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检索条件"任意字段=25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015"

共 79 条记录，以下是21-30 订阅

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Fully-distributed optimization with Network Exact Consensus-GIANT

Fully-distributed optimization with Network Exact Consensus-...

引用

ieee workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Alessio Maritan Ganesh Sharma Subhrakanti Dey Luca Schenato Department of Information Engineering University of Padova Italy Hamilton Institute Maynooth University Ireland Department of Electrical Engineering Uppsala University Sweden

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

We consider a fully-distributed optimization problem involving multiple collaborative agents, where the global objective is to minimize a sum of local cost functions. Agents are part of a communication network and can only exchange information with their neighbors. We introduce a novel optimization algorithm called NEC-GIANT, which improves over both GIANT, a popular federated learning algorithm, and Network-GIANT, our previously proposed fully-distributed counterpart of GIANT. NEC-GIANT extends GIANT to the fully-distributed scenario, removing the need for a central server to orchestrate the agents. Unlike the existing Network-GIANT, which suffers from the inefficiency of standard asymptotic consensus, the novel NEC-GIANT is based on finite-time distributed consensus and retains all the convergence properties of the original GIANT. Numerical simulations prove the efficiency and superiority of the proposed algorithm in terms of both iterations and machine run-time.

关键词： Wireless communication Degradation Federated learning signal processing algorithms signal processing Numerical simulation Cost function Servers Standards Convergence

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Multi-Site Wireless Channel Charting through Latent Space Alignment

Multi-Site Wireless Channel Charting Through Latent Space Al...

引用

ieee workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Yamil Vindas Maxime Guillaud Inria MARACAS Team & CITI Laboratory Lyon France

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Wireless channel charting is a self-supervised machine learning approach designed to capture and leverage the statistical properties of wireless propagation. By applying dimensionality reduction to channel state information, channel charting establishes a low-dimensional representation of the channel state, which is akin to a pseudo-location of the users within the propagation environment. Conversely to multipoint channel charting, which considers efficient ways of fusing the features acquired by multiple access points, we introduce in this work the multi-site channel charting problem whereby multiple base stations generate their own channel charts, while their coverage areas partially overlap. We introduce several methods to align the latent spaces at each base station into a common latent space, effectively performing the fusion of multiple channel charts. We benchmark the proposed approaches and compare their performance in terms of the classical dimensionality reduction metrics using measured data, as well as their degree of distributedness.

关键词： Wireless communication Dimensionality reduction Training Measurement Base stations Focusing Distributed databases signal processing Data mining Space stations

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Dictionary learning Based Near-Field Channel Estimation for Wideband XL-MIMO Systems

Dictionary Learning Based Near-Field Channel Estimation for ...

引用

ieee workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Peicong Zheng Xuantao Lyu Ye Wang Yi Gong Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Extremely large-scale MIMO (XL-MIMO) is pivotal for enabling future 6G networks. However, accurate channel estimation for XL-MIMO faces a new challenge known as the near-field beam split effect. To capture the characteristics of the channel, this paper proposes a dictionary learning based channel estimation algorithm for wideband XL-MIMO systems. the channel estimation problem is formulated as a bilevel optimization task, where the upper-level aims to learn the optimal dictionary while the lower-level aims to find sparse channel representations based on the learned dictionary. To efficiently solve this bilevel optimization problem, we unfold the iterative shrinkage-thresholding algorithm into a deep neural network architecture. Learnable network blocks are employed to represent the dictionary, thus enabling adaptation to the characteristics of near-field wideband channel in XL-MIMO. the unfolded network is trained in an unsupervised manner by minimizing the reconstruction error via backpropagation and stochastic gradient descent. this eliminates the need for explicit gradient calculations associated with the bilevel optimization. Simulation results demonstrate that the proposed algorithm achieves good channel estimation performance, while offering lower computational complexity than the conventional compressed sensing based estimators.

关键词： Wireless communication Dictionaries Simulation Channel estimation signal processing algorithms machine learning signal processing Iterative algorithms Optimization Wideband

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Optimized Random Antenna Switching for Channel Measurement Based Secret Key Generation : Invited Paper

Optimized Random Antenna Switching for Channel Measurement B...

