Signal Processing

New submissions

• New submissions
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New submissions for Thu, 9 May 24

[1]  arXiv:2405.04704 [pdf, other]

Title: System Identification of the Upgraded LHPOST6 Reaction Mass at the University of California San Diego

Authors: Andres Rodriguez-Burneo, Jose I. Restrepo, Joel P. Conte

Comments: 38 pages, 35 figures

Subjects: Signal Processing (eess.SP)

Upon completing the upgrade from one to six degrees of freedom of the Outdoor Shake Table at UCSD in 2019, forced vibration tests were carried out to identify the dynamic characteristics of the reaction mass and soil system. This report describes the motivation, execution, and results from such tests, which independently excited the reaction mass in four degrees of freedom: longitudinal, transverse, yaw, and vertical. The report discusses the frequency response curves and deformation patterns from which the natural frequencies, damping ratio, mode shapes, and rigid body motion were determined. The first objective of the study was to investigate if the dynamic properties of the system had dramatically changed after the upgrade by comparing the results to those from forced vibration tests performed 20 years ago, during the construction of the facility. In addition, most recent tests also contributed with results from the vertical degree of freedom, which had never been tested. The second objective was to obtain high-quality response data of the system that will be used to develop a high-fidelity computational model of the reaction mass in future research. A comparison of results showed a slight difference of 0.5Hz in the natural frequency of 2 degrees of freedom. Moreover, maximum displacements in the recent tests were overall larger than the previous ones with few exceptions. The report thoroughly discusses the several sources of discrepancy between the past and most recent results. Finally, test results allowed us to estimate the system's response if the shake table actuators were to be used at their maximum nominal capacity. Small displacement and high damping results were consistent with those of previous tests and further validated the design of the reaction mass.

[2]  arXiv:2405.04962 [pdf, other]

Title: Bistatic OFDM-based ISAC with Over-the-Air Synchronization: System Concept and Performance Analysis

Authors: David Brunner, Lucas Giroto de Oliveira, Charlotte Muth, Silvio Mandelli, Marcus Henninger, Axel Diewald, Yueheng Li, Mohamad Basim Alabd, Laurent Schmalen, Thomas Zwick, Benjamin Nuss

Subjects: Signal Processing (eess.SP)

Integrated sensing and communication (ISAC) has been defined as one goal for 6G mobile communication systems. In this context, this article introduces a bistatic ISAC system based on orthogonal frequency-division multiplexing (OFDM). While the bistatic architecture brings advantages such as not demanding full duplex operation with respect to the monostatic one, the need for synchronizing transmitter and receiver is imposed. In this context, this article introuces a bistatic ISAC signal processing framework where an incoming OFDM-based ISAC signal undergoes over-the-air synchronization based on preamble symbols and pilots. Afterwards, bistatic radar processing is performed using either only pilot subcarriers or the full OFDM frame. The latter approach requires estimation of the originally transmitted frame based on communication processing and therefore error-free communication, which can be achieved via appropriate channel coding. The performance and limitations of the introduced system based on both aforementioned approaches are assessed via an analysis of the impact of residual synchronization mismatches and data decoding failures on both communication and radar performances. Finally, the performed analyses are validated by proof-of-concept measurement results.

[3]  arXiv:2405.05157 [pdf, ps, other]

Title: Filtering and smoothing estimation algorithms from uncertain nonlinear observations with time-correlated additive noise and random deception attacks

Authors: R. Caballero-Águila, J. Hu, J. Linares-Pérez

Journal-ref: International Journal of Systems Science, March 19 2024

Subjects: Signal Processing (eess.SP)

This paper discusses the problem of estimating a stochastic signal from nonlinear uncertain observations with time-correlated additive noise described by a first-order Markov process. Random deception attacks are assumed to be launched by an adversary, and both this phenomenon and the uncertainty in the observations are modelled by two sets of Bernoulli random variables. Under the assumption that the evolution model generating the signal to be estimated is unknown and only the mean and covariance functions of the processes involved in the observation equation are available, recursive algorithms based on linear approximations of the real observations are proposed for the least-squares filtering and fixed-point smoothing problems. Finally, the feasibility and effectiveness of the developed estimation algorithms are verified by a numerical simulation example, where the impact of uncertain observation and deception attack probabilities on estimation accuracy is evaluated.

