiple tasks and empirically validating the effectiveness of the proposed dataset. Together, these contributions lay the foundation for a comprehensive Wu dialect speech processing ecosystem, and we open-source proposed datasets, benchmarks, and models to support future research on dialectal speech intelligence.

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All authors Chengyou Wang, Mingchen Shao, Jingbin Hu, Zeyu Zhu, Hongfei Xue, Bingshen Mu, Xin Xu, Xingyi Duan, Binbin Zhang, Pengcheng Zhu, Chuang Ding, Xiaojun Zhang, Hui Bu, Lei Xie

Signal Processing · Cross-listed from cs.IT

PEMNet: Towards Autonomous and Enhanced Environment-Aware Mobile Networks

Lei Li, Yanqing Xu, Ye Xue et al. (+4 more)

With 5G deployment and the evolution toward 6G, mobile networks must make decisions in highly dynamic environments under strict latency, energy, and spectrum constraints. Achieving this goal, however, depends on prior knowledge of spatial-temporal variations in wireless channels and traffic demands. This motivates a joint, site-specific representation of radio propagation and user demand that is queryable at low online overhead. In this work, we propose the perception embedding map (PEM), a localized framework that embeds fine-grained channel statistics together with grid-level spatial-temporal traffic patterns over a base station's coverage. PEM is built from standard-compliant measurements – such as measurement report and scheduling/quality-of-service logs – so it can be deployed and maintained at scale with low cost. Integrated into PEM, this joint knowledge supports enhanced environment-aware optimization across PHY, MAC, and network layers while substantially reducing training overhead and signaling. Compared with existing site-specific channel maps and digital-twin replicas, PEM distinctively emphasizes (i) joint channel-traffic embedding, which is essential for network optimization, and (ii) practical construction using standard measurements, enabling network autonomy while striking a favorable fidelity-cost balance.

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All authors Lei Li, Yanqing Xu, Ye Xue, Feng Yin, Chao Shen, Rui Zhang, Tsung-Hui Chang

Signal Processing · Cross-listed from astro-ph.IM

A Novel, Beam-based Formalism for Active Impedance of Phased Arrays

M. Deng and J. Wu

The active impedance is a fundamental parameter for characterizing the behavior of large, uniform phased array antennas. However, its conventional calculation via the mutual impedance matrix (or the scattering matrix) offers limited physical intuition and can be computationally intensive. This paper presents a novel derivation of the active impedance directly from the radiated beam pattern of such arrays. This approach maps the scan-angle variation of the active impedance directly to the intrinsic angular variation of the beam, providing a more intuitive physical interpretation. The theoretical derivation is straightforward and rigorous. The validity of the proposed equation is conclusively confirmed through full-wave simulations of a prototype array. This work establishes a new and more intuitive framework for understanding, analyzing and accurately measuring the scan-dependent variations in phased arrays, which is one of the main challenges in modern phased array designs. Consequently, this novel formalism is expected to expedite and simplify the overall design and optimization process for next-generation, large-scale uniform phased arrays.

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Signal Processing

Uni-Fi: Integrated Multi-Task Wi-Fi Sensing

Mengning Li and Wenye Wang

Wi-Fi sensing technology enables non-intrusive, continuous monitoring of user locations and activities, which supports diverse smart home applications. Since different sensing tasks exhibit contextual relationships, their integration can enhance individual module performance. However, integrating sensing tasks across different studies faces challenges due to the absence of: 1) a unified architecture that captures the fundamental nature shared across diverse sensing tasks, and 2) an extensible pipeline that accommodates future sensing methodologies. This paper presents UNI-FI, an extensible framework for multi-task Wi-Fi sensing integration. This paper makes the following contributions: 1) we propose a unified theoretical framework that reveals fundamental differences between single-task and multi-task sensing; 2) we develop a scalable sensing pipeline that automatically generates a multi-task sensing solver, enabling seamless integration of multiple sensing models. Experimental results show that UNI-FI achieves robust performance across tasks, with a median localization error of approximately 0.54 m, 98.34% accuracy for activity classification, and 98.57% accuracy for presence detection.

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Signal Processing

Delay-Aware Task Offloading for Heterogeneous VLC-RF-based Vehicular Fog Computing

Nan An, Hongyi He, Fang Yang et al. (+4 more)

Vehicular fog computing (VFC) is a promising paradigm for reducing the computation burden of vehicles, thus supporting delay-sensitive services in next-generation transportation networks. However, traditional VFC schemes rely on radio frequency (RF) communications, which limits their adaptability for dense vehicular environments. In this paper, a heterogeneous visible light communication (VLC)-RF architecture is designed for VFC systems to facilitate efficient task offloading. Specifically, computing tasks are dynamically partitioned and offloaded to idle vehicles via both VLC and RF links, thereby fully exploiting the interference resilience of VLC and the coverage advantage of RF. To minimize the average task processing delay (TPD), an optimization problem of task offloading and computing resource allocation is formulated, and then solved by the developed residual-based majorization-minimization (RBMM) algorithm. Simulation results confirm that the heterogeneous VLC-RF architecture with the proposed algorithm achieves a 15% average TPD reduction compared to VFC systems relying solely on VLC or RF.

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All authors Nan An, Hongyi He, Fang Yang, Chang Liu, Jian Song, Zhu Han, Binbin Zhu

Systems and Control

Determining optimal thermal energy storage charging temperature for cooling using integrated building and coil modeling

Ju-Hong Oh, Seon-In Kim and Eui-Jong Kim

Thermal energy storage (TES) systems coupled with heat pumps offer significant potential for improving building energy efficiency by shifting electricity demand to off-peak hours. However, conventional operating strategies maintain conservatively low chilled water temperatures throughout the cooling season, a practice that results in suboptimal heat pump performance. This study proposes a physics-based integrated simulation framework to determine the maximum feasible chilled water supply temperature while ensuring cooling stability. The framework integrates four submodels: relative humidity prediction, dynamic cooling load estimation, cooling coil performance prediction, and TES discharge temperature prediction. Validation against measured data from an office building demonstrates reliable accuracy across all sub-models (e.g., CVRMSE of 9.3% for cooling load and R2 of 0.91 for peak-time discharge temperature). The integrated simulation reveals that the proposed framework can increase the daily initial TES charging temperature by an average of 2.55 {\deg}C compared to conventional fixed-temperature operation, enabling the heat pump to operate at a higher coefficient of performance. This study contributes a practical methodology for optimizing TES charging temperatures in building heating, ventilation, and air conditioning (HVAC) systems while maintaining indoor setpoint temperatures.

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Systems and Control

A monolithic fabrication platform for intrinsically stretchable polymer transistors and complementary circuits

Yujia Yuan, Chuanzhen Zhao, Margherita Ronchini et al. (+20 more)

Soft, stretchable organic field-effect transistors (OFETs) can provide powerful on-skin signal conditioning, but current fabrication methods are often material-specific: each new polymer semiconductor (PSC) requires a tailored process. The challenge is even greater for complementary OFET circuits, where two PSCs must be patterned sequentially, which often leads to device degradation. Here, we introduce a universal, monolithic photolithography process that enables high-yield, high-resolution stretchable complementary OFETs and circuits. This approach is enabled by a process-design framework that includes (i) a direct, photopatternable, solvent-resistant, crosslinked dielectric/semiconductor interface, (ii) broadly applicable crosslinked PSC blends that preserve high mobility, and (iii) a patterning strategy that provides simultaneous etch masking and encapsulation. Using this platform, we achieve record integration density for stretchable OTFTs (55,000 cm^-2), channel lengths down to 2 um, and low-voltage operation at 5 V. We demonstrate photopatterning across multiple PSC types and realize complementary circuits, including 3 kHz stretchable ring oscillators, the first to exceed 1 kHz and representing more than a 60-fold increase in stage switching speed over the state of the art. Finally, we demonstrate the first stretchable complementary OTFT neuron circuit, where the output frequency is modulated by the input current to mimic neuronal signal processing. This scalable approach can be readily extended to diverse high-performance stretchable materials, accelerating the development and manufacturing of skin-like electronics.

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All authors Yujia Yuan, Chuanzhen Zhao, Margherita Ronchini, Yuya Nishio, Donglai Zhong, Can Wu, Hyukmin Kweon, Zehao Sun, Rachael K. Mow, Yuran Shi, Lukas Michalek, Haotian Wu, Qianhe Liu, Weichen Wang, Yating Yao, Zelong Yin, Junyi Zhao, Zihan He, Ke Chen, Ruiheng Wu, Jiuyun Shi, Jian Pei, Zhenan Bao

Systems and Control · Cross-listed from cs.LG

Toward Adaptive Grid Resilience: A Gradient-Free Meta-RL Framework for Critical Load Restoration

Zain ul Abdeen, Waris Gill and Ming Jin

Restoring critical loads after extreme events demands adaptive control to maintain distribution-grid resilience, yet uncertainty in renewable generation, limited dispatchable resources, and nonlinear dynamics make effective restoration difficult. Reinforcement learning (RL) can optimize sequential decisions under uncertainty, but standard RL often generalizes poorly and requires extensive retraining for new outage configurations or generation patterns. We propose a meta-guided gradient-free RL (MGF-RL) framework that learns a transferable initialization from historical outage experiences and rapidly adapts to unseen scenarios with minimal task-specific tuning. MGF-RL couples first-order meta-learning with evolutionary strategies, enabling scalable policy search without gradient computation while accommodating nonlinear, constrained distribution-system dynamics. Experiments on IEEE 13-bus and IEEE 123-bus test systems show that MGF-RL outperforms standard RL, MAML-based meta-RL, and model predictive control across reliability, restoration speed, and adaptation efficiency under renewable forecast errors. MGF-RL generalizes to unseen outages and renewable patterns while requiring substantially fewer fine-tuning episodes than conventional RL. We also provide sublinear regret bounds that relate adaptation efficiency to task similarity and environmental variation, supporting the empirical gains and motivating MGF-RL for real-time load restoration in renewable-rich distribution grids.

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Signal Processing

DuTrack: Long-Term Indoor Human Tracking with Dual-Channel Sensing and Inference

Mengning Li and Wenye Wang

Wi-Fi tracking technology demonstrates promising potential for future smart home and intelligent family care. Currently, accurate Wi-Fi tracking methods rely primarily on fine-grained velocity features. However, such velocity-based approaches suffer from the problem of accumulative errors, making it challenging to stably track users' trajectories over a long period of time. This paper presents DuTrack, a fusion-based tracking system for stable human tracking. The fundamental idea is to leverage the ubiquitous acoustic signals in households to rectify the accumulative Wi-Fi tracking error. Theoretically, Wi-Fi sensing in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios can be modeled as elliptical Fresnel zones and hyperbolic zones, respectively. By designing acoustic sensing signals, we are able to model the acoustic sensing zones as a series of hyperbolic clusters. We reveal how to fuse the fields of electromagnetic waves and mechanical waves, and establish the optimization equation. Next, we design a data-driven architecture to solve the aforementioned optimization equation. Experimental results show that the proposed multimodal tracking scheme exhibits superior performance. We achieve a 89.37% reduction in median tracking error compared to model-based methods and a 65.02% reduction compared to data-driven methods.

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Signal Processing · Cross-listed from quant-ph

Noise-Aware Quantum Architecture Search Based on NSGA-II Algorithm

Chenlu Li, Hui Zeng and Dazhi Ding

Quantum architecture search (QAS) has emerged to automate the design of high-performance quantum circuits under specific tasks and hardware constraints. We propose a noise-aware quantum architecture search (NA-QAS) framework based on variational quantum circuit design. By incorporating a noise model into the training of parameterized quantum circuits (PQCs) , the proposed framework identifies the noise-robust architectures. We introduce a hybrid Hamiltonian $\varepsilon$ -greedy strategy to optimize evaluation costs and circumvent local optima. Furthermore, an enhanced variable-depth NSGA-II algorithm is employed to navigate the vast search space, enabling an automated trade-off between architectural expressibility and quantum hardware overhead. The effectiveness of the framework is validated through binary classification and iris multi-classification tasks under a noisy condition. Compared to existing approaches, our framework can search for quantum architectures with superior performance and greater resource efficiency under a noisy condition.

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Signal Processing

Large Wireless Foundation Models: Stronger over Bigger

Xiang Cheng, Boxun Liu, Xuanyu Liu et al. (+1 more)

AI-communication integration is widely regarded as a core enabling technology for 6G. Most existing AI-based physical-layer designs rely on task-specific models that are separately tailored to individual modules, resulting in poor generalization. In contrast, communication systems are inherently general-purpose and should support broad applicability and robustness across diverse scenarios. Foundation models offer a promising solution through strong reasoning and generalization, yet wireless-system constraints hinder a direct transfer of large language model (LLM)-style success to the wireless domain. Therefore, we introduce the concept of large wireless foundation models (LWFMs) and present a novel framework for empowering the physical layer with foundation models under wireless constraints. Specifically, we propose two paradigms for realizing LWFMs, including leveraging existing general-purpose foundation models and building novel wireless foundation models. Based on recent progress, we distill two roadmaps for each paradigm and formulate design principles under wireless constraints. We further provide case studies of LWFM-empowered wireless systems to intuitively validate their advantages. Finally, we characterize the notion of "large" in LWFMs through a multidimensional analysis of existing work and outline promising directions for future research.

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All authors Xiang Cheng, Boxun Liu, Xuanyu Liu, Xuesong Cai

Systems and Control · Cross-listed from cs.CR

Secure Data Bridging in Industry 4.0: An OPC UA Aggregation Approach for Including Insecure Legacy Systems

Dalibor Sain, Thomas Rosenstatter, Olaf Saßnick et al. (+2 more)

The increased connectivity of industrial networks has led to a surge in cyberattacks, emphasizing the need for cybersecurity measures tailored to the specific requirements of industrial systems. Modern Industry 4.0 technologies, such as OPC UA, offer enhanced resilience against these threats. However, widespread adoption remains limited due to long installation times, proprietary technology, restricted flexibility, and formal process requirements (e.g. safety certifications). Consequently, many systems do not yet implement these technologies, or only partially. This leads to the challenge of dealing with so-called brownfield systems, which are often placed in isolated security zones to mitigate risks. However, the need for data exchange between secure and insecure zones persists. This paper reviews existing solutions to address this challenge by analysing their approaches, advantages, and limitations. Building on these insights, we identify three key concepts, evaluate their suitability and compatibility, and ultimately introduce the SigmaServer, a novel TCP-level aggregation method. The developed proof-of-principle implementation is evaluated in an operational technology (OT) testbed, demonstrating its applicability and effectiveness in bridging secure and insecure zones.

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All authors Dalibor Sain, Thomas Rosenstatter, Olaf Saßnick, Christian Schäfer, Stefan Huber

Image and Video Processing · Cross-listed from q-bio.NC

Graph Neural Network Reveals the Cortical Morphology of Local Brain Aging in Normal Cognition and Alzheimer's Disease

Samuel D. Anderson, Jordan Jomsky, Nikhil N. Chaudhari et al. (+4 more)

Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a powerful framework for quantifying anatomical brain aging. Whereas global BA (GBA) summarizes overall brain health, local BA (LBA) provides cortically specific patterns of aging at the subject level. Although previous studies have examined anatomical contributors to GBA, to our knowledge, no framework has been established to estimate LBA using cortical morphology. To address this gap, we introduce a graph neural network (GNN) that uses morphometric features$\unicode{x2013}$cortical thickness, surface area, curvature, gray/white matter intensity ratio (GWR), sulcal depth$\unicode{x2013}$to estimate LBA across the cortical surface at high spatial resolution (mean inter-vertex distance = 1.37 mm). Trained on cortical surface meshes extracted from the MRIs of cognitively normal (CN) adults (N = 14,423), our model achieves lower mean absolute error (MAE) than the existing state-of-the-art while identifying more biologically plausible patterns of aging in Alzheimer's disease (AD) on the ADNI dataset. Association cortices emerge as primary sites of morphometric aging in CNs, whereas mild cognitive impairment is characterized by widespread aging that is pronounced in the parahippocampal gyrus. AD subjects demonstrate significant aging across the entire cortex, particularly within medial temporal regions and associated cortical networks. Feature ablation highlights curvature and GWR as preferentially sensitive to AD pathology. Regional LBA gaps are significantly associated with neuropsychological measures of AD-related cognitive impairment, linking cortical aging patterns to clinical outcomes. These results demonstrate that GNN-based modeling of cortical morphometry enables biologically interpretable mapping of local brain aging with greater interpretability than prior work.

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All authors Samuel D. Anderson, Jordan Jomsky, Nikhil N. Chaudhari, Nahian F. Chowdhury, Xiaoyu Zheng, Andrei Irimia, Alzheimers Disease Neuroimaging Initiative

Thursday 15 January

Systems and Control

Sustainable Vertical Heterogeneous Networks: A Cell Switching Approach with High Altitude Platform Station

Maryam Salamatmoghadasi, Amir Mehrabian, Halim Yanikomeroglu et al. (+1 more)

The rapid growth of radio access networks (RANs) is increasing energy consumption and challenging the sustainability of future systems. We consider a dense-urban vertical heterogeneous network (vHetNet) comprising a high-altitude platform station (HAPS) acting as a super macro base station, a terrestrial macro base station (MBS), and multiple small base stations (SBSs). We propose a HAPS-enhanced cell-switching algorithm that selectively deactivates SBSs based on their traffic load and the capacity and channel conditions of both the MBS and HAPS. The resulting energy-minimization problem, subject to an outage-based quality-of-service (QoS) constraint, is formulated as a mixed-integer nonlinear program and reformulated into a mixed-integer program for efficient solution. Using realistic 3GPP channel models, simulations show substantial energy savings versus All-ON, terrestrial cell switching, and sorting benchmarks. Relative to All-ON, the proposed method reduces power consumption by up to 77% at low loads and about 40% at high loads; a NoQoS variant achieves up to 90% and 47%, respectively. The approach maintains high served-traffic levels and provides a tunable trade-off between power efficiency and outage-based QoS, supporting scalable and sustainable 6G deployments.

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All authors Maryam Salamatmoghadasi, Amir Mehrabian, Halim Yanikomeroglu, Georges Kaddoum

Signal Processing · Cross-listed from quant-ph

Efficient Quantum Circuits for the Hilbert Transform

Henry Zhang and Joseph Li

The quantum Fourier transform and quantum wavelet transform have been cornerstones of quantum information processing. However, for non-stationary signals and anomaly detection, the Hilbert transform can be a more powerful tool, yet no prior work has provided efficient quantum implementations for the discrete Hilbert transform. This letter presents a novel construction for a quantum Hilbert transform in polylogarithmic size and logarithmic depth for a signal of length $N$, exponentially fewer operations than classical algorithms for the same mapping. We generalize this algorithm to create any $d$-dimensional Hilbert transform in depth $O(d\log N)$. Simulations demonstrate effectiveness for tasks such as power systems control and image processing, with exact agreement with classical results.

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Systems and Control · Cross-listed from cs.PF

Balanced allocation: considerations from large scale service environments

Amer Diwan, Prabhakar Raghavan and Eli Upfal

We study d-way balanced allocation, which assigns each incoming job to the lightest loaded among d randomly chosen servers. While prior work has extensively studied the performance of the basic scheme, there has been less published work on adapting this technique to many aspects of large-scale systems. Based on our experience in building and running planet-scale cloud applications, we extend the understanding of d-way balanced allocation along the following dimensions: (i) Bursts: Events such as breaking news can produce bursts of requests that may temporarily exceed the servicing capacity of the system. Thus, we explore what happens during a burst and how long it takes for the system to recover from such bursts. (ii) Priorities: Production systems need to handle jobs with a mix of priorities (e.g., user facing requests may be high priority while other requests may be low priority). We extend d-way balanced allocation to handle multiple priorities. (iii) Noise: Production systems are often typically distributed and thus d-way balanced allocation must work with stale or incorrect information. Thus we explore the impact of noisy information and their interactions with bursts and priorities. We explore the above using both extensive simulations and analytical arguments. Specifically we show, (i) using simulations, that d-way balanced allocation quickly recovers from bursts and can gracefully handle priorities and noise; and (ii) that analysis of the underlying generative models complements our simulations and provides insight into our simulation results.

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Systems and Control

Disturbance Attenuation Regulator II: Stage Bound Finite Horizon Solution

Davide Mannini and James B. Rawlings

This paper develops a generalized finite horizon recursive solution to the discrete time stage bound disturbance attenuation regulator (StDAR) for state feedback control. This problem addresses linear dynamical systems subject to stage bound disturbances, i.e., disturbance sequences constrained independently at each time step through stagewise squared two-norm bounds. The term generalized indicates that the results accommodate arbitrary initial states. By combining game theory and dynamic programming, this work derives a recursive solution for the optimal state feedback policy. The optimal policy is nonlinear in the state and requires solving a tractable convex optimization for the Lagrange multiplier vector at each stage; the control is then explicit. For systems with constant stage bound, we introduce a steady-state StDAR whose solution reduces to a tractable linear matrix inequality (LMI) with empirical computational cost approximately cubic in $n$. Numerical examples illustrate the properties of the solution. This work provides a complete feedback solution to the StDAR for arbitrary initial states. Companion papers address the signal bound disturbance attenuation regulator (SiDAR): the finite horizon solution in Part~I-A and convergence properties in Part~I-B.

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Systems and Control

Disturbance Attenuation Regulator I-B: Signal Bound Convergence and Steady-State

Davide Mannini and James B. Rawlings

This paper establishes convergence and steady-state properties for the signal bound disturbance attenuation regulator (SiDAR). Building on the finite horizon recursive solution developed in a companion paper, we introduce the steady-state SiDAR and derive its tractable linear matrix inequality (LMI) with $O(n^3)$ complexity. Systems are classified as degenerate or nondegenerate based on steady-state solution properties. For nondegenerate systems, the finite horizon solution converges to the steady-state solution for all states as the horizon approaches infinity. For degenerate systems, convergence holds in one region of the state space, while a turnpike arises in the complementary region. When convergence holds, the optimal multiplier and control gain are obtained directly from the LMI solution. Numerical examples illustrate convergence behavior and turnpike phenomena. Companion papers address the finite horizon SiDAR solution and the stage bound disturbance attenuation regulator (StDAR).

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Systems and Control

Disturbance Attenuation Regulator I-A: Signal Bound Finite Horizon Solution

Davide Mannini and James B. Rawlings

This paper develops a generalized finite horizon recursive solution to the discrete time signal bound disturbance attenuation regulator (SiDAR) for state feedback control. This problem addresses linear dynamical systems subject to signal bound disturbances, i.e., disturbance sequences whose squared signal two-norm is bounded by a fixed budget. The term generalized indicates that the results accommodate arbitrary initial states. By combining game theory and dynamic programming, we derive a recursive solution for the optimal state feedback policy valid for arbitrary initial states. The optimal policy is nonlinear in the state and requires solving a tractable convex scalar optimization for the Lagrange multiplier at each stage; the control is then explicit. For fixed disturbance budget $\alpha$, the state space partitions into two distinct regions: $\mathcal{X}_L(\alpha)$, where the optimal control policy is linear and coincides with the standard linear $H_{\infty}$ state feedback control, and $\mathcal{X}_{NL}(\alpha)$, where the optimal control policy is nonlinear. We establish monotonicity and boundedness of the associated Riccati recursions and characterize the geometry of the solution regions. A numerical example illustrates the theoretical properties. This work provides a complete feedback solution to the finite horizon SiDAR for arbitrary initial states. Companion papers address the steady-state problem and convergence properties for the signal bound case, and the stage bound disturbance attenuation regulator (StDAR).

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Systems and Control

Beyond Uptime: Actionable Performance Metrics for EV Charging Site Operators

Brandon D'Agostino, Jimmy Chen and Ram Rajagopal

The transition to electric vehicles (EVs) depends heavily on the reliability of charging infrastructure, yet approximately 1 in 5 drivers report being unable to charge during station visits due to inoperable equipment. While regulatory efforts such as the National Electric Vehicle Infrastructure (NEVI) program have established uptime requirements, these metrics are often simplistic, delayed, and fail to provide the diagnostic granularity needed by Charging Site Operators (CSOs). Despite their pivotal role in maintaining and improving site performance, CSOs have been largely overlooked by existing reporting standards. In this paper, we propose a suite of readily computable, actionable performance metrics-Fault Time, Fault-Reason Time, and Unreachable Time-that decompose charger behavior into operationally meaningful states. Unlike traditional uptime, these metrics are defined over configurable periods and distinguish between hardware malfunctions and network connectivity issues. We demonstrate the implementation of these metrics via an open-source tool that derives performance data from existing infrastructure without requiring hardware modifications. A case study involving 98 chargers at a California academic institution spanning 2018-2024 demonstrates that these metrics reveal persistent "zombie chargers" and high-frequency network instability that remain hidden in standard annual reporting.

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Signal Processing

RIS-aided Radar Detection Architectures with Application to Low-RCS Targets

Fabiola Colone, Filippo Costa, Yiding Gao et al. (+4 more)

In this paper, we address the radar detection of low observable targets with the assistance of a reconfigurable intelligent surface (RIS). Instead of using a multistatic radar network as counter-stealth strategy with its synchronization, costs, phase coherence, and energy consumption issues, we exploit a RIS to form a joint monostatic and bistatic configuration that can intercept the energy backscattered by the target along irrelevant directions different from the line-of-sight of the radar. Then, this energy is redirected towards the radar that capitalizes all the backscattered energy to detect the low observable target. To this end, five different detection architectures are devised that jointly process monostatic and bistatic echoes and exhibit the constant false alarm rate property at least with respect to the clutter power. To support the practical implementation, we also provide a guideline for the design of a RIS that satisfies the operating requirements of the considered application. The performance analysis is carried out in comparison with conventional detectors and shows that the proposed strategy leads to effective solutions to the detection of low observable targets.

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All authors Fabiola Colone, Filippo Costa, Yiding Gao, Chengpeng Hao, Linjie Yan, Giuliano Manara, Danilo Orlando

Systems and Control · Cross-listed from cs.RO

Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets

Simin Liu, Tong Zhao, Bernhard Paus Graesdal et al. (+5 more)

If we consider human manipulation, it is clear that contact-rich manipulation (CRM)-the ability to use any surface of the manipulator to make contact with objects-can be far more efficient and natural than relying solely on end-effectors (i.e., fingertips). However, state-of-the-art model-based planners for CRM are still focused on feasibility rather than optimality, limiting their ability to fully exploit CRM's advantages. We introduce a new paradigm that computes approximately optimal manipulator plans. This approach has two phases. Offline, we construct a graph of mutual reachable sets, where each set contains all object orientations reachable from a starting object orientation and grasp. Online, we plan over this graph, effectively computing and sequencing local plans for globally optimized motion. On a challenging, representative contact-rich task, our approach outperforms a leading planner, reducing task cost by 61%. It also achieves a 91% success rate across 250 queries and maintains sub-minute query times, ultimately demonstrating that globally optimized contact-rich manipulation is now practical for real-world tasks.

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All authors Simin Liu, Tong Zhao, Bernhard Paus Graesdal, Peter Werner, Jiuguang Wang, John Dolan, Changliu Liu, Tao Pang

Signal Processing

Adaptive algorithm for microsensor in sustainable environmental monitoring

Nursultan Daupayev, Christian Engel, Ricky Bendyk et al. (+1 more)

Traditional data collection from sensors produce a lot of data, which lead to constant power consumption and require more storage space. This study proposes an algorithm for a data acquisition and processing method based on Fourier transform (DFT), which extracts dominant frequency components using harmonic analysis (HA) to identify frequency peaks. This algorithm allows sensors to activate only when an event occurs, while preserving critical information for detecting defects, such as those in the surface structures of buildings and ensuring accuracy for further predictions.

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All authors Nursultan Daupayev, Christian Engel, Ricky Bendyk, Soeren Hirsch

Signal Processing

Physically constrained unfolded multi-dimensional OMP for large MIMO systems

Nay Klaimi, Clément Elvira, Philippe Mary et al. (+1 more)

Sparse recovery methods are essential for channel estimation and localization in modern communication systems, but their reliability relies on accurate physical models, which are rarely perfectly known. Their computational complexity also grows rapidly with the dictionary dimensions in large MIMO systems. In this paper, we propose MOMPnet, a novel unfolded sparse recovery framework that addresses both the reliability and complexity challenges of traditional methods. By integrating deep unfolding with data-driven dictionary learning, MOMPnet mitigates hardware impairments while preserving interpretability. Instead of a single large dictionary, multiple smaller, independent dictionaries are employed, enabling a low-complexity multidimensional Orthogonal Matching Pursuit algorithm. The proposed unfolded network is evaluated on realistic channel data against multiple baselines, demonstrating its strong performance and potential.

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All authors Nay Klaimi, Clément Elvira, Philippe Mary, Luc Le Magoarou

Audio and Speech Processing · Cross-listed from cs.SD

Unifying Speech Recognition, Synthesis and Conversion with Autoregressive Transformers

Runyuan Cai, Yu Lin, Yiming Wang et al. (+2 more)

Traditional speech systems typically rely on separate, task-specific models for text-to-speech (TTS), automatic speech recognition (ASR), and voice conversion (VC), resulting in fragmented pipelines that limit scalability, efficiency, and cross-task generalization. In this paper, we present General-Purpose Audio (GPA), a unified audio foundation model that integrates multiple core speech tasks within a single large language model (LLM) architecture. GPA operates on a shared discrete audio token space and supports instruction-driven task induction, enabling a single autoregressive model to flexibly perform TTS, ASR, and VC without architectural modifications. This unified design combines a fully autoregressive formulation over discrete speech tokens, joint multi-task training across speech domains, and a scalable inference pipeline that achieves high concurrency and throughput. The resulting model family supports efficient multi-scale deployment, including a lightweight 0.3B-parameter variant optimized for edge and resource-constrained environments. Together, these design choices demonstrate that a unified autoregressive architecture can achieve competitive performance across diverse speech tasks while remaining viable for low-latency, practical deployment.

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All authors Runyuan Cai, Yu Lin, Yiming Wang, Chunlin Fu, Xiaodong Zeng

Signal Processing · Cross-listed from cs.IT

Breaking the Storage-Bandwidth Tradeoff in Distributed Storage with Quantum Entanglement

Lei Hu, Mohamed Nomeir, Alptug Aytekin et al. (+1 more)

This work investigates the use of quantum resources in distributed storage systems. Consider an $(n,k,d)$ distributed storage system in which a file is stored across $n$ nodes such that any $k$ nodes suffice to reconstruct the file. When a node fails, any $d$ helper nodes transmit information to a newcomer to rebuild the system. In contrast to the classical repair, where helper nodes transmit classical bits, we allow them to send classical information over quantum channels to the newcomer. The newcomer then generates its storage by performing appropriate measurements on the received quantum states. In this setting, we fully characterize the fundamental tradeoff between storage and repair bandwidth (total communication cost). Compared to classical systems, the optimal storage–bandwidth tradeoff can be significantly improved with the enhancement of quantum entanglement shared only among the surviving nodes, particularly at the minimum-storage regenerating point. Remarkably, we show that when $d \geq 2k-2$, there exists an operating point at which both storage and repair bandwidth are simultaneously minimized. This phenomenon breaks the tradeoff in the classical setting and reveals a fundamentally new regime enabled by quantum communication.

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All authors Lei Hu, Mohamed Nomeir, Alptug Aytekin, Sennur Ulukus