Signal Processing
Sparse Channel Estimation for Pixel Antennas: Addressing the Pilot Rank Deficiency
Yiting Chen, Yumeng Zhang and Hongyu Li
Composed of multiple interconnected pixels controlled by on/off RF switches, the pixel
antenna can generate reconfigurable radiation patterns that can be further exploited to construct diverse pilot sequences for effective channel estimation. However, such pilot sequences inherently have rank deficiency, making it difficult to effectively and efficiently acquire the full channel state information (CSI) across all available radiation patterns. To tackle this difficulty, we consider a sparse environment with a limited number of propagation paths for a pixel
antenna system, where a user equipped with a pixel
antenna transmits only a limited number of pilots to recover the CSI under all radiation patterns. The proposed
algorithm exploits the limited number of propagation paths that are invariant with the pixel
antenna patterns, and then formulates the full channel estimation as a sparse recovery problem in the angular domain solved by Generalized Approximate Message Passing (GAMP). Moreover, to mitigate the rank deficiency of pilot sequences, we additionally incorporate a Multipath Matching Pursuit (MMP)
algorithm for robust initialization. The overall proposed scheme, termed MMP-GAMP, achieves higher estimation accuracy than other
algorithm baselines, while requiring lower pilot overhead.
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Signal Processing · Cross-listed from cs.AI
A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy
David Martin-Calle, Cesar Alvarez Llamas, Vincent Motto- Ros et al. (+3 more)
A lightweight and reproducible denoising pipeline for high-throughput Raman spectroscopy is presented. The approach relies on a one-dimensional convolutional autoencoder trained using a Noise2Noise strategy, requiring neither external spectral libraries nor high signal-to-noise reference spectra for training. From a reduced training subset composed of repeated short-exposure acquisitions, the model learns to reconstruct Raman spectra while efficiently suppressing
stochastic noise. The method is evaluated on a heterogeneous mineral sample, using both quantitative spectral fidelity metrics (RMSE, SNR, SSIM) and task-oriented criteria based on unsupervised K-means classification. Results demonstrate that integration times as short as 5 ms per spectrum, which are typically insufficient for reliable interpretation, yield denoised spectra with high fidelity to the reference data while preserving chemically coherent maps. This work provides a practical trade-off between spectral quality and acquisition speed, enabling fast, adaptable Raman workflows compatible with routine laboratory use. It also offers a transferable framework for other one-dimensional spectroscopic modalities.
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All authors David Martin-Calle, Cesar Alvarez Llamas, Vincent Motto- Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney
Systems and Control
On Piecewise Quadratic Terminal Costs for MPC
Sampath Kumar Mulagaleti, Boris Houska, Mario Zanon et al. (+1 more)
This paper presents a novel approach to synthesize stabilizing termi- nal ingredients for linear model predictive control (MPC) schemes, with the aim of increasing the region of attraction while reducing suboptimal- ity with respect to the solution of the infinite-horizon optimal control problem. It is based on the construction of a novel terminal region using methods from the field of configuration-constrained polytopic computing, along with a terminal cost that is exactly equal to the infinite-horizon linear-quadratic regulator cost in a nontrivial neighborhood of the steady- state. The practical performance of the controller is illustrated through various case studies, and comparisons with state-of-the-art approaches are presented.
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All authors Sampath Kumar Mulagaleti, Boris Houska, Mario Zanon, Mario E. Villanueva
Signal Processing
Movable Antenna-Enabled Integrated Sensing and Communication in Low-Altitude UAV Networks
Bin Li, Pengcheng Rao, Xuedong Zhang et al. (+1 more)
This paper investigates a multiple unmanned aerial vehicle (
UAV)-assisted integrated sensing and communication (
ISAC) system equipped with movable
antenna (MA) arrays. To align with practical scenarios, we simulate the dynamic roaming of ground users and the three-dimensional deployment of
UAVs in the airspace. We aim to maximize the total data rate by jointly optimizing key operational variables, including
UAV trajectories, user association,
antenna positions, and beamforming. This formulated problem is subject to constraints on transmission power and the sensing signal-to-noise ratio. To address the challenge of dynamically unknown state transitions due to user mobility, the original problem is decomposed into two steps and solved using different
algorithms. First, we utilize the hierarchical density-based spatial clustering of applications with noise (HDBSCAN)
algorithm to address the ground-to-air association problem, periodically updating clusters and re-associating during training. The clustering hotspots are used to suggest flight directions for the
UAVs. Second, we develop the soft actor-critic
algorithm to solve the joint optimization problem of
UAV trajectories,
antenna positions, and beamforming. Experimental results demonstrate that
UAVs equipped with MA arrays outperform those with traditional fixed
antenna arrays in
ISAC systems, and the proposed optimization strategy effectively enhances communication rates while ensuring sensing performance.