[4]  arXiv:2405.05234 [pdf, other]

Title: Performance Bounds for Velocity Estimation with Large Antenna Arrays

Authors: Caterina Giovannetti, Nicolò Decarli, Davide Dardari

Comments: 5 pages, 5 figures

Subjects: Signal Processing (eess.SP)

Joint communication and sensing (JCS) is envisioned as an enabler of future 6G networks. One of the key features of these networks will be the use of extremely large aperture arrays (ELAAs) and high operating frequencies, which will result in significant near-field propagation effects. This unique property can be harnessed to improve sensing capabilities. In this paper, we focus on velocity sensing, as using ELAAs allows the estimation of not just the radial component but also the transverse component. We derive analytical performance bounds for both velocity components, demonstrating how they are affected by the different system parameters and geometries. These insights offer a foundational understanding of how near-field effects play in velocity sensing differently from the far field and from position estimate.

Cross-lists for Thu, 9 May 24

[5]  arXiv:2405.04539 (cross-list from stat.ML) [pdf, other]

Title: Some variation of COBRA in sequential learning setup

Authors: Aryan Bhambu, Arabin Kumar Dey

Subjects: Machine Learning (stat.ML); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Signal Processing (eess.SP); Computational Finance (q-fin.CP)

This research paper introduces innovative approaches for multivariate time series forecasting based on different variations of the combined regression strategy. We use specific data preprocessing techniques which makes a radical change in the behaviour of prediction. We compare the performance of the model based on two types of hyper-parameter tuning Bayesian optimisation (BO) and Usual Grid search. Our proposed methodologies outperform all state-of-the-art comparative models. We illustrate the methodologies through eight time series datasets from three categories: cryptocurrency, stock index, and short-term load forecasting.

[6]  arXiv:2405.04865 (cross-list from cs.LG) [pdf, ps, other]

Title: Regime Learning for Differentiable Particle Filters

Authors: John-Joseph Brady, Yuhui Luo, Wenwu Wang, Victor Elvira, Yunpeng Li

Subjects: Machine Learning (cs.LG); Signal Processing (eess.SP)

Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference. This paper concerns the case where the system may switch between a finite set of state-space models, i.e. regimes. No prior approaches effectively learn both the individual regimes and the switching process simultaneously. In this paper, we propose the neural network based regime learning differentiable particle filter (RLPF) to address this problem. We further design a training procedure for the RLPF and other related algorithms. We demonstrate competitive performance compared to the previous state-of-the-art algorithms on a pair of numerical experiments.

[7]  arXiv:2405.04976 (cross-list from cs.IT) [pdf, other]

Title: RF-based Energy Harvesting: Nonlinear Models, Applications and Challenges

Authors: Ruihong Jiang

Subjects: Information Theory (cs.IT); Signal Processing (eess.SP)

So far, various aspects associated with wireless energy harvesting (EH) have been investigated from diverse perspectives, including energy sources and models, usage protocols, energy scheduling and optimization, and EH implementation in different wireless communication systems. However, a comprehensive survey specifically focusing on models of radio frequency (RF)-based EH behaviors has not yet been presented. To address this gap, this article provides an overview of the mainstream mathematical models that capture the nonlinear behavior of practical EH circuits, serving as a valuable handbook of mathematical models for EH application research. Moreover, we summarize the application of each nonlinear EH model, including the associated challenges and precautions. We also analyze the impact and advancements of each EH model on RF-based EH systems in wireless communication, utilizing artificial intelligence (AI) techniques. Additionally, we highlight emerging research directions in the context of nonlinear RF-based EH. This article aims to contribute to the future application of RF-based EH in novel communication research domains to a significant extent.

