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A distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems in Wireless Sensor Networks

IEEE TRANSACTIONS ON signal processing 2024年 72卷 4682-4697页

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

A wireless sensor network often relies on a fusion center to process the data collected by each of its sensing nodes. Such an approach relies on the continuous transmission of raw data to the fusion center, which typically has a major impact on the sensors' battery life. To address this issue in the particular context of spatial filtering and signal fusion problems, we recently proposed the distributed Adaptive signal Fusion (DASF) algorithm, which distributively computes a spatial filter expressed as the solution of a smooth optimization problem involving the network-wide sensor signal statistics. In this work, we show that the DASF algorithm can be extended to compute the filters associated with a certain class of non-smooth optimization problems. This extension makes the addition of sparsity-inducing norms to the problem's cost function possible, allowing sensor selection to be performed in a distributed fashion, alongside the filtering task of interest, thereby further reducing the network's energy consumption. We provide a description of the algorithm, prove its convergence, and validate its performance and solution tracking capabilities with numerical experiments.

关键词： signal processing algorithms Wireless sensor networks Optimization Spatial filters Linear programming Europe distributed databases Real-time systems Convergence Adaptive systems distributed signal processing non-smooth optimization spatial filtering

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distributed Distortion-Aware Beamforming Designs for Cell-Free mMIMO Systems

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第2期19卷 381-397页

作者： Liu, Mengzhen Li, Ming Liu, Rang Liu, Qian Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Univ Calif Irvine Ctr Pervas Commun & Comp Irvine CA 92697 USA Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

Cell-free massive multi-input multi-output (CF-mMIMO) systems have emerged as a promising paradigm for next-generation wireless communications, offering enhanced spectral efficiency and coverage through distributed antenna arrays. However, the non-linearity of power amplifiers (PAs) in these arrays introduce spatial distortion, which may significantly degrade system performance. This paper presents the first investigation of distortion-aware beamforming in a distributed framework tailored for CF-mMIMO systems, enabling pre-compensation for beam dispersion caused by nonlinear PA distortion. Using a third-order memoryless polynomial distortion model, the impact of the nonlinear PA on the performance of CF-mMIMO systems is firstly analyzed by evaluating the signal-to-interference-noise-and-distortion ratio (SINDR) at user equipment (UE). Then, we develop two distributed distortion-aware beamforming designs based on ring topology and star topology, respectively. In particular, the ring-topology-based fully-distributed approach reduces interconnection costs and computational complexity, while the star-topology-based partially-distributed scheme leverages the superior computation capability of the central processor to achieve improved sum-rate performance. Extensive simulations demonstrate the effectiveness of the proposed distortion-aware beamforming designs in mitigating the effect of nonlinear PA distortion, while also reducing computational complexity and backhaul information exchange in CF-mMIMO systems.

关键词： Nonlinear distortion Array signal processing Matrix decomposition Vectors signal processing algorithms Interference Backhaul networks Analytical models Radio frequency Phase distortion Cell-free nonlinear distortion beamforming distributed signal processing

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Design and Implementation of Ambiently Powered Internet of Things-That-Think With Asynchronous Inference

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IEEE INTERNET OF THINGS JOURNAL 2023年第17期10卷 15283-15300页

作者： Papageorgiou, Vasileios Nichoritis, Athanasios Vasilakopoulos, Panagiotis Vougioukas, Georgios Bletsas, Aggelos Tech Univ Crete Sch ECE Khania 73100 Greece Univ Wisconsin Madison CS Dept Madison WI 53706 USA Tech Univ Crete Telecommun Syst Inst Khania 73100 Greece

This work offers design and implementation of in-network inference, using message passing among ambiently powered wireless sensor network (WSN) terminals. The stochastic nature of ambient energy harvesting dictates intermittent operation of each WSN terminal and as such, the message passing inference algorithms should be robust to asynchronous operation. It is shown, perhaps for the first time in the literature (to the best of our knowledge), a proof of concept, where a WSN harvests energy from the environment and processes itself the collected information in a distributed manner, by converting the (network) inference task to a probabilistic, in-network message passing problem, often at the expense of increased total delay. Examples from Gaussian belief propagation and average consensus (AC) are provided, along with the derivation of a statistical convergence metric for the latter case. A k-means method is offered that maps the elements of the calculated vector to the different WSN terminals and overall execution delay (in number of iterations) is quantified. Interestingly, it is shown that there are divergent instances of the in-network message passing algorithms that become convergent, under asynchronous operation. Ambient solar energy harvesting availability is also studied, controlling the probability of successful (or not) message passing. Hopefully, this work will spark further interest for asynchronous message passing algorithms and technologies that enable in-network inference, toward ambiently powered, batteryless Internet of Things-That-Think.