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All authors Bin Li, Pengcheng Rao, Xuedong Zhang, Xinyi Wang
Systems and Control
A characteristic function framework for chance constraint programming in stochastic model predictive control
Yuwei Ying, Johan Löfberg and Anders Hansson
The computation of chance constraints in
stochastic model predictive control is often numerically challenging due to the non-Gaussian nature of the disturbances. To overcome this problem, we propose an optimization computational framework applicable to non-Gaussian disturbances. This framework employs a numerical inversion method, utilizing the characteristic function of the disturbance distribution to compute the probability in the chance constraint as well as its gradient. To improve efficiency, it vectorizes integral points and reuses intermediate computations in Gauss-Kronrod quadrature. The framework is implemented within the YALMIP toolbox to perform chance constraint calculations for arbitrary non-Gaussian disturbances, applicable to both single-component distributions and mixture models. It allows the user to simply specify a distribution type and its parameters for the disturbance and directly compute the probability and its gradient to solve the optimization problem. The method is validated through a numerical example of a
stochastic model predictive control application.
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Systems and Control
Advanced PID architectures for tracking changing active constraints
Sigurd Skogestad
Advanced regulatory control (ARC), also known as advanced PID architectures, is a simple and robust way of controlling processes with changing and possibly conflicting constraints, where it previously was believed - at least in academia - that model-based solutions, such as MPC, were the only effective solution. To illustrate this, ARC is applied in two case studies. The first is a gas-
liquid separation process, in which selectors and split-parallel control are combined to achieve bidirectional inventory control in which the throughput manipulator moves automatically to the most optimal position. The second case study is on keeping acceptable air quality (CO2-level) and temperature in a room (in this case, a barn for cows). The CO2 and temperature constraints can be conflicting, leading to a hierarchical switching network of PID controllers. Note: this is an extended version (with simulations) of paper at IFAC World Congress, August 2026, Korea.
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Signal Processing
Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks
Zhonghao Lyu, Xiaowen Cao, Xianxin Song et al. (+11 more)
Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks.
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All authors Zhonghao Lyu, Xiaowen Cao, Xianxin Song, Yuchen Li, Jiacheng Wang, Shuoyao Wang, Yuanhao Cui, Weijie Yuan, Xianghao Yu, Guangxu Zhu, Hai Liu, Jie Xu, Derrick Wing Kwan Ng, Shuguang Cui
Systems and Control
Electric Vehicle Charging Profile Forecasting Using Hybrid Models
Riccardo Ramaschi, Mario Paolone and Sonia Leva
Electric Vehicle (EV) fast charging stations require
forecasting techniques both at the single charger level and aggregated level. While for the latter several models exist,
forecasting individual EV charging profiles is still underexplored in literature. However, such methods may be potentially used by battery-aware scheduling, leading to a more granular update of the charging station aggregated forecast and provide a more accurate estimation of EVs departure times. Nonetheless, the variable extent of available information in time and in different settings could jeopardize these benefits. For this reason, we propose a hybrid and lightweight method to estimate the EV charging profile before and during the charging process. Besides evaluating this method on multiple EVs from a public
dataset, we also assess the impact of different level of information in the time transposition of the charging profile.
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Audio and Speech Processing
Flexible Multi-Channel Target Speaker Extraction Using Geometry-Conditioned Spatially Selective Non-linear Filters
Jiatong Li, Wiebke Middelberg and Simon Doclo
Recently, a spatially selective non-linear filter (SSF) has been proposed for target speaker extraction, using the target direction-of-arrival (DOA) as a spatial cue. Since learned intermediate features are tied to the microphone geometry, the performance of the SSF degrades significantly when evaluated on mismatched array geometries. In this paper, we propose a geometry-conditioned SSF (GC-SSF), which incorporates a geometry-conditioning branch based on FiLM layers. Furthermore, we propose a feature that jointly encodes the DOA and the microphone positions (DOA-MPE). The conditioning branch modulates the intermediate feature maps of the SSF using the DOA-MPE feature to capture the spatial relationship between the microphone positions and the target speaker. Experimental results across circular, uniform linear, and random microphone arrays show that the proposed GC-SSF generalizes better to mismatched geometries while maintaining high spatial selectivity, demonstrating its ability to effectively adapt the filtering process to different array geometries
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Systems and Control · Cross-listed from cs.RO
Orchestrating Wheeled Mobile Robots Online without Conflicts
Jianghong Dong, Yifeng Zhang, Wenhan Cao et al. (+4 more)
Formation control of wheeled mobile
robots (WMRs) has been widely studied due to its broad applications. However, existing studies primarily focus on tracking predefined formations, with limited adaptability to varying environments. To address this limitation, we propose CFOO, a Conflict-Free Online Orchestration framework for continuous formation
navigation of WMRs that integrates centralized formation generation with distributed formation maintenance. The upper layer monitors the environment in real time and generates adaptive formations as needed, using the proposed TCF-R2T (Trajectory-Conflict-Free
Robot-to-Target assignment)
algorithm to compute optimal assignments in
polynomial time for timely, conflict-free formation transitions. At the lower layer, each WMR continuously employs JSTP (Joint Spatio-Temporal trajectory Planning) to maintain the given formation by jointly optimizing spatial positions and segment durations, improving coordination over spatial-only optimization with fixed durations. By coordinating these two layers, CFOO orchestrates WMRs to adapt their formations to varying environments and thus enable continuous
navigation. Moreover, we design a Time-Varying Formation Reference (TVFR) mechanism to facilitate smooth transitions between successive desired formations. Both simulation and real-world experiments validate the effectiveness and adaptability of CFOO. Experimental
videos are available on our project website: https://github.com/dongjh20/CFOO.