[8]  arXiv:2405.05252 (cross-list from cs.CV) [pdf, other]

Title: Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models

Authors: Hongjie Wang, Difan Liu, Yan Kang, Yijun Li, Zhe Lin, Niraj K. Jha, Yuchen Liu

Comments: Accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024

Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV); Signal Processing (eess.SP)

Diffusion Models (DMs) have exhibited superior performance in generating high-quality and diverse images. However, this exceptional performance comes at the cost of expensive architectural design, particularly due to the attention module heavily used in leading models. Existing works mainly adopt a retraining process to enhance DM efficiency. This is computationally expensive and not very scalable. To this end, we introduce the Attention-driven Training-free Efficient Diffusion Model (AT-EDM) framework that leverages attention maps to perform run-time pruning of redundant tokens, without the need for any retraining. Specifically, for single-denoising-step pruning, we develop a novel ranking algorithm, Generalized Weighted Page Rank (G-WPR), to identify redundant tokens, and a similarity-based recovery method to restore tokens for the convolution operation. In addition, we propose a Denoising-Steps-Aware Pruning (DSAP) approach to adjust the pruning budget across different denoising timesteps for better generation quality. Extensive evaluations show that AT-EDM performs favorably against prior art in terms of efficiency (e.g., 38.8% FLOPs saving and up to 1.53x speed-up over Stable Diffusion XL) while maintaining nearly the same FID and CLIP scores as the full model. Project webpage: https://atedm.github.io.

Replacements for Thu, 9 May 24

[9]  arXiv:2211.04019 (replaced) [pdf, other]

Title: Dynamic Sensor Placement Based on Graph Sampling Theory

Authors: Saki Nomura, Junya Hara, Hiroshi Higashi, Yuichi Tanaka

Subjects: Signal Processing (eess.SP)

[10]  arXiv:2212.07484 (replaced) [pdf, other]

Title: Joint Delay-Phase Precoding Under True-Time Delay Constraints in Wideband Sub-THz Hybrid Massive MIMO Systems

Authors: Dang Qua Nguyen, Taejoon Kim

Subjects: Signal Processing (eess.SP)

[11]  arXiv:2303.02408 (replaced) [pdf, ps, other]

Title: Data Augmentation for Generating Synthetic Electrogastrogram Time Series

Authors: Nadica Miljković, Nikola Milenić, Nenad B. Popović, Jaka Sodnik

Journal-ref: Med.Biol.Eng.Comput.(2024):1-13

Subjects: Signal Processing (eess.SP)

[12]  arXiv:2308.08946 (replaced) [pdf, other]

Title: FR2 5G Networks for Industrial Scenarios: Experimental Characterization and Beam Management Procedures in Operational Conditions

Authors: Alejandro Ramírez-Arroyo, Melisa López, Ignacio Rodríguez, Troels B. Sørensen, Samantha Caporal del Barrio, Pablo Padilla, Juan F. Valenzuela-Valdés, Preben Mogensen

Comments: Published in IEEE Transactions on Vehicular Technology, 2024

Subjects: Signal Processing (eess.SP)

[13]  arXiv:2310.14465 (replaced) [pdf, ps, other]

Title: Channel State Information-Free Location-Privacy Enhancement: Delay-Angle Information Spoofing

Authors: Jianxiu Li, Urbashi Mitra

Subjects: Signal Processing (eess.SP)

[14]  arXiv:2311.05532 (replaced) [pdf, other]

Title: Uncertainty-Aware Bayes' Rule and Its Applications

Authors: Shixiong Wang

Subjects: Signal Processing (eess.SP); Methodology (stat.ME)

[15]  arXiv:2402.00329 (replaced) [pdf, ps, other]

Title: Optimized Parameter Design for Channel State Information-Free Location Spoofing

Authors: Jianxiu Li, Urbashi Mitra

Comments: arXiv admin note: text overlap with arXiv:2310.14465

Subjects: Signal Processing (eess.SP)

[16]  arXiv:2402.11983 (replaced) [pdf, other]

Title: Antenna Array Design for Mono-Static ISAC

Authors: Alexander Felix, Silvio Mandelli, Marcus Henninger, Stephan ten Brink

Comments: 5 pages, 5 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Subjects: Signal Processing (eess.SP)

[17]  arXiv:2308.05384 (replaced) [pdf, other]

Title: Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

Authors: Hongyang Du, Ruichen Zhang, Yinqiu Liu, Jiacheng Wang, Yijing Lin, Zonghang Li, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shuguang Cui, Bo Ai, Haibo Zhou, Dong In Kim

Comments: This paper has been accepted by IEEE Communications Surveys & Tutorials (COMST)

Subjects: Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)

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