关键词： Wireless sensor networks Message passing Inference algorithms Cloud computing Internet of Things Radio frequency Convergence distributed signal processing energy harvesting in situ processing low-cost sensors and device resourceconstrained networks

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distributed Alignment Processes With Samples of Group Average

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2023年第2期10卷 960-971页

作者： Korman, Amos Vacus, Robin HUJI WIS Int Joint Res Unit CNRS UMI FILOFOCSUP7French Israeli Lab Fdn Comp SciT Tel Aviv Israel Res Inst Fdn Comp Sci CNRS Paris France

This article studies a stochastic alignment problem assuming that agents can sense the general tendency of the system. More specifically, we consider $n$ agents, each being associated with a real number. In each round, each agent receives a noisy measurement of the system's average value and then updates its value. This value is then perturbed by random drift. We assume that both noise and drift are Gaussian. We prove that a distributed weighted-average algorithm optimally minimizes the deviation of each agent from the average value, and for every round. Interestingly, this optimality holds even in the centralized setting, where a master agent can gather all the agents' measurements and instruct a move to each one. We find this result surprising since it can be shown that the set of measurements obtained by all agents contains strictly more information about the deviation of Agent $i$ from the average value, than the information contained in the measurements obtained by Agent $i$ alone. Although this information is relevant for Agent $i$, it is not processed by it when running a weighted-average algorithm. Finally, we also analyze the drift of the center of mass and show that no distributed algorithm can achieve drift that is as small as the one that can be achieved by the best centralized algorithm.

关键词： Noise measurement Current measurement Weight measurement Synchronization signal processing algorithms Protocols Position measurement Biological distributed algorithms clock synchronization consensus distributed signal processing flocking Kalman filter noisy communication weighted-average algorithms

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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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distributed Combined Acoustic Echo Cancellation and Noise Reduction in Wireless Acoustic Sensor and Actuator Networks

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2022年 30卷 534-547页

作者： Ruiz, Santiago van Waterschoot, Toon Moonen, Marc Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS B-3001 Leuven Belgium

The paper presents distributed algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor and actuator network (WASAN) where each node may have multiple microphones and multiple loudspeakers, and where the desired signal is a speech signal. A centralized integrated AEC and NR algorithm, i.e., multichannel Wiener filter (MWF), is used as starting point where echo signals are viewed as background noise signals and loudspeaker signals are used as additional input signals to the algorithm. By including prior knowledge (PK), namely that the loudspeaker signals do not contain any desired signal component, an alternative centralized cascade algorithm (PK-MWF) is obtained with an AEC stage first followed by an MWF-based NR stage which has a lower computational complexity. distributed algorithms can then be obtained from the MWF and PK-MWF algorithm, i.e., the generalized eigenvalue decomposition (GEVD)-based distributed adaptive node-specific signal estimation (DANSE) and PK-GEVD-DANSE algorithm, respectively. In the former, each node performs a reduced dimensional integrated AEC and NR algorithm and broadcasts only 1 fused signal (instead of all its signals) to the other nodes. In the PK-GEVD-DANSE algorithm, each node performs a reduced dimensional cascade AEC and NR algorithm and broadcasts only 2 fused signals (instead of all its signals) to the other nodes. The distributed algorithms achieve the same performance, upon convergence, as the corresponding centralized integrated (MWF) and centralized cascade (PK-MWF) algorithm. It is observed, however, that the communication cost in the PK-GEVD-DANSE algorithm can also be reduced, where each node then broadcasts only 1 fused signal (instead of 2 signals) to the other nodes. The resulting algorithm, referred to as the pruned PK-GEVD-DANSE (pPK-GEVD-DANSE) algorithm, then effectively combines the lowest possible communication cost (as low as in the GEVD-DANSE algorithm) wit

关键词： Loudspeakers signal processing algorithms Microphones Acoustics distributed algorithms Echo cancellers Speech processing distributed signal processing wireless acoustic sensor and actuator networks acoustic echo cancellation noise reduction multichannel Wiener filter

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Adaptive diffusion networks: An overview