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All authors Jianghong Dong, Yifeng Zhang, Wenhan Cao, Jiawei Wang, Mengchi Cai, Keqiang Li, Guillaume Sartoretti
Signal Processing
Augmented Set-membership Affine Projection Algorithm and Its Performance Analysis
Xinnian Guo, Haiquan Zhao, Chen Wang et al. (+3 more)
The augmented affine projection
algorithm (AAPA) has considerably excellent performance for highly colored input signals. However, the direct
matrix inversion operation leads to a high computational complexity, especially with high projection order. Inspired by the excellent characteristics of set-membership filtering (SMF), this paper proposes the augmented set-membership affine projection
algorithm (ASM-APA), which not only has low computational complexity but also offers improved performance compared with AAPA. Then, the computational complexity and stability of ASM-APA are analyzed, and the condition for maintaining the stability of the
algorithm is provided. Finally, in the computer simulation phase, the results of the simulation experiments demonstrated that ASM-APA has superior performance compared to AAPA.
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All authors Xinnian Guo, Haiquan Zhao, Chen Wang, Xiaoqiang Long, Yalin Liu, Wenjing Luo
Signal Processing
A Fast Robust Adaptive filter using Improved Data-Reuse Method
Yi Peng, Haiquan Zhao and Jinhui Hu
Adaptive filter in complex scenarios demands
algorithms that integrate fast convergence, low complexity, and robust performance under diverse noise conditions. To address this challenge, we propose a online censoring robust total generalized adaptive filter using improved data-reused method (RTGA-IDROC)
algorithm. The proposed RTGA variant possesses the advantages of both the total least squares (TLS) strategy and the robust generalized adaptive (RGA) function. This
algorithm not only effectively handles input noise under the errors-in-variables (EIV) model but also achieves excellent performance across diverse noise environments. Furthermore, to meet the high demand for convergence speed in practical applications, an improved data reuse (IDR) method is introduced, enabling faster convergence in the early stages of iteration without compromising steady-state performance. The increased computational complexity brought by the IDR method is mitigated using the online censoring (OC) strategy. We also modify the OC threshold for real-valued
algorithms, as the original threshold was defined for the complex domain. Beyond these algorithmic enhancements, a local stability analysis for the proposed
algorithm is provided, and the theoretical steady-state mean-square deviation (MSD) is derived. Finally, simulation experiments in system identification and acoustic echo cancellation (AEC) scenarios validate the superior performance of the proposed
algorithm.
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Image and Video Processing
Evaluating the Effect of Compression on Video Temporal Consistency Using Objective Quality Metrics
Peter Zsoldos
While
video compression
algorithms effectively reduce bitrate, aggressive quantization often compromises temporal coherence, introducing artifacts such as flicker, motion inconsistency, and unstable textures. Although spatial quality degradation is well-documented, the relationship between compression intensity and temporal stability remains insufficiently characterized. This paper systematically examines the progression of frame-to-frame coherence errors across different bitrate regimes, utilizing multiple codecs (AV1, HEVC, VP9, H.264) and content types. Our findings reveal that temporal consistency degrades non-linearly with increasing compression. Most critically, we identify a "Predictability anomaly" where sequences with unpredictable or irregular dynamics experience disproportionately higher instability than sequences with higher, but more predictable, motion magnitude. This challenges the conventional assumption that motion volume alone dictates encoding difficulty and highlights the necessity of temporal-aware metrics in compression pipelines.
