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distributed signal processing for dense 5G IoT platforms: Networking, synchronization, interference detection and radio sensing

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AD HOC NETWORKS 2019年第0期89卷 9-21页

作者： Soatti, Gloria Savazzi, Stefano Nicoli, Monica Antonieta Alvarez, Maria Kianoush, Sanaz Rampa, Vittorio Spagnolini, Umberto Politecn Milan DEIB Pzza Lda Vinci 32 I-20133 Milan Italy CNR IEIIT Inst Pzza Lda Vinci 32 I-20133 Milan Italy Politecn Milan DIG Pzza Lda Vinci 32 I-20133 Milan Italy Escuela Super Politecn Litoral ESPOL Guayaquil Ecuador

Driven by the fourth industrial revolution (Industry 4.0), future and emerging Internet of Things (IoT) technologies will be required to support unprecedented services and demanding applications for massive machine-type connectivity, with low latency, high reliability and distributed information processing capability. In this article, distributed signal processing methodologies are highlighted as enablers for next generation cloud-assisted IoT systems. The proposed distributed algorithms run inside a wireless cloud network (WCN) platform and are exploited for WCN self-organization, distributed synchronization, networking and sensing. The WCN can lease augmented communication and sensing services to off-the-shelf industrial wireless devices via a dense, self-organizing "cloud" of wireless nodes. The paper introduces, at first, the WCN architecture and illustrates an experimental case study inside a pilot industrial plant. Next, it proposes a re-design of consensus-based algorithms for enabling a selected set of distributed information processing functionalities within the WCN platform, with application in practical loT scenarios. In particular, cooperative communication algorithms are adopted to support reliable communication services. distributed timing and carrier frequency offset estimation methods are investigated to enable low-latency services through accurate synchronization. Autonomous identification of recurring interference patterns is proposed for multiple access coordination in the shared 5G spectrum. Finally, localization and vision applications based on distributed processing of wireless signals are investigated to support contact-free human-machine interfaces. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Dense cooperative networks distributed consensus distributed synchronization distributed signal processing Internet of things

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distributed signal processing for Wireless EEG Sensor Networks

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2015年第6期23卷 923-935页

作者： Bertrand, Alexander Katholieke Univ Leuven Stadius Ctr Dynam Syst Signal Proc & Data Analyt Dept Elect Engn ESAT B-3001 Leuven Belgium iMinds Med Informat Technol B-3001 Leuven Belgium

Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.

关键词： distributed estimation distributed signal processing electroencephalography (EEG) neuromonitoring wireless body area networks (WBANs) wireless electroencephalography (EEG) sensor networks

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distributed signal processing Architecture for Real-Time Convolution of 3D Audio Rendering for Mobile Applications 15th

Distributed Signal Processing Architecture for Real-Time Con...

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15th Annual EuroVR International Conference (EuroVR) on Virtual Reality and Augmented Reality

作者： Iwaya, Yukio Katz, Brian F. G. Tohoku Gakuin Univ Tajao Miyagi Japan Sorbonne Univ CNRS Inst Jean Le Rond dAlembert Paris France

ISBN: (纸本)9783030017903;9783030017897

By convolving an audio stream with a given pair of impulse responses between a source position and the two ears, virtual sound scenes can be created over headphones. Typically, the set of these filters for an ensemble of spatial positions, termed the Head-Related Impulse Response (HRIR) is used to render position information of a sound object to a listener. However, HRIRs are measured in free-field conditions, ignoring room reflections. In the real world, multiple reflections and reverberation exist, producing complex rich sound spaces. Including room reflections and reverberation with the HRIR results in a binaural room impulse response (BRIR). The length of a given BRIR depend on the shape and volume of the room, with BRIRs having typical duration of several seconds, resulting in computationally long processing. When the virtual environment is updated in response to head/body movement, BRIRs need to be updated according to the relative direction of a sound object within the perceptual detection threshold of system latency. This poses complications for mobile devices where processing power is limited, such as the case of augmented reality. In this paper, the architecture of a new signal processing method by distributed computers is proposed for convolution of BRIRs applicable to such conditions.