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signal processing 2024年 223卷

作者： Tiglea, Daniel Gilio Candido, Renato Silva, Magno T. M. Univ Sao Paulo Escola Politecn Sao Paulo Brazil

This work provides a comprehensive overview of adaptive diffusion networks, from the first papers published on the subject to state-of-the-art solutions and current challenges. These networks consist of a collection of agents that can measure and learn from streaming data locally, and cooperate to improve the overall performance. Since their inception, adaptive diffusion networks have consolidated themselves as interesting tools for distributed estimation and learning, and have spun several types of solutions for these problems. We begin by discussing the technological advances that led to their emergence, and present the many ramifications of the area. We also discuss some of the most critical limitations of these types of networks in practical situations, such as energy consumption, and show techniques that have been proposed to cope with them. Finally, simulations with real -world data are presented in order to illustrate in practice the opportunities and challenges that they pose.

关键词： Diffusion networks distributed estimation distributed signal processing Multitask learning Kernel adaptive filtering Graph signal processing

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distributed Denoising over Simplicial Complexes using Chebyshev Polynomial Approximation 30

Distributed Denoising over Simplicial Complexes using Chebys...

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30th European signal processing Conference (EUSIPCO)

作者： Kadambari, Sai Kiran Francis, Robin Chepuri, Sundeep Prabhakar Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9789082797091

In this work, we focus on denoising smooth signals supported on simplicial complexes in a distributed manner. We assume that the simplicial signals are dominantly smooth on either the lower or upper Laplacian matrices, which are used to compose the so-called Hodge Laplacian matrix. This corresponds to denoising non-harmonic signals on simplicial complexes. We pose the denoising problem as a convex optimization problem, where we assign different weights to the quadratic regularizers related to the upper and lower Hodge Laplacian matrices and express the optimal solution as a sum of simplicial complex operators related to the two Laplacian matrices. We then use the recursive relation of the Chebyshev polynomial to implement these operators in a distributed manner. We demonstrate the efficacy of the developed framework on synthetic and real-world datasets.

关键词： Chebyshev polynomials denoising distributed signal processing simplicial complex

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distributed CROSS-RELATION-BASED FREQUENCY-DOMAIN BLIND SYSTEM IDENTIFICATION USING ONLINE-ADMM 17

DISTRIBUTED CROSS-RELATION-BASED FREQUENCY-DOMAIN BLIND SYST...

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17th International Workshop on Acoustic signal Enhancement (IWAENC)

作者： Blochberger, M. Elvander, F. Ali, R. Moonen, M. van Waterschoot, T. Ostergaard, J. Jensen, J. Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS B-3001 Leuven Belgium Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

ISBN: (数字)9781665468671

ISBN: (纸本)9781665468671

In this paper, we propose a distributed cross-relation-based adaptive algorithm for blind identification of single-input multiple-output (SIMO) systems in the frequency domain, using the alternating direction method of multipliers (ADMM) in a wireless sensor network (WSN). The network consists of a fixed number of nodes each equipped with a processing unit and a sensor that represents an output channel of the SIMO system. The proposed algorithm exploits the separability of the cross-channel relations by splitting the multichannel identification problem into sub-problems containing a subset of channels, in a way that is determined by the network topology. Each node delivers estimates for the subset of channel frequency responses, which are then combined into a consensus estimate per channel using general-form consensus ADMM in an adaptive updating scheme. Using numerical simulations, we show that it is possible to achieve convergence speeds and steady-state misalignment values comparable to fully centralized low-cost frequency-domain algorithms.

关键词： blind system identification multichannel signal processing distributed signal processing admm online-admm

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Quasi-distributed Antenna Selection for Spectral Efficiency Maximization in Subarray Switching XL-MIMO Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第7期70卷 6713-6725页

作者： Inacio de Souza, Joao Henrique Amiri, Abolfazl Abrao, Taufik de Carvalho, Elisabeth Popovski, Petar Univ Estadual Londrina Elect Engn Dept BR-86057970 Londrina Parana Brazil Aalborg Univ Tech Fac IT & Design Dept Elect Syst DK-9220 Aalborg Denmark

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings.

关键词： Antennas Transceivers Radio frequency Massive MIMO Computer architecture signal processing algorithms Resource management Extra-large scale massive MIMO (XL-MIMO) antenna selection (AS) resource allocation (RA) genetic algorithm (GA) distributed signal processing

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