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ieee workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Julius Nestler Martin Mittelbach Axel Schmidt Rafael F. Schaefer Chair of Information Theory and Machine Learning Dresden University of Technology Germany

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

In this work we consider an experimental hardware platform for secret key generation operating in the ISM frequency band, where one of the communication partners has multiple antennas available. For this scenario we derive optimized antenna switching schemes to improve the random characteristics of the channel measurements used as raw data for key generation. We consider different optimization problems to minimize the total correlation of the channel measurements over frequency and demonstrate for real measurement data that with a simplified, low complexity optimization scheme a high or even maximum degree of decorrelation is achievable. the results show that independent antenna switching for each frequency is de facto optimal and that uniform antenna switching probabilities are not optimal. In particular, the results show the effectiveness of the optimal randomized scheme under unfavorable conditions which appear, e.g., in static and line-of-sight communication situations.

关键词： Antenna measurements Wireless communication Atmospheric measurements Line-of-sight propagation Switches Particle measurements Frequency measurement Optimization Antennas MIMO communication

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GLOBALLY OPTIMAL ROBUST MATRIX COMPLETION BASED ON M-ESTIMATION 30

GLOBALLY OPTIMAL ROBUST MATRIX COMPLETION BASED ON M-ESTIMAT...

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30th ieee international workshop on machine learning for signal processing (mlsp)

作者： Ruppel, Felicia Muma, Michael Zoubir, Abdelhak M. Tech Univ Darmstadt Signal Proc Grp Merckstr 25 D-64283 Darmstadt Germany

ISBN: (纸本)9781728166629

Robust matrix completion allows for estimating a low-rank matrix based on a subset of its entries, even in presence of impulsive noise and outliers. We explore recent progress in the theoretical analysis of non-convex low-rank factorization problems to develop a robust approach that is based on a fast factorization method. We propose an algorithm that uses joint regression and scale estimation to compute the estimates. We prove that our algorithm converges to a global minimum with random initialization. An example function for which the guarantees hold is the pseudo-Huber function. In simulations, the proposed approach is compared to state-of the art robust and non-robust methods. In addition, its applicability to image inpainting and occlusion removal is demonstrated.

关键词： Matrix completion optimality low-rank factorization robustness image inpainting occlusion removal

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COLLISION-FREE UAV NAVIGATION WIth A MONOCULAR CAMERA USING DEEP REINFORCEMENT learning 30

COLLISION-FREE UAV NAVIGATION WITH A MONOCULAR CAMERA USING ...

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30th ieee international workshop on machine learning for signal processing (mlsp)

作者： Chen, Yun Gonzalez-Prelcic, Nuria Heath, Robert W., Jr. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

ISBN: (纸本)9781728166629

Small unmanned aerial vehicles (UAV) with reduced sensing and communication capabilities can support potential use cases in different indoor environments such as automated factories or commercial buildings. In this context, we consider the problem of collision-free autonomous UAV navigation supported by a simple sensor. We propose a navigation system based on object detection and deep reinforcement learning (DRL) that only exploits sensing data obtained by a monocular camera mounted on the UAV. Object detection is incorporated into DRL training to reduce flight time and to maximize the probability of avoiding both current and future crashes. Moreover, object detection also helps to remove the impact of wrong predictions from the deep network. When compared to schemes using traditional RL methods, the proposed framework not only leads to collision-free trips, but it also reduces flying times towards given destinations by 25%, and cuts down 50% of unnecessary turns.

关键词： Deep reinforcement learning UAV navigation Collision avoidance

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Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM

Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFD...

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ieee workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Fabrizio Carpi Soheil Rostami Joonyoung Cho Siddharth Garg Elza Erkip Charlie Jianzhong Zhang Department of Electrical and Computer Engineering New York University Brooklyn NY Standards and Mobility Innovation Samsung Research America USA

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

High peak-to-average power ratio (PAPR) is one of the main factors limiting cell coverage for cellular systems, especially in the uplink direction. Discrete Fourier transform spread orthogonal frequency-domain multiplexing (DFT-s-OFDM) with spectrally-extended frequency-domain spectrum shaping (FDSS) is one of the efficient techniques deployed to lower the PAPR of the uplink waveforms. In this work, we propose a machine learning-based framework to determine the FDSS filter, optimizing a tradeoff between the symbol error rate (SER), the PAPR, and the spectral flatness requirements. Our end-to-end optimization framework considers multiple important design constraints, including the Nyquist zero-ISI (inter-symbol interference) condition. the numerical results show that learned FDSS filters lower the PAPR compared to conventional baselines, with minimal SER degradation. Tuning the parameters of the optimization also helps us understand the fundamental limitations and characteristics of the FDSS filters for PAPR reduction.

关键词： Degradation Wireless communication Frequency-domain analysis Symbols Peak to average power ratio Uplink Gain Optimization Tuning signal to noise ratio

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A HYBRID INTERIOR POINT - DEEP learning APPROACH FOR POISSON IMAGE DEBLURRING 30

A HYBRID INTERIOR POINT - DEEP LEARNING APPROACH FOR POISSON...

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30th ieee international workshop on machine learning for signal processing (mlsp)

作者： Galinier, M. Prato, M. Chouzenoux, E. Pesquet, J-C Univ Modena & Reggio Emilia Dipartimento Sci Fis Informat & Matemat Via Campi 213-B I-41125 Modena Italy Univ Paris Saclay Cent Supelec Inria Ctr Vis Numer F-91190 Gif Sur Yvettes France Inst Univ France Paris France

ISBN: (数字)9781728166629

ISBN: (纸本)9781728166629

In this paper we address the problem of deconvolution of an image corrupted with Poisson noise by reformulating the restoration process as a constrained minimization of a suitable regularized data fidelity function. the minimization step is performed by means of an interior-point approach, in which the constraints are incorporated within the objective function through a barrier penalty and a forward-backward algorithm is exploited to build a minimizing sequence. the key point of our proposed scheme is that the choice of the regularization, barrier and step-size parameters defining the interior point approach is automatically performed by a deep learning strategy. Numerical tests on Poisson corrupted benchmark datasets show that our method can obtain very good performance when compared to a state-of-the-art variational deblurring strategy.

关键词： Interior point method proximal algorithms deep unfolding neural network Poisson image restoration

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Graph Neural Networks versus Gated Recurrent Units Only for approximate bayesian MU-MIMO detectors

Graph Neural Networks versus Gated Recurrent Units Only for ...

引用

ieee workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Arthur Michon Charly Poulliat Antonio Maria Cipriano INP-ENSEEIHT Toulouse France THALES SIX GTS Gennevilliers France

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Multi-User Multiple-Input Multiple-Output (MU-MIMO) communications is one of the technological cornerstones of cellular systems, starting from 4G, up to 5G and beyond. Representation of this kind of transmission via a factor graph has been proven to be an effective way to define message-passing algorithms for detection. these algorithms may be classified into two main categories. the first class relies on vector message passing algorithms such as Vector Expectation Propagation (VEP), Vector Approximate Message Passing (VAMP) for example. this approach offers great performance at the cost of high computational complexity. the second class is composed of scalar message-passing algorithms on the graph that aim to reduce the complexity at the cost of performance degradation. Both of these approaches use some approximations during inference to estimate the posterior symbol distribution. machine learning has received growing attention to cope with these approximations. In this work, we investigate the hybrid use of Graph Neural Networks (GNN), which can exploit the bipartite graph structure of the transmission, and Gated Reccurent Units (GRU), which can exploit the long-term dependency throughout the iterative process. We show that, contrarily to AMP, for VEP the GNN aggregation step has only a limited contribution to the overall performance and can be skipped to substantially reduce the algorithm complexity.

关键词： Wireless communication Costs Message passing signal processing algorithms Symbols Logic gates Approximation algorithms Graph neural networks Vectors Classification algorithms

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the Application of machine-learning Approach for the Classification of People According to their Participation in Meditation based on Neurophysiological Data

The Application of Machine-Learning Approach for the Classif...

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Annual Siberian Russian workshop on Electron Devices and Materials (EDM)

作者： Nadezhda A. Istomina Xi Fu Sergey S. Tamozhnikov Alexander E. Saprygin Alexander N. Savostyanov Institute for the Humanities Novosibirsk State University Novosibirsk Russia Institute for Intelligent Robotics Novosibirsk State University Novosibirsk Russia Laboratory of differential psychophysiology Institute of Neurosciences and Medicine Novosibirsk Russia Laboratory of psychological genetics Institute of Cytology and Genetics SB RAS Novosibirsk Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

Motor control is a human ability to manage own goal-directed motions. this ability can be estimated by means of so-called stop-signal paradigm (SSP). SSP is an experimental approach consisting of two behavioral tasks - activation or inhibition of motions. SSP is used for diagnosing a wide range of neuropsychiatric pathologies, including depressive and anxiety disorders. Combined with the analysis of evoked brain potentials (ERP), processing of the behavioral SSP results allows to reveal the neurophysiological causes of normal motor control and its deviations. Meditation is a psychological practice aimed at reducing stress and anxiety levels. the