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Signal Processing
Baseband-Efficient WMMSE Precoding: From a Signal Weighting Cost Perspective
Shuai Gao, Fan Xu, Mian Li et al. (+4 more)
For downlink transmission in massive multi-user multiple-input multiple-output (MU-
MIMO) systems, conventional precoding research heavily focuses on reducing the computational complexity of precoding
matrix design, while largely overlooking another critical bottleneck: the substantial signal weighting cost incurred by repeatedly applying the precoder to high-speed data streams. To address both challenges simultaneously, this paper proposes a novel sparse precoding framework tailored for fully-digital architectures. Within this framework, from the sum-rate maximization perspective, we design two sparse precoding architectures: a common-support row-sparse architecture and a user-specific row-sparse architecture, so as to reduce the number of multiplication operations required in baseband signal weighting while largely preserving the achievable sum-rate. For the formulated mixed-integer non-linear programming (MINLP) problem, we rigorously prove that the optimal precoder under both sparse architectures strictly resides in a specific low-dimensional subspace determined by the channel
matrices, thereby reducing the dimensionality of the optimization variables. Based on this insight, an alternating optimization
algorithm is developed within the weighted minimum mean square error (WMMSE) framework to jointly optimize sparse beam selection and low-dimensional precoding coefficients. The combinatorial beam selection problem is handled using an efficient penalty-based majorize-minimization (MM) method, yielding a low-complexity closed-form solution. Simulation results demonstrate that the proposed schemes achieve sum-rate performance close to that of full-dimensional WMMSE precoding without sparsity constraints, while substantially reducing the overall signal-weighting cost.
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All authors Shuai Gao, Fan Xu, Mian Li, Xinzhi Ning, Lei Qiu, Boyu Ning, Qingjiang Shi
Signal Processing
Multi-dimensional hierarchical dictionary search for large MIMO-OFDM systems
Nay Klaimi, Philippe Mary and Luc Le Magoarou
Sparse recovery
algorithms are of utmost importance for estimation processes in wireless communications. However, communication systems such as massive multiple input multiple output (
MIMO) systems are rapidly growing in dimension, which consequently increases the computational complexity of these
algorithms. This work proposes a low-complexity strategy for the efficient implementation of the ”atom selection step” in these greedy sparse recovery
algorithms, based on the structural features of these systems. A theoretical justification is presented along with tests using realistic channel data, to demonstrate the computational gain induced by the proposed approach and compare it to the classical sparse recovery approach.
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Signal Processing
Mixture-of-Experts Diffusion Models for Adaptive Massive MIMO Channel Estimation via Variational Bayesian Inference
Zhuorui Jiang, Jun Fang, Boyu Ning et al. (+2 more)
Channel estimation is essential to massive multiple-input multiple-output (
MIMO) systems. While recent generative model-based approaches using lightweight
diffusion models (DMs) have achieved superior performance, they typically rely on a single data-driven prior, which limits their adaptability to varying channel distributions in real-world scenarios. To address this deficiency, we propose a mixture-of-experts (MoE)
diffusion model (DM) framework combined with variational
Bayesian inference. Specifically, our approach employs multiple pre-trained DMs, with each trained on a specific type of propagation channels. We then propose a probabilistic graphical model in which the channel is modeled as a latent variable drawn from one of these candidate generative priors with a certain probability. By integrating variational
Bayesian inference with DM-based data priors, the underlying channel along with the expert indicator variable are jointly inferred, thus enabling automatic model adaptation for channel estimation. The effectiveness of our approach is evaluated on 3GPP CDL channels. Simulation results demonstrate that our proposed approach achieves a clear performance improvement over the standard DM-based method that employs a single prior trained on aggregated data from all channel types, particularly when the channel samples from different propagation environments are imbalanced.
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All authors Zhuorui Jiang, Jun Fang, Boyu Ning, Hongbin Li, Ying-Chang Liang
Systems and Control
Learning the dynamics of nonlinear systems with regional stability guarantees through linear matrix inequality constraints
Daniel Frank, Fahim Shakib and Steffen Staab
This paper presents a method that learns a regionally stable recurrent neural network model from a set of input-output data generated by an unknown dynamical system. Relying on generalized sector conditions on the deadzone activation function, we first derive sufficient conditions that guarantee forward invariance on a compact set of the state space for any inputs from a given set. Such regional properties lead to less conservative conditions compared to variants that offer a global form of stability, and are in line with the system data that is only observed regionally. Our learning method derives conditions for regional stability using a barrier function approach, leading to models equipped with a certificate of regional stability in a subset of the state space and for a given input set. We illustrate our theoretical result with a numerical example and compare it to methods that impose a global form of stability, which fail to identify the system, and with a method that imposes no stability constraints at all, which does not guarantee a stable behavior within any state or input set.
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Signal Processing
Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions
Yuwen Zeng, Dengzhe Hou, Zhang Zhang et al. (+4 more)
Self-initiated
attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers. While previous studies have examined their neural correlates, it remains unclear how multi-dimensional electroencephalography (
EEG) features contribute to their characterization within an interpretable computational framework. In this study, we build on an experimental paradigm developed in our previous work, which enables controlled comparison between task-constrained self-initiated shifts and externally instructed shifts under identical visual stimulation. Within this setting, we investigate whether preparatory
EEG activity can distinguish these two types of
attention shifts. We adopt a machine learning-based approach and conduct two complementary analyses: (1) a performance-oriented assessment of frequency-specific topographic patterns, and (2) a model-based feature attribution analysis using SHapley Additive exPlanations (SHAP). These analyses provide a structured view of how spectral features across regions of interest contribute to model behavior. Our results demonstrate reliable within-subject classification performance, indicating that preparatory
EEG activity contains subject-specific discriminative information within this paradigm. The analysis shows that higher-frequency bands and frontal regions contribute strongly to model decisions, although such contributions should be interpreted cautiously due to the potential influence of non-neural artifacts in high-frequency
EEG signals. Overall, this work highlights the value of interpretable machine learning for analyzing subject-specific
EEG signal patterns in a controlled experimental setting, with potential applications in personalized and asynchronous
brain-machine interface systems.