关键词： Three-dimensional sound rendering Binaural room impulse responses distributed signal processing Real-time rendering

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Randomized data selection in detection with applications to distributed signal processing

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PROCEEDINGS OF THE IEEE 2003年第8期91卷 1184-1198页

作者： Sestok, CK Said, MR Oppenheim, AV MIT Elect Res Lab Cambridge MA 02139 USA

Performing robust detection with resource limitations such as low-power requirements or limited communication bandwidth is becoming increasingly important in contexts involving distributed signal processing. One way to address these constraints consists of reducing the amount of data used by the detection algorithms. Intelligent data selection in detection can be highly dependent on a priori information about the signal and noise. In this paper we explore detection strategies based on randomized data selection and analyze the resulting algorithms' performance. Randomized data selection is a viable approach in the absence of reliable and detailed a priori information, and it provides a reasonable lower bound on signal processing performance as more a priori information is incorporated. The randomized selection procedure has the added benefits of simple implementation in a distributed environment and limited communication overhead. As an example of detection algorithms based upon randomized selection, we analyze a binary hypothesis testing problem, and determine several useful properties of detectors derived from the likelihood ratio test. Additionally, we suggest an adaptive detector that accounts for fluctuations in the selected data subset. The advantages and disadvantages of this approach in distributed sensor networks applications are also discussed.

关键词： distributed signal processing likelihood ratio test nonparametric detection randomized algorithms randomized sampling sensor networks

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ON THE DUALITY OF GLOBALLY CONSTRAINED SEPARABLE PROBLEMS AND ITS APPLICATION TO distributed signal processing

ON THE DUALITY OF GLOBALLY CONSTRAINED SEPARABLE PROBLEMS AN...

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European signal processing Conference

作者： Thomas Sherson Richard Heusdens W. Bastiaan Kleijn Faculty of EEMCS Delft University of Technology

ISBN: (纸本)9781509018918

In this paper, we focus on the challenge of processing data generated within decentralised wireless sensor networks in a distributed manner. When the desired operations can be expressed as globally constrained separable convex optimisation problems, we show how we can convert these to extended monotropic programs and exploit Lagrangian duality to form equivalent distributed consensus problems. Such problems can be embedded in sensor network applications via existing solvers such as the alternating direction method of multipliers or the primal dual method of multipliers. We then demonstrate how this approach can be used to solve specific problems including linearly constrained quadratic problems and the classic Gaussian channel capacity maximisation problem in a distributed manner.

关键词： Wireless sensor networks distributed signal processing Lagrangian duality Extended monotropic programs

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distributed Adaptive signal Fusion for Fractional Programs

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2025年 11卷 258-273页

作者： Musluoglu, Cem Ates Bertrand, Alexander Katholieke Univ Leuven Stadius Ctr Dynam Syst Signal Proc & Data Analyt Dept Elect Engn ESAT B-3001 Leuven Belgium Leuven AI KU Leuven Inst AI B-3001 Leuven Belgium

The distributed adaptive signal fusion (DASF) is an algorithmic framework that allows solving spatial filtering optimization problems in a distributed and adaptive fashion over a bandwidth-constrained wireless sensor network. The DASF algorithm requires each node to sequentially build a compressed version of the original network-wide problem and solve it locally. However, these local problems can still result in a high computational load at the nodes, especially when the required solver is iterative. In this paper, we study the particular case of fractional programs, i.e., problems for which the objective function is a fraction of two continuous functions, which indeed require such iterative solvers. By exploiting the structure of a commonly used method for solving fractional programs and interleaving it with the iterations of the standard DASF algorithm, we obtain a distributed algorithm with a significantly reduced computational cost compared to the straightforward application of DASF as a meta-algorithm. We prove convergence and optimality of this "fractional DASF" (F-DASF) algorithm and demonstrate its performance via numerical simulations.

关键词： Wireless sensor networks Optimization Iterative methods Convergence Indexes Linear programming Europe Artificial intelligence Reviews Information processing distributed optimization distributed signal processing spatial filtering signal fusion wireless sensor networks fractional programming

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distributed Adaptive Spatial Filtering With Inexact Local Solvers

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1262-1277页

作者： Hovine, Charles Bertrand, Alexander STADIUS Ctr Dynam Syst Signal Proc & Data Analyt B-3001 Leuven Belgium Katholieke Univ Leuven Leuven AI Inst Artificial Intelligence B-3001 Leuven Belgium

The distributed Adaptive signal Fusion (DASF) framework is a meta-algorithm for computing data-driven spatial filters in a distributed sensing platform with limited bandwidth and computational resources, such as a wireless sensor network. The convergence and optimality of the DASF algorithm has been extensively studied under the assumption that an exact, but possibly impractical solver for the local optimization problem at each updating node is available. In this work, we provide convergence and optimality results for the DASF framework when used with an inexact, finite-time solver such as (proximal) gradient descent or Newton's method. We provide sufficient conditions that the solver should satisfy in order to guarantee convergence of the resulting algorithm as well as numerical simulations to validate these theoretical results.

关键词： signal processing algorithms Convergence Wireless sensor networks Optimization distributed databases Principal component analysis Europe Artificial intelligence Adaptive systems Spatial filters distributed signal processing wireless sensor networks adaptive spatial filtering optimization

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A Unified Algorithmic Framework for distributed Adaptive signal and Feature Fusion Problems-Part I: Algorithm Derivation

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1863-1878页

In this article, we describe a general algorithmic framework for solving linear signal or feature fusion optimization problems in a distributed setting, for example in a wireless sensor network (WSN). These problems require linearly combining the observed signals (or features thereof) collected at the various sensor nodes to satisfy a pre-defined optimization criterion. The framework covers several classical spatial filtering problems, including minimum variance beamformers, multi-channel Wiener filters, principal component analysis, canonical correlation analysis, (generalized) eigenvalue problems, etc. The proposed distributed adaptive signal fusion (DASF) algorithm is an iterative method that solves these types of problems by allowing each node to share a linearly compressed version of the local sensor signal observations with its neighbors to reduce the energy and bandwidth requirements of the network. We first discuss the case of fully-connected networks and then extend the analysis to more general network topologies. The general DASF algorithm is shown to have several existing distributed algorithms from the literature as a special case, while at the same time allowing to solve new distributed problems as well with guaranteed convergence and optimality. This article focuses on the algorithm derivation of the DASF framework along with simulations demonstrating its performance. A technical analysis along with convergence conditions and proofs are provided in a companion article.

关键词： distributed optimization distributed signal processing spatial filtering signal fusion feature fusion wireless sensor networks

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distributed combined acoustic echo cancellation and noise reduction using GEVD-based distributed adaptive node specific signal estimation with prior knowledge 28

Distributed combined acoustic echo cancellation and noise re...

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28th European signal processing Conference (EUSIPCO)

作者： Ruiz, Santiago van Waterschoot, Toon Moonen, Marc Katholieke Univ Leuven ESAT STADIUS Dept Elect Engn Leuven Belgium

ISBN: (纸本)9789082797053

distributed combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor network (WASN) is tackled by using a specific version of the PK-GEVD-DANSE algorithm (cfr. [1]). Although this algorithm was initially developed for distributed NR with partial prior knowledge of the desired speech steering vector, it is shown that it can also be used for AEC combined with NR. Simulations have been carried out using centralized and distributed batch-mode implementations to verify the performance of the algorithm in terms of AEC quantified with the echo return loss enhancement (ERLE), as well as in terms of the NR quantified with the signal-to-noise ratio (SNR).

关键词： distributed signal processing wireless acoustic sensor networks acoustic echo cancellation noise reduction

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Improved Tracking for distributed signal Fusion Optimization in a Fully-Connected Wireless Sensor Network 30

Improved Tracking for Distributed Signal Fusion Optimization...

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30th European signal processing Conference (EUSIPCO)

作者： Musluoglu, Cem Ates Moonen, Marc Bertrand, Alexander Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS Ctr Dynam Syst Signal Proc & Data Analyt Leuven Belgium

ISBN: (纸本)9789082797091

The distributed adaptive signal fusion (DASF) algorithm is a generic algorithm that can be used to solve various spatial signal and feature fusion optimization problems in a distributed setting such as a wireless sensor network. Examples include principal component analysis, adaptive beamforming, and source separation problems. While the DASF algorithm adaptively learns the relevant second order statistics from the collected sensor data, accuracy problems can arise if the spatial covariance structure of the signals is rapidly changing. In this paper, we propose a method to improve the tracking or convergence speed of the DASF algorithm in a fully-connected sensor network with a broadcast communication protocol. While the improved tracking increases communication cost, we demonstrate that this tradeoff is efficient in the sense that an L-fold increase in bandwidth results in an R times faster convergence with R >> L.

关键词： distributed signal processing distributed Optimization distributed Spatial Filtering Feature Fusion

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