Signal Processing
Reconfigurable Intelligent Surfaces for Cognitive Radio Networks: Design, Optimization, and Emerging Trends
Diluka Galappaththige, Chintha Tellambura, Ranga Kulathunga et al. (+1 more)
Reconfigurable intelligent surfaces (RISs) enable programmable wireless propagation environments, offering new opportunities for cognitive
radio networks (CRNs) to improve spectrum utilization, enhance spectral and energy efficiency, and operate reliably under low signal-to-noise ratio conditions. By combining the complementary strengths of RISs and CRNs, RIS-assisted CRNs (RCNs) have emerged as a promising architecture for future 6G wireless systems. Despite their growing importance, a comprehensive survey of this rapidly evolving field has been lacking. This paper fills this gap by providing a systematic and comprehensive review of RCNs. The paper first introduces the fundamentals of CRNs and RISs, including dynamic spectrum access models, spectrum sensing techniques, RIS operating principles, and RIS architectures. It then examines the design of RCNs, covering their system architectures, deployment strategies, channel estimation, spectrum access mechanisms, communication protocols, and the joint optimization of RIS and CRN parameters. Next, the existing literature is organized into six major research directions: performance analysis, resource allocation and optimization, secure RCNs, active RISs, simultaneously transmitting and reflecting RISs, and machine learning-enabled RCNs. Finally, the paper discusses key research challenges and future directions, including scalability, practical deployment, integration with emerging 6G technologies, coexistence with evolving network architectures, standardization, and security and
privacy. This survey provides a unified reference for researchers and practitioners and establishes a roadmap for the future development of RCNs.
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All authors Diluka Galappaththige, Chintha Tellambura, Ranga Kulathunga, Gayan Amarasuriya Aruma Baduge
Image and Video Processing
FlowSem: Flow Matching for Adaptive Wireless Image Transmission in Semantic Communication
Arnab Emon and Eslam Eldeeb
Wireless image transmission becomes challenging under poor channel conditions and stringent bandwidth constraints, as the receiver needs to preserve both pixel-level fidelity and meaningful visual structure. Classical separation-based systems, such as better portable graphics with low-density parity-check coding (BPG+LDPC), may suffer from cliff-effect behavior. While deep joint source-channel coding (DeepJSCC) provides graceful degradation as channel conditions worsen, its reconstructions may lose fine details under strong compression and severe channel distortion. To address this limitation, this paper proposes a two-stage flow matching-based semantic communication framework, termed FlowSem. In the first stage, a signal-to-noise ratio (SNR)-adaptive DeepJSCC model maps the source image into channel symbols and produces a coarse reconstruction at the receiver. In the second stage, a conditional flow matching model generates the final image from Gaussian noise conditioned on the DeepJSCC reconstruction and channel SNR. The proposed framework is evaluated on the Cityscapes
dataset under additive white Gaussian noise (AWGN) and Rayleigh fading channels using a fixed channel-symbol budget. The baselines include a rate-matched BPG+LDPC system, DeepJSCC, a denoising
diffusion probabilistic model (DDPM), and a denoising
diffusion implicit model (DDIM). Results show that FlowSem achieves competitive pixel-level fidelity and improved structural and perceptual reconstruction quality over the considered generative baselines across different channel conditions. FlowSem provides up to 60% lower Fréchet Inception Distance (FID) than the
diffusion baselines at low SNRs. Moreover, FlowSem reaches high reconstruction quality using only a few ODE integration steps, providing a favorable quality-latency tradeoff compared with standard DDPM and accelerated DDIM sampling.
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Audio and Speech Processing
Separating Voice from Age in COPD Screening
George P. Kafentzis and Nikoletta Arvaniti
Voice has been proposed as a low-cost screening signal for chronic obstructive pulmonary disease (COPD). COPD is strongly age-associated and voice changes with age, thus such results admit a trivial alternative explanation. We re-evaluate a public sustained-phonation corpus ($1246$ recordings, $68$ participants) under a strictly participant-level protocol. We therefore evaluate on repeatedly drawn age-matched cohorts and report the discrimination achieved by the confounders themselves on those same cohorts. Where raw (unmodelled) age ($0.510$ $[0.469, 0.551]$) and raw gender ($0.479$) are both measured at chance, acoustic models excluding age retain ROC-AUC $0.717$ $[0.552, 0.859]$ and average precision $0.747$ $[0.581, 0.892]$ against a one-to-one baseline of $0.5$, whereas models containing age fall to $0.531$–$0.679$. The separation is reproduced by two further learners with fixed hyperparameters. Two findings have broader methodological implications: models trained with age transfer less effectively to an age-balanced target cohort than otherwise identical models trained without age, and fourteen classical voice-quality and perturbation measures achieve comparable discrimination to a $55$-dimensional combined representation. We conclude that a non-age acoustic signal is present, that confounding by recording conditions cannot be excluded from the released features, and that the evaluation protocol in standard use cannot distinguish these possibilities.
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Systems and Control · Cross-listed from cs.RO
Model-Free Adaptive Parameter Tuning for Efficient Multi-Robot Warehouse Operations
Pratap Tokekar, Mouhacine Benosman, Rahul Chandan et al. (+3 more)
Robotic Fulfillment Centers (FCs) store inventory on shelves (pods) arranged in dense blocks. Retrieving a target pod that is buried deep in a block requires moving obstructing pods out of the way (i.e., digout). Multi-
robot planners use parameterized cost functions to control digout behavior, producing a spectrum of strategies: at one extreme, obstructing pods are sent to other blocks (using more
robots in travel lanes); at the other, pods are shuffled within the block (avoiding lane congestion but increasing extraction time). Each point on this spectrum has different downstream consequences for floor congestion and throughput. The optimal operating point depends on the specific facility configuration and shifts with operational conditions such as varying station demand and congestion patterns, making offline tuning impractical. We present an adaptive parameter tuning framework based on Extremum Seeking Control (ESC) that continuously adjusts planner parameters in response to measured throughput. ESC performs model-free optimization by perturbing parameters with sinusoidal dither signals and correlating perturbations with performance changes to estimate gradients, making it robust to the multi-minute delayed effects and credit assignment challenges inherent in large FC operations. Simulation studies demonstrate that the adaptive policy improves upon fixed policies across several conditions. We observe an improvement in throughput by an average of 5.0% across map and
robot fleet size variations, and by 8.4% under dynamic operating conditions. This work eliminates manual parameter provisioning and enables real-time adaptation, providing a self-tuning paradigm for FC storage operations.
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All authors Pratap Tokekar, Mouhacine Benosman, Rahul Chandan, Alexandre Ormiga Galvao Barbosa, Michael Caldara, Joseph W. Durham
Image and Video Processing
CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasets
Robert N. Spaans, Catherine Chia, Tongjie Wang et al. (+7 more)
Biochemical recurrence (BCR), defined as any detectable prostate-specific antigen level after prostatectomy with confirmatory elevation, is widely used as a surrogate endpoint and typically assessed using clinical and pathological variables. Currently, no standardized
benchmark exists for
multimodal prognostic modeling in urological cancers, partly because curating heterogeneous
multimodal data remains challenging. We developed the CHIMERA Challenge, a
multimodal benchmark integrating preoperative mpMRI, post-prostatectomy histopathology, patient characteristics, and clinician-derived variables from 267 patients across two institutions. The
dataset comprises 801 MRI sequences, 13 clinical variables per case, and 942 WSIs. Training (n=95), validation (n=23), and test (n=149) splits were established and hosted on the Grand Challenge platform. Baseline clinical and pathological characteristics did not differ significantly across splits. Models were evaluated on predicting time to BCR using the C-index. Post-challenge analyses tested how each model type performed when clinician-derived variables were withheld or randomized. Unimodal clinical models achieved the highest test C-index of 0.7402 but proved sensitive to the integrity of these variables, with performance collapsing toward chance (C approximately 0.50) when they were randomized.
Multimodal models retained near-baseline performance when these variables were withheld (delta C at most 0.04), indicating their ability to recover prognostic signal directly from imaging data. CHIMERA is the first public, standardized
multimodal benchmark for prostate cancer prognosis. Although models using only patient characteristics and clinician-derived variables yielded the highest leaderboard performance,
multimodal models demonstrated greater robustness in clinically realistic scenarios where complete expert annotation is not guaranteed.
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All authors Robert N. Spaans, Catherine Chia, Tongjie Wang, Adam Kowalewski, Parandzem Khachatryan, Domingos Oliveira, Khrystyna Faryna, Jean-Paul A. van Basten, Geert Litjens, Nadieh Khalili
Image and Video Processing
MDFI: A Multi-Domain Features Integration for Compressed Video Quality Enhancement
Sang NguyenQuang, Hieu Bui Minh, Dang BuiDinh et al. (+1 more)
The latest
video coding standard, H.266/VVC, has demonstrated significant improvements in compression efficiency compared to H.265/HEVC. Despite its advanced coding techniques, H.266/VVC still faces challenges in meeting the increasing demand for higher perceptual quality and enhanced compression performance. To address these limitations, we propose MDFI (Multi-Domain Features Integration), a compressed
video quality enhancement approach that features a novel Frame-Prediction Feature Transform (FPFT) module to process prediction information. Moreover, MDFI integrates a multi-domain feature fusion strategy that effectively combines spatiotemporal characteristics, cross-frequency representations, and compressed-domain prediction information to enhance decoded
video quality. Additionally, we introduce a comprehensive
dataset that encompasses uncompressed
video sequences, corresponding reconstructed versions at multiple QP levels, and predicted frames generated from H.266/VVC compressed bitstreams, providing essential resources for developing and benchmarking
video enhancement approaches. Extensive experiments demonstrate that our MDFI approach achieves superior performance to state-of-the-art methods in both objective metrics and visual quality, effectively mitigating
video compression artifacts. The code is available at: https://github.com/dangdinh17/MDFI.git.
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All authors Sang NguyenQuang, Hieu Bui Minh, Dang BuiDinh, Xiem HoangVan
Signal Processing · Cross-listed from cs.LG
Congruence Decomposition with Neural Block Solvers for Large-Scale PCI Assignment
Yeqing Qiu, Chengpiao Huang, Ye Xue et al. (+7 more)
Physical
Cell Identity (PCI) assignment is essential for interference management in dense 5G networks. As cellular networks scale, PCI reuse becomes unavoidable, which may cause collisions, confusions, and multiple forms of modular interference. Jointly mitigating these effects gives rise to a large-scale, multi-objective combinatorial optimization problem that is difficult to solve efficiently at practical network scales. In this work, we propose a congruence decomposition framework with neural block solvers for large-scale PCI assignment. The proposed decomposition exploits the arithmetic structure of PCI values to decouple multiple modular interference objectives into a collection of blockwise Min-$k$-Partition subproblems, followed by a graph coloring procedure to resolve PCI conflicts. For the resulting NP-hard Min-$k$-Partition subproblems, we develop neural block solvers by parameterizing their relaxed quadratic formulations with graph neural networks, enabling efficient optimization at large scales. Discrete assignments are recovered through conditional expectation rounding with theoretical guarantees. Experiments on synthetic cellular graphs and real-world 5G networks show that the proposed method consistently outperforms existing modular-interference-aware baselines in modular interference reduction, conflict elimination, and computational efficiency.
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All authors Yeqing Qiu, Chengpiao Huang, Ye Xue, Akang Wang, Fan Xu, Zhipeng Jiang, Dong Zhang, Ruoyu Sun, Qingjiang Shi, Zhi-Quan Luo
Image and Video Processing
Reliability- and Anatomy-Consistency-Aware Multimodal Learning for Robust Fracture Classification from Bangladeshi Radiographs
Musa Tur Farazi and K G Subarno Bithi
Background:
Multimodal fracture classifiers may benefit from patient and anatomical metadata, but they can also become brittle when contextual information is missing or mismatched. Methods: We studied 1493 radiographs from the Bangladeshi OrthoFrac-XR
dataset using leakage-safe age, sex, bone type, and laterality. A ConvNeXt image encoder was combined with a clinical multilayer perceptron through concatenation, late fusion, reliability-gated residual fusion, and a hierarchical state-location formulation. We additionally introduced an anatomy-consistency gate that attenuates metadata corrections when an image-side anatomical prediction disagrees with the reported bone type. Results: Across five folds and three seeds, hierarchical residual fusion achieved a macro-F1 of 0.6046 +/- 0.0279, compared with 0.5727 +/- 0.0270 for image-only learning, while improving the Brier score from 0.5239 to 0.4948. In a five-fold robustness experiment, anatomy-consistency fusion reduced the macro-F1 loss under shuffled metadata from 0.0567 to 0.0203 relative to ordinary residual fusion, although its clean-data macro-F1 was lower. Without bone type at inference, auxiliary anatomy supervision improved macro-F1 from 0.5620 +/- 0.0330 to 0.5899 +/- 0.0289. Conclusions: Structured context improves fracture classification, and consistency-aware gating limits harm from mismatched metadata. The observed clean-performance-robustness trade-off and the absence of patient-level identifiers motivate external and prospective validation.
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Image and Video Processing
Multimodal pseudo-CT synthesis for PET attenuation correction using separate modality encoding and topogram conditioning
Rory Bell, Artemis Bouzaki, Jiaming Cao et al. (+2 more)
We participated in the BIC-MAC Challenge with a
multimodal 3D patch-based U-Net for pseudo-CT generation from NAC-PET, MRI, and 2D topograms. By using separate PET and MR encoders, multi-scale feature fusion, and FiLM-based topogram conditioning at the bottleneck, we obtain a model that integrates complementary cross-modal information while reducing reliance on precise voxel-wise correspondence between modalities. Our final submission can be found: https://github.com/rrr-uom-projects/BIC-MAC-MICCAI2026
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All authors Rory Bell, Artemis Bouzaki, Jiaming Cao, Jasmine Morrison, Chelsea Sargeant
Audio and Speech Processing
TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems
Vladimir Bataev, Lilit Grigoryan, Andrei Andrusenko et al. (+3 more)
Contextualization is essential for production automatic
speech recognition (ASR) systems, where user-provided phrases must be recognized accurately under strict latency constraints. Although many context-biasing methods improve recognition accuracy, they often do not address the practical requirements of modern production ASR systems: streaming inference, efficient batched decoding, user-specific context lists, and low runtime overhead. We propose TurboBias 2.0, a production-oriented framework for efficient phrase boosting in Transducer-based ASR systems. The framework extends GPU-accelerated TurboBias with a case-insensitive boosting graph and per-stream batched decoding, allowing each utterance in a batch to use an independent context-biasing configuration. This enables personalized context biasing for multiple simultaneous users without sharing or mixing their context lists. The proposed framework supports both offline and streaming inference and can be used with greedy and beam-search decoding. Experiments show that TurboBias 2.0 improves contextual phrase recognition while preserving low latency and high throughput.
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All authors Vladimir Bataev, Lilit Grigoryan, Andrei Andrusenko, Nikolay Karpov, Vitaly Lavrukhin, Boris Ginsburg
Systems and Control
Fault Diagnosis of Dynamic Systems Under Unknown Operating Conditions: A Condition-Guided Selective Adaptation Approach
Jiaming Liu, Zeyi Liu, Hongshuo Zhao et al. (+2 more)
Fault diagnosis under unknown operating conditions remains challenging for dynamic industrial systems, as the distribution shift caused by changing operating conditions can significantly degrade the performance of diagnostic models in real-world applications. To address the problem, a condition-guided selective adaptation approach is proposed. Specifically, a novel continuous operating-condition
adversarial learning strategy with progressive training is developed in the offline stage to enhance the generalization ability of the diagnostic model. During online deployment, residual operating-condition responses are exploited to identify reliable unlabeled samples from streaming data, which are then used to update the diagnostic model. Extensive experiments on real-world gearbox and motor
datasets have demonstrated that the proposed framework outperforms state-of-the-art methods in diagnostic accuracy while maintaining relatively low test-time, showing its potential for practical industrial applications.
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All authors Jiaming Liu, Zeyi Liu, Hongshuo Zhao, Pengyu Han, Xiao He
Systems and Control
Loadability Limits Under Periodic Load Forcing
Maitraya Avadhut Desai, Gustavo Valverde and Gabriela Hug
The static loadability limit, defined as the demand at which the
equilibrium equations lose their solution, is the standard basis for interconnection screening of large new loads. This letter shows that when part of the demand varies periodically, as for data-center loads, the steady state is a forced periodic orbit whose loadability limit differs from the static one. The classical optimization argument is extended directly from
equilibria to fixed points of the period map. At the limit, the monodromy
matrix acquires a Floquet multiplier at +1, so the generic instability is a cyclic fold of the orbit rather than a saddle-node of
equilibria. The limit is therefore a function of the forcing frequency, which no static computation can capture. Additionally, with the network Jacobian turning singular along the cycle, singularity-induced instability is extended to orbits. On a four-bus test system, a static margin of 2.5 p.u. shrinks to 0.53 p.u. near the swing frequency, the instability mechanism switches from voltage collapse to a rotor-angle fold, and cold starts fail at amplitudes where the orbit still exists. A static analysis reproduces none of these effects.
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Signal Processing
Pattern Multiplication Underestimates the Sidelobe Level of a Planar Slotted-Waveguide Array by 1.1 to 2.8 dB Across a 2% Band
William Khalili Jr
The standard shortcut in slotted-waveguide array design – solve one radiating stick, multiply its embedded pattern by the array factor of the intended aperture distribution, and accept the product as the array pattern – is tested against a full-wave solution of the same metal. For a 16x16-slot planar array in WR-90 synthesised to a -30 dB Taylor illumination, the shortcut underestimates the transverse-plane sidelobe level by 1.06 dB at the design frequency and by 2.22 and 2.84 dB at the edges of a 2% band: a spread of 1.78 dB, larger than the mid-band error itself. Every sidelobe figure carries +/-0.11 dB of mesh spread from a convergence study on the production geometry. The error is signed, is worst at the band edges where the design has least margin, and falls only from 1.58 dB to 1.06 dB when the aperture is doubled from eight sticks to sixteen, which identifies the edge elements as its source. The array was made analysable by a thirteen-point single-slot full-wave calibration: over the design's offset range the handbook closed form for resonant length moves 3.9 micrometres where the measurement moves 143.9 micrometres, and Stevenson's conductance is shown to overestimate by a nearly constant 5.16%.
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Signal Processing
Transmit Beamformer Design for Beampattern Synthesis in Phased-Array Radar Systems
Berkan Kilic, Kenan Turbic and Slawomir Stanczak
This paper presents a phased-array transmit beamformer design for beampattern matching and interference suppression. We propose an optimization framework jointly addressing these two objectives. The resulting non-convex problem is solved via alternating minimization, where each subproblem admits a closed-form solution, one of which recovers the classical maximum-gain and null-steering phased-array beamformers. To design an
algorithm ensuring theoretical convergence guarantees, the beamformer design is then reformulated as a non-convex set feasibility problem, closely related to the original formulation. This problem is addressed using a provably convergent projected gradient descent method and further enhanced with acceleration techniques that substantially improve empirical convergence. Extensive simulations validate the excellent performance and computational efficiency of the proposed method.
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Audio and Speech Processing
SlimDiffuSE: Towards Efficient Diffusion-Based Speech Enhancement using Slimmable Networks
Nagashree K. S. Rao, Shrishti Saha Shetu, Mohamed Elminshawi et al. (+2 more)
Diffusion-based models are emerging in the
speech enhancement domain and are achieving state-of-the-art performance across various
benchmark datasets. A major downside of
diffusion models is that data generation requires many evaluations of a typically large neural network, which results in high overall complexity. In this work, we propose a slimmable
diffusion model that employs adaptive network widths throughout the data generation process to reduce computational cost. By using a greedy search
algorithm to optimize the network width schedule, our method achieves performance comparable to baseline
diffusion models with significantly reduced computational complexity. Notably, our approach reduces the computational complexity by up to $87.5\%$ without a significant drop in objective metrics, such as perceptual evaluation of
speech quality (PESQ) and SI-SDR.
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All authors Nagashree K. S. Rao, Shrishti Saha Shetu, Mohamed Elminshawi, Emanuël A. P. Habets, Andreas Brendel
Image and Video Processing
Toward Vision Language Model-based Assessment of Clinical Quality and Usability of LGE-MR Images for Cardiac Ablation Planning
Bipasha Kundu, Abhishek Chaturvedi, Axel W. E. Wismueller et al. (+2 more)
LGE cardiac MRI is widely used for left atrial fibrosis assessment and ablation planning in atrial fibrillation patients as knowledge of fibrotic tissue regions identified from LGE-MRI is critical for catheter ablation. Often, poor quality images used during ablation planning can cause mis-localization of ablation targets, directly impacting procedure safety and outcome. The decision of whether a scan meets the minimum quality threshold for ablation planning is currently made informally by the reviewing radiologist and is not captured by any automated system, yet it is arguably the most safety-critical output of the image quality assessment (IQA) process. However, variations in image quality caused by noise, motion artifacts, and poor boundary definition significantly compromise the reliability of downstream segmentation and clinical decision-making tasks. Manual quality assessment by expert radiologists is subjective and difficult to scale, while existing automated methods produce scalar scores without interpretable clinical
reasoning. In this work, we propose a two-stage
vision language model (VLM) framework for clinically grounded image quality assessment of left atrial LGE-MRI. In the first stage, a fine-tuned VLM generates structured radiology-style quality reports predicting five radiologist-defined criteria: Noise, Motion Artifact, LA Boundary Accuracy, PV Region Accuracy, and Under-segmentation Severity. In the second stage, a GPT-based
reasoning module maps the predicted quality and reports to a structured quality scores and binary clinical usability decision for ablation planning. We curate a
dataset of 60 annotated image slice-text pairs from 20 patients and
benchmark four state-of-the-art VLM architectures. InternVL2 achieves the highest criterion-level accuracy (Avg ACC=0.65, PLCC=0.79), while DeepSeek achieves perfect clinical usability agreement (Acc=1.00, kappa=1.00).
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All authors Bipasha Kundu, Abhishek Chaturvedi, Axel W. E. Wismueller, Richard Simon, Cristian A. Linte
Audio and Speech Processing
{\mu}Net: Ultra-Low-Memory and Low-Complexity Speech Enhancement for Embedded Digital Signal Processors
Shrishti Saha Shetu, Jose Miguel Martinez Aponte, Nagashree K. S. Rao et al. (+3 more)
Speech enhancement on embedded digital signal processors (DSPs) imposes strict constraints on
memory footprint, computational complexity, latency, and support for integer operations. Although recent DNN-based approaches have addressed these challenges individually, no unified framework in the literature simultaneously addresses all these requirements for practical deployment. In this work, we propose {\mu}Net, an ultra-low-
memory, low-complexity, and low-latency end-to-end DNN model. The proposed method requires only $90$~KB of static
memory and $28$~MMACs, while supporting an algorithmic latency as low as $4$~ms with performance comparable to state-of-the-art methods of similar complexity. Our experiments demonstrate that {\mu}Net is compatible with neural accelerators and supports full integer-arithmetic operations on consumer DSP platforms such as Cadence Tensilica HiFi 4/5.
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All authors Shrishti Saha Shetu, Jose Miguel Martinez Aponte, Nagashree K. S. Rao, Sharvin Vittappan, Oliver Thiergart, Emanuël A. P. Habets
Systems and Control
Robust lane-change intention anticipation under uncertainty based on a recursive Bayesian filtering approach
Dilara K{\i}l{\i}nç, Hendrik Kleikamp and Bruno Viti
In this paper we discuss and analyze a method for lane-change intention anticipation of drivers on highways based on kinematic features and surrounding observations. The approach makes use of a recursive
Bayesian filtering strategy, which can be interpreted as a hidden Markov model. We present two models for the likelihood term and show how they can handle missing observations. An additional focus of the work lies on calibrating the obtained probabilities in order to obtain reliable predictions. The introduced approach is evaluated in practice on the highD
dataset and compared to several other baseline methods. Particular emphasize is put on evaluating the robustness and temporal consistency as well as calibration of the developed
algorithm. Extensive numerical experiments allow for a careful performance assessment and detailed discussion of advantages and limitations of the different methodologies.
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Signal Processing
Privacy-Preserving Localization via Transmit Antenna Selection and Permutation
Yiyang Zhang, Yanmo Hu, Junyuan Gao et al. (+3 more)
Integrated sensing and communication (
ISAC) has been identified as one primary usage scenario in the sixth-generation (6G) network. While techniques to preserve information
privacy, such as cryptography, have been widely investigated, how to preserve sensing
privacy is still an open problem in the literature. This paper makes an early attempt to tackle the above issue. Specifically, we consider a localization system consisting of a multi-
antenna transmitter, termed Alice, a single-
antenna legitimate receiver, termed Bob, and a single-
antenna illegitimate receiver, termed Eve. To allow Bob to estimate Alice's angle-of-departure (AOD) but prevent Eve from performing this task based on Alice's signals, this paper proposes a novel
antenna selection and permutation based transmission strategy for Alice. Under this scheme, Alice carefully selects a subset of
antennas and permutes their indices to establish a specific pilot-
antenna mapping for transmission. Similar to cryptography for information
privacy, such a mapping will serve as the secret key to preserve localization
privacy. In the special case without noise at Bob and Eve, we manage to find out all the
antenna selection and permutation solutions such that with this key (knowledge about the exact pilot-
antenna mapping), Bob can uniquely estimate Alice's AOD, while without this key, Eve can estimate multiple AODs of Alice that can lead to its received signals. In the noisy case, numerical results are provided to show that our scheme can confuse Eve to make inaccurate AOD estimation as well.
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All authors Yiyang Zhang, Yanmo Hu, Junyuan Gao, Shuowen Zhang, Jiannong Cao, Liang Liu
Signal Processing
UW-OCDM for Low-Altitude UAV Communication and Cooperative Sensing
Yi Tao, Zhen Gao, Ziwei Wan et al. (+4 more)
Integrated sensing and communications (
ISAC) is a key enabler for uncrewed aerial vehicles (
UAVs) in the low-altitude economy. This paper proposes an
ISAC waveform that embeds a unique word (UW) into orthogonal chirp division multiplexing (OCDM), termed UW-OCDM, together with corresponding communication reception and cooperative sensing schemes for high-mobility
UAV scenarios. For communication, the embedded UW enables timing synchronization and Doppler estimation and compensation without requiring a separate synchronization sequence. A sparse spatio-temporal channel estimation method exploits the common channel support across multiple receive
antennas and consecutive UW observations to support reliable data demodulation. For sensing, the deterministic UW serves as a shared prior that allows distributed base stations to construct sensing dictionaries locally without exchanging random payload symbols in real time. A hierarchical multi-target detection and tracking
algorithm integrates direct-path interference suppression, kinematic prediction, multi-candidate screening, off-grid refinement, residual verification, and successive interference cancellation for robust localization with reduced search complexity. Simulation results demonstrate reliable communication and localization in highly dynamic
UAV scenarios, while the proposed framework retains low-complexity frequency-domain equalization and reduces transmit-reference sharing overhead and multi-static localization complexity.
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All authors Yi Tao, Zhen Gao, Ziwei Wan, Yuezu Lv, Hua Wang, Kaibin Huang, Sheng Chen
Signal Processing
HAP-Centric Flying Ad-Hoc Networks With Cell-Free Non-Terrestrial Connectivity
Muhammet K{\i}r{\i}k, Liza Afeef, Halim Yanikomeroglu et al. (+1 more)
High-altitude platforms (HAPs) are key enablers of next-generation non-terrestrial networks (NTNs), offering wide coverage, long endurance, and rapid deployment. Despite these advantages, current NTN designs remain satellite-centric and rely on terrestrial cellular assumptions, limiting flexibility and scalability. To overcome these limitations, this article proposes a multi-layer HAP-centric flying ad-hoc network (FANET). In this framework, HAPs are integrated with distributed uncrewed aerial vehicles (
UAVs) to form a standalone,
cell-free (CF) non-terrestrial system capable of autonomous operation. The layered architecture consists of an inter-HAP ad-hoc layer, a HAP-to-
UAV cooperative layer, and a
UAV-to-ground access layer, collectively enabling aerial connectivity, adaptive coverage, and interference-aware user access. Unique challenges for each layer are analyzed, including inter-HAP connectivity, FANET co-existence with terrestrial networks (TNs), and user access under heterogeneous conditions. Moreover, the article introduces enabling strategies such as fast beam
alignment for high data rate connectivity, uncoordinated FANET/TN co-existence, and user localization and environment classification. Validated by three case studies, the discussion also outlines standardization pathways. The results highlight HAP-centric FANETs as a foundation for resilient, scalable, and application-oriented 6G NTN deployments.
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All authors Muhammet K{\i}r{\i}k, Liza Afeef, Halim Yanikomeroglu, Hüseyin Arslan
Signal Processing
Frequency-Selective Pinching-Antenna Systems
Chongjun Ouyang, Hao Jiang, Zhiguo Ding et al. (+1 more)
An uplink pinching-
antenna system (PASS) is considered, where a segmented dielectric waveguide is employed to serve multiple users and one pinching
antenna (PA) is activated on each segment. Extensive waveguide deployment reduces user-to-PA path loss but also creates differential propagation delays among the PA-assisted paths spanning the large waveguide aperture. These delays are retained, and a Toeplitz block model is derived to characterize the resulting frequency-selective (FS) channel. For infinite-blocklength transmission, the achievable single-user rate and the multiuser sum-rates under joint and parallel decoding are characterized. For carrier-phase-aligned single-user PA placement, the frequency-flat (FF) model is shown to overestimate the exact FS rate. The difference between the rates computed under the FF channel model and the exact FS channel model is referred to as the FF rate overestimation, and its dependence on the waveguide-aperture-induced delay spread is characterized. For small delay spreads, the FF rate overestimation is shown to increase approximately quadratically with the delay spread, whereas a derived lower bound reveals that the overestimation grows at least double logarithmically with the waveguide aperture, or equivalently with the delay spread, in the large-aperture regime. For finite-blocklength transmission, achievable rates are derived under a practical cyclic-prefix single-carrier frequency-domain equalization framework. An element-wise
algorithm is developed to optimize FS-aware PA placement in both transmission regimes. The analytical findings demonstrate that differential delays across the extensively deployed waveguide can cause significant frequency selectivity and that the proposed FS-aware PA placement method substantially improves the achievable rate over placement based on the FF approximation, especially for large bandwidths and apertures.
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All authors Chongjun Ouyang, Hao Jiang, Zhiguo Ding, Robert Schober
Signal Processing
Obstacle-Aware Online Receiver Planning in Multistatic Ranging
Jingwei Hu, Dave Zachariah, Petre Stoica et al. (+1 more)
Multistatic ranging with mobile receivers enables good tracking performance due to the combined adaptive sensor geometry. However, environments that contain signal obstructing obstacles require receiver trajectory planning to maintain line-of-sight (LOS) conditions with transmitters and the target of interest. In this letter, we develop a non-myopic receding-horizon framework for multistatic tracking. It uses convex collision-avoidance constraints and a control objective that focuses on maintaining good LOS signal conditions, taking into account future obstructions. We demonstrate the efficiency and tracking accuracy of the method via a numerical experiment.
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All authors Jingwei Hu, Dave Zachariah, Petre Stoica, Torbjörn Wigren
Audio and Speech Processing
Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement
Alessia Milo, Georg Götz, Steinar Gu{\dh}jónsson et al. (+3 more)
We investigate how the realism of synthetic room impulse response (RIR)
datasets affects the training of DeepFilterNet3 for single-channel
speech enhancement. We compare a DNS4 image-source-method (ISM) RIR
dataset with a higher-acoustic-fidelity
dataset generated using hybrid wave-based and geometrical acoustics simulation. Rather than isolating individual simulation factors, we compare complete RIR generation pipelines while keeping the enhancement model unchanged. Models are evaluated on unseen measured RIRs using objective
speech enhancement metrics and downstream automatic
speech recognition (ASR). Training with the higher-fidelity
dataset consistently yields modest improvements in objective metrics and substantially lower ASR word error rates than the ISM
dataset. Although the experiments do not attribute these gains to individual modelling components, they show that increasing the overall realism of synthetic acoustic training data improves the generalization of DeepFilterNet3 to unseen measured environments.
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All authors Alessia Milo, Georg Götz, Steinar Gu{\dh}jónsson, Daniel Gert Nielsen, Jesper Pedersen, Finnur Pind
Signal Processing
SR-TL1: A Square-Root TL1-Norm Framework for Robust SMV DoA Estimation under Highly-Coherent Dictionaries
Youval Klioui
This paper proposes a Square-Root Transformed $L_1$-norm ($SR\text{-}TL_{1}$) sparse recovery framework for single-measurement-vector (SMV) direction of arrival (DoA) estimation under highly-coherent overcomplete dictionaries with angular-dependent array imperfections. The proposed framework combines the square-root Least Absolute Shrinkage and Selection Operator (square-root LASSO) framework which is known to be robust against noise variance with the Transformed $L_1$-norm ($TL_1$-norm), a non-convex penalty that shows a stronger recovery performance than the classical convex $L_1$-norm under highly-coherent dictionaries. We use the Difference of Convex
Algorithm (DCA) along with the Alternating Direction Method of Multipliers (ADMM)
algorithm to obtain simple, closed-form update rules and provide an efficient implementation that leverages the low-rank nature of the Gram
matrix of the dictionary so as to obtain a computational complexity of at most $\mathcal{O}(MN)$ per iteration where $M$ is the array size and $N$ is the length of the dictionary. We additionally provide a convergence guarantee for the DCA iterates of $SR\text{-}TL_{1}$. Experimental verification of the proposed framework shows a lower sensitivity of the regularization hyperparameter to the noise variance level and a competitive recovery performance compared to state-of-the-art baselines.
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Systems and Control
Multi-Objective Deep Reinforcement Learning for Secure and Stable Power System Operation
Ioannis Papadopoulos, Georgios Tsaousoglou and Johanna Vorwerk
The ongoing energy transition challenges the stable operation of power systems and increases the need for rapid decision-making under uncertainty. While reinforcement learning has emerged as a promising framework for power system control and operation, existing applications typically focus on a single operational criterion, such as thermal security or small-signal stability. However, power system operation is inherently multi-objective and may involve trade-offs between objectives. This paper develops a unified-control deep reinforcement learning
agent that maintains thermal security under
stochastic load variations while steering the system toward operating points with improved damping of the most critical mode. Compared to a thermal-security-only
agent and a business-as-usual policy, the proposed
agent achieves a better balance among the operational objectives considered, with notably improved damping and negligible thermal-security violations. Finally, the operational value of increased critical damping is demonstrated under small- and large-signal disturbances, where operating points with higher damping lead to faster oscillation decay and improved critical clearing times.
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Systems and Control
A Safety-Driven Architectural Framework for Fail-Operational Drone Swarms in Critical Missions
Luiz Giacomossi, Zafer Yigit, Marwan Shakarna et al. (+4 more)
The certification of Unmanned Aerial Vehicle (
UAV) swarms for safety-critical operations requires verifiable design assurance. Airworthiness standards demand deterministic reliability, whereas multi-
agent coordination
algorithms execute non-deterministic models. This paper proposes a mixed-criticality architectural framework that applies SAE ARP4754B methods to swarm reconfiguration. First, a hardware-isolated Safety Monitor functions as a Run-Time Assurance (RTA) gateway, decoupling the flight-critical core from the non-deterministic Swarm Manager. Second, the monitor enforces formal safety contracts based on
agent Health Vectors derived systematically from a Functional Hazard Assessment (FHA). Third, the framework propagates these Health Vectors to the collective planner to trigger fail-operational task reallocation, enabling intelligent swarm behaviors without compromising flight-critical isolation. Markov reliability modeling demonstrates that the $10^{-7}$ failures per flight hour Hazardous target is theoretically achievable for our SAIL IV scenario, provided the Safety Monitor meets $C_{monitor}>0.9991$, consistent with DAL B CMD/MON implementations.
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All authors Luiz Giacomossi, Zafer Yigit, Marwan Shakarna, Shoaib Saleemi, Ivan Tomasic, Baran Çurüklü, Håkan Forsberg
Systems and Control
Parameters Overshadowed by Price Lags: Load, Climate, and Calendar Effects in ERCOT Day-Ahead Price Formation
Yunus Bicen and Eman Hammad
Accurate electricity price forecasting is critical for smart grid stability, yet the heavy reliance on historical price lags in modern predictive models often obscures the fundamental physical drivers of
market volatility. This paper proposes a regime-sensitive, explainable artificial intelligence (XAI) framework to unmask the hidden roles of load,
climate, and calendar variables in the ERCOT Day-Ahead
Market (2014-2024). Utilizing a Histogram-based Gradient Boosting Regressor (HGBR), we introduce a Suppression Ratio to quantify how price lags overshadow physical parameters. Our analysis reveals that while lags provide short-term
memory during normal conditions, they fail to capture extreme dynamics; notably, their inclusion increased forecasting error (MAE/RMSE) during price spikes. By isolating these effects, the study revealed that price spikes are not merely the result of continuously rising temperatures, but rather emerge from the system reacting sharply within a specific temperature range. These findings underscore the importance of moving beyond lag-based models to uncover the true drivers of extreme price events.
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Systems and Control
Sharing the Control Authority Between Deep Reinforcement Learning and Model Predictive Control: Application to Multi-Class Transportation Networks
Giray Onur, Azita Dabiri and Bart De Schutter
Transportation networks, in particular multi-class transportation networks (i.e., networks with mixed vehicle types), are complex systems that are challenging to control. Recently, Deep Reinforcement Learning (DRL), which learns control policies from interactions with the environment, and Model Predictive Control (MPC), which uses a system model to optimize control inputs, have been increasingly utilized for transportation network control. However, nonlinear system dynamics and high-dimensional state spaces in large-scale networks limit DRL's learning capacity under time-constrained training and increase MPC's computation time, hindering real-time implementation with limited computational resources. Moreover, MPC depends on an accurate network model, which is often unavailable for complex systems such as multi-class transportation networks. This paper proposes a novel DRL-MPC framework for multi-class transportation networks that divides control authority between DRL and MPC, combining DRL's fast online computation and model independence with MPC's built-in optimization and constraint-handling capabilities. In the hierarchical framework, MPC operates at the higher level and determines low-frequency control inputs whose slower update rate accommodates its high computation time, while DRL operates at the lower level and determines high-frequency control inputs using its fast online deployment. The framework is evaluated on a multi-class freeway network against a hierarchical MPC controller and a hybrid state-feedback-MPC controller, including scenarios with model mismatch and noisy traffic demands. Results show that the proposed framework outperforms the hybrid state-feedback-MPC controller, substantially reduces online computation time compared with the hierarchical MPC controller, and provides more effective constraint enforcement under model mismatch.
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Signal Processing
From Sparse Probes to Sum-Rate Maximization: Electromagnetic Twin Beamforming
Tuo Wu, Kangda Zhi, Jie Tang et al. (+3 more)
Probing every candidate location at each wireless-map update is costly. This letter develops electromagnetic-twin (ET) beamforming that converts sparse spatial probes into a sum-rate decision. The ET stores a location-dependent angular power spectrum, updates its innovation through graph-regularized estimation, and queries user covariances for projected beam optimization. A rate-sensitivity bound selects subsequent probes by query-weighted posterior-variance reduction. With 1% random probes, ET beamforming achieves $2.69$ bit/s/Hz versus $1.12$ for a static channel knowledge map; with 7% query-aware probes, it reaches $3.03$ bit/s/Hz, within 3.4% of perfect-covariance beamforming.
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All authors Tuo Wu, Kangda Zhi, Jie Tang, Jianchao Zheng, Naofal Al-Dhahir, Fumiyuki Adachi