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All authors Yuwen Zeng, Dengzhe Hou, Zhang Zhang, Sai Sun, Yongsong Huang, Chia-huei Tseng, Satoshi Shioiri
Audio and Speech Processing
Contextual Biasing for Streaming ASR via CTC-based Word Spotting
Kai-Chen Tsai, Tien-Hong Lo, Yun-Ting Sun et al. (+1 more)
Contextual biasing is essential to improving the recognition of rare and domain-specific words in an automatic
speech recognition (ASR) system. While numerous methods have been proposed in recent years, most of them focus on offline settings and do not explicitly address the challenges of streaming ASR. For example, CTC-based word spotting (CTC-WS) have demonstrated strong performance by directly detecting keywords from CTC log-probabilities, but they are limited to offline processing and require access to the full utterance. In This work, we present a streaming extension of CTC-WS for real-time contextual biasing. Our method maintains active keyword paths across audio chunks using a stateful
token passing
algorithm, enabling the detection of keywords that span multiple chunks. To ensure low latency and stable output, we introduce an incremental commitment mechanism that only emits segments guaranteed not to be affected by future audio, while deferring uncertain regions. This method naturally integrates with streaming ASR pipelines and does not require modifications to the underlying acoustic model or additional training, making it practical for real-world deployment. Experimental results show that our method reduces overall WER and effectively improves keyword F-score, demonstrating its effectiveness for real-time ASR applications.
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All authors Kai-Chen Tsai, Tien-Hong Lo, Yun-Ting Sun, Berlin Chen
Signal Processing
Fast 5G Signal Acquisition by Using Non-Uniform Sampling
Alejandro Gonzalez Garrido and Carla Amatetti
This paper proposes a framework for fast signal acquisition based on deterministic non-uniform sampling, with emphasis on multi-coset architectures and receivers driven by known synchronization sequences, pilots, or preambles. Unlike conventional sampling theory, which is formulated from a waveform-reconstruction perspective, the proposed approach is derived from the observation that acquisition is fundamentally a parametric inference problem in delay-Doppler space. Accordingly, the objective is not to reconstruct the full Nyquist-rate signal, but to preserve the statistics required for detection and estimation. The paper formulates compressed-domain acquisition through a generalized likelihood ratio test and shows how multi-coset sampling leads to reduced correlator structures operating directly on the retained samples. An offline exhaustive design procedure is introduced to select the coset pattern for a given sampling ratio by minimizing a cost that jointly enforces peak isolation in the acquisition surface and uniform retained-energy coverage over the delay search interval. The framework is evaluated on 5G NR synchronization using the PSS/SSS signals under a worst-case Doppler scenario. Results show that substantial reductions in mean acquisition time can be achieved relative to uniform sampling, with measured gains ranging from 2.8x to 34.2x, depending on the selected compression ratio. The corresponding delay and Doppler root-mean-square errors quantify the estimation penalty introduced by aggressive sample reduction. These results demonstrate a clear complexity-performance trade-off and confirm the potential of multi-coset sampling for fast synchronization-oriented receivers.
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Signal Processing
Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization
Julian Withöft, Werner John, Emre Ecik et al. (+2 more)
Signal integrity (SI) analysis in printed
circuit board (PCB) interconnects faces increasing complexity due to diverse integrated
circuit (IC) buffer technologies, varying operating conditions, and manufacturing tolerances. Existing machine learning (ML) surrogate models for the prediction of SI metrics such as the inner eye contour, eye-height (EH), eye-width (EW), and transient waveform features often rely on fixed buffer parameters, limiting the adaptability across design and technology variations. This paper introduces a buffer-parameterized ML surrogate modeling methodology capable of handling cross-technology variations without retraining by treating IC buffer characteristics, e.g., clock frequency, supply voltage, rise/fall times, jitter, and internal resistors and capacitors, as model inputs in addition to the PCB parameters. To identify the optimal surrogate architecture for this high-dimensional space, tree-based methods (RFR/GBM), kernel methods (SVR/KRR), Gaussian process regression (GPR), and neural networks are compared. The buffer-parameterized approach is validated using a complex interconnect with a total of 44 design parameters. In addition to demonstrating strong generalization, the results show that neural networks outperform other ML models on large
datasets, while the anisotropic GPR excels in low-data regimes. Finally, the practical value of the ML surrogate models is demonstrated through a cross-technology design space exploration and optimization scenario, which indicates large computational speedups for eye mask compliance checking compared to simulation.
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All authors Julian Withöft, Werner John, Emre Ecik, Ralf Brüning, Jürgen Götze
Signal Processing
R$^{2}$Net: 2D Deep Residual Learning with Height Embedding for 3D Radio Map Estimation
Huiting Rao, Junyuan Wang, Huiling Zhu et al. (+1 more)
Acquiring channel knowledge is required by many applications. For instance, handover in cellular networks is mainly decided based on the knowledge of pathloss. In contrast to traditional statistical distance-determined models that might provide misleading pathloss estimates, researchers started to explore deep learning methods recently to accurately estimate the
radio map that characterizes the spatial distribution of pathloss according to the specific physical wireless propagation environment. However, existing works mainly focused on 2D
radio map estimation by assuming that all receivers are at the same height. In fact,
radio maps could be significantly different at different receiver heights, highlighting the importance of 3D
radio map estimation. In this paper, we first propose a method to embed height information into 2D images, and then propose a general 2D
radio residual network (R$^{2}$Net) for 3D
radio map estimation. Since pathloss exhibits different characteristics in indoor and outdoor scenarios, we specifically propose R$^{2}$Net-In for indoor scenarios and R$^{2}$Net-Out for outdoor scenarios to better capture penetration loss and diffraction loss, respectively. Extensive experimental results show that our R$^{2}$Net significantly outperforms the state-of-the-art
benchmarks in terms of estimation accuracy, computational and storage costs, and inference speed. In addition, due to the lack of publicly available 3D
radio map
datasets, a 3D indoor
radio map
dataset (3DiRM3200) is created, which took more than $1,000$ labour hours. The
dataset and codes will be available at https://github.com/lighttime2023/3DiRM3200.git.
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All authors Huiting Rao, Junyuan Wang, Huiling Zhu, Cheng-Xiang Wang
Signal Processing
From Coverage to Sensing: ISAC meets FR3
Ahmad Bazzi, Florian Gast, Fan Liu et al. (+3 more)
Future 6G systems are expected to exploit upper midband spectrum in frequency range 3 (FR3) not only for high throughput communications, but also for sensing services such as localization, detection, and situational awareness. The following paper develops a concrete path from today's coverage-oriented deployments to FR3 networks that treat sensing as a native function. We first show how existing FR2 radars can be time-multiplexed and coordinated under a $6$G medium access control as radar-as-a-service, forming a bridge between legacy sensing and network-managed integrated sensing and communications (
ISAC). We then propose a hierarchical FR3 beam-
alignment strategy in which coarse access occurs at lower frequencies and refinement occurs at upper FR3, and quantify the resulting sensing and communication capabilities via range-angle Cramér-Rao bounds in the near field. We identify intra- and inter-beam squint phenomena specific to wideband FR3 arrays, and discuss design approaches to mitigate them. On the signal-processing side, we argue that FR3 sensing cannot rely solely on pilot resources and discuss how much sensing information can be extracted from payload resource elements. We further highlight the role of calibrated FR3 channel simulators and real-time models as the core of wireless digital twins for training and evaluating
ISAC algorithms, and discuss how massive
MIMO and dense or distributed deployments at FR3 naturally act as large reconfigurable sensor arrays.
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All authors Ahmad Bazzi, Florian Gast, Fan Liu, Shi Jin, Gerhard Fettweis, Marwa Chafii
Signal Processing
PRiSE-EEG: A Prior-Guided Foundation Model with Depth-Stratified Experts for Cross-Paradigm EEG Representation Learning
Wei Xiong, Jiangtong Li, Kun Zhu et al. (+2 more)
EEG foundation models aim to learn reusable representations across heterogeneous paradigms, yet existing approaches often use uniform adaptation mechanisms and are typically reported under separate downstream fine-tuning protocols. In this work, we first analyze dense
EEG Transformers from two complementary perspectives. Gradient similarity across six downstream
datasets reveals substantial optimization conflicts among
EEG paradigms, while CKA analysis on mixed-paradigm batches shows a consistent depth-wise transition: shallow layers preserve stronger cross-paradigm similarity, whereas deeper layers become increasingly specialized. Motivated by these findings, we propose PRiSE-
EEG, a prior-guided
EEG foundation model with CKA-calibrated Depth-Stratified Experts. PRiSE-
EEG forms continuous multi-channel
EEG patches using weak static cortical and network priors and dynamic short-time channel interactions, then allocates shared and specialized experts across MoE
Transformer blocks according to a sigmoid mapping from layer-wise CKA sharedness. This design preserves common
EEG regularities in early blocks while assigning more specialized capacity to later task-specific transformations. Experiments on 12 public
EEG benchmarks show strong cross-paradigm performance under matched protocols. Compact ablations further show that CKA-derived expert allocation improves over dense
Transformers, uniform MoE, and manually fixed shared-specific expert ratios.
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All authors Wei Xiong, Jiangtong Li, Kun Zhu, Jie Li, Changjun Jiang
Image and Video Processing
CATRF: Codec-Adaptive TriPlane Radiance Fields for Volumetric Content Delivery
Tung-I Chen, Lingdong Wang, Subhransu Maji et al. (+1 more)
Volumetric media promises next-generation content delivery applications, but its bandwidth demand remains a key bottleneck. Implicit and hybrid volumetric representations reduce model sizes, yet still require careful coding to reach 2D
video-like bitrates. We present CATRF, a standard-codec-in-the-loop compression framework for plane-factorized radiance fields. During training, we quantize and pack 2D feature planes into codec-friendly canvases, run a standard codec roundtrip (JPEG/VP9/HEVC/AV1), then unpack and dequantize the decoded features before volume rendering. We use a straight-through
estimator (STE) to insert the non-differentiable, standard codec pipeline into the training loop, allowing radiance-field features to adapt directly to the real, client-side codec distortions without introducing any learned codec parameters. On both static and dynamic
benchmarks, CATRF consistently achieves a better rate-distortion trade-off over codec-agnostic and learned-codec-in-the-loop baselines, and also outperforms recent compressed 3DGS methods in both compression efficiency and decoding speed. These results highlight a practical path toward low-bitrate, compression-resilient volumetric representations for free-viewpoint
video streaming.
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All authors Tung-I Chen, Lingdong Wang, Subhransu Maji, Ramesh K. Sitaraman
Image and Video Processing
Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression
Chang Sun, Hui Yuan, Shiqi Jiang et al. (+3 more)
Because
LiDAR sensors acquire point clouds with a fixed angular resolution, the resulting data can be systematically parameterized and efficiently compressed in the spherical coordinate system. Traditional spherical coordinate-based point cloud compression methods have demonstrated strong rate-distortion (RD) performance, with the predictive geometry coding (PredGeom) method in the geometry-based point cloud compression (G-PCC) standard being a prominent example. Although PredGeom includes an inter-frame prediction mode, it relies on a simple linear model, which limits its ability to capture complex motion patterns and structural dependencies. Meanwhile, existing learning-based compression methods in the spherical domain do not exploit inter-frame correlations to reduce geometry redundancy. To address these limitations, we propose a learning-based inter-frame predictive coding method, termed Inter-LPCM. For azimuth prediction, we employ a delta coding strategy based on the predefined angular resolution. To improve radius compression, we introduce an inter-frame radius predictive (Inter-RP) model that estimates the current point's radius using neighboring points from both the current frame and the registered reference frame. In addition, we design a lightweight
attention-based prediction (LAEP) model to predict elevation angles by capturing long-range
geometric correlations across different coordinates. For quantization, we propose an RD-optimized method to select quantization steps in the spherical coordinate system. For
entropy coding, we design distinct models for each spherical coordinate component. These models are adapted to the statistical priors of each coordinate, enabling more accurate probability estimation. Our source code is publicly available at https://github.com/SDUChangSun/Inter-LPCM
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All authors Chang Sun, Hui Yuan, Shiqi Jiang, Chongzhen Tian, Guanghui Zhang, Raouf Hamzaoui
Audio and Speech Processing · Cross-listed from cs.CL
Bridging the Gap: Converting Read Text to Conversational Dialogue
Parshav Singla, Agnik Banerjee, Aaditya Arora et al. (+5 more)
In recent advancements within
speech processing, converting read
speech to conversational
speech has gained significant
attention. The primary challenge in this domain is maintaining naturalness and intelligibility while minimizing computational overhead for real-time applications. Traditional read
speech often lacks the nuanced prosodic variation essential for natural conversational interactions, posing challenges for applications in virtual assistants, customer service, and
language learning tools. This paper introduces a novel approach, Prosodic Adjustment with Conversational Context (PACC), aimed at converting read
speech into natural conversational
speech used in various modern applications. PACC utilizes advanced deep neural networks to analyze and modify prosodic features such as intonation, stress, and rhythm. Unlike conventional methods, our approach uses High-Fidelity Generative
Adversarial Networks (HiFi-GAN) for
speech synthesis. Our experimental results demonstrate significant improvements in
speech conversion, enhancing naturalness and achieving better model accuracy with additional training on
speech datasets. This research establishes new
benchmarks in
speech conversion tasks and Mean Opinion Score (MOS) evaluation for testing model accuracy, and we show that our approach can be successfully extended to other
speech conversion applications.
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All authors Parshav Singla, Agnik Banerjee, Aaditya Arora, Shruti Aggarwal, Anil Kumar Verma, Vikram C M, Raj Prakash Gohil, Gopal Kumar Agarwal
Signal Processing
A Computationally Efficient Reciprocal Effective Roughness Model for Diffuse Scattering
Giacomo Melloni, Enrico M. Vitucci, Vittorio Degli Esposti et al. (+4 more)
Ray-tracing (RT) has become central to site-specific electromagnetic propagation modeling in dynamic complex environments. Yet its computational burden grows sharply as high-fidelity digital twins of these environments scale to millions of facets whose material parameters must be continuously updated as the environment changes. The challenge is amplified at mmWave and sub-THz frequencies, where surface roughness becomes comparable to the wavelength and so diffuse scattering can account for up to 40% of the received power, making accurate yet tractable models essential. The popular Effective Roughness (ER) approach offers physical consistency but become increasingly costly when highly directive lobes are required or when parameters must be iteratively tuned. This communication introduces a directive, reciprocal diffuse scattering model that preserves the structure of the ER while enabling an order-of-magnitude reduction in computational cost. Validation across eight materials shows no loss in accuracy - and a slight improvement - demonstrating a scalable and physically meaningful solution for RT in scenarios where diffuse scattering is non-negligible.
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All authors Giacomo Melloni, Enrico M. Vitucci, Vittorio Degli Esposti, Samuel Berweger, Jack Chuang, Camillo Gentile, Nada Golmie
Image and Video Processing
See Silhouettes in Motion with Neuromorphic Vision
Pei Zhang, Shijie Lin, Zhou Ge et al. (+2 more)
Quasi-bimodal objects, such as text, road signs, and barcodes, play a basic yet vital role in daily visual communication. By boiling these down to clear silhouettes, binarization uses a minimal
language to convey essential
vision cues for maximum downstream efficiency, especially for tasks that require simple
geometric, topological
reasoning rather than heavy appearance modeling. The catch is that frame-based imaging often struggles on mobile platforms like drones, self-driving cars, and
underwater vehicles, in which rapid motion causes severe motion blur and harsh lighting washes out scene details. To overcome these physical limits, neuromorphic
vision via event cameras, featuring microsecond time resolution and high dynamic range, steps in as a natural solution. Building upon this event-driven paradigm, we propose a simple yet effective dual-modal approach that harnesses the synergy between frames and events for training-free, real-time, high-frame-rate binarization on CPU-only devices. Extensive evaluations show that it earns competitive performance against leading techniques in reducing blur artifacts and delivers impressive improvements under challenging illumination at a lower computational cost. Besides, its asynchronous nature bypasses long-standing event-scarcity issues that break traditional time-binning reconstruction at fixed time slots, maintaining clear target shapes even at extreme kilohertz frame rates. Its binary results further serve as reliable representations to facilitate a range of downstream tasks. This work paves the way towards lightweight perception and interaction in
embodied intelligence on resource-constrained edge platforms.
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All authors Pei Zhang, Shijie Lin, Zhou Ge, Jinpeng Chen, Wei Pu
Audio and Speech Processing
Fractional-Order Subband p-Norm Adaptive Filter via Transformation Nearest Kronecker Product Decomposition for Active Noise Control
Jianhong Ye, Haiquan Zhao, Shaohui Lv et al. (+1 more)
The conventional normalized subband p-norm (NSPN)
algorithm achieves robustness in $\alpha$-stable noise ($1<\alpha \leq 2$) by utilizing low-order error moments. However, its performance degrades significantly under three scenarios: (1) non-Gaussian inputs, (2) $\alpha$-stable noise with $0<\alpha \leq 1$, and (3) sparse system identification. To address these limitations, this paper proposes a fractional-order NSPN
algorithm based on the nearest Kronecker product (NKP) decomposition and fractional-order
stochastic gradient descent, termed NKP-FoNSPN. Theoretical bounds for the fractional-order parameter $\beta$ are also derived. Notably, when $\beta=1$, the NKP-FoNSPN reduces to a new NKP-NSPN
algorithm, while its non-NKP decomposition variant becomes the fractional-order NSPN (FoNSPN)
algorithm. Furthermore, a novel transformation-based NKP (TNKP) decomposition technique is designed, which exhibits lower computational complexity than conventional NKP for specific filter structures. The resulting TNKP-based FoNSPN (TNKP-FoNSPN) achieves lower steady-state misadjustment and multiplication cost compared with the NKP-FoNSPN
algorithm. Additionally, complete computational complexity analyses are provided. For active noise control (ANC) scenarios, we develop filtered-x variants: NKP-FxFoNSPN and TNKP-FxFoNSPN. From the former, two additional variants are derived: NKP-FxNSPN and FxFoNSPN. Simulations using diverse noise sources (pink, helicopter, gunshot, pile driver, and traction substation noise) demonstrate the superiority of the proposed
algorithms. Finally, we validate their noise reduction performance in a real constructed single-channel duct ANC and a simulated multi-channel ANC systems.
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All authors Jianhong Ye, Haiquan Zhao, Shaohui Lv, Yang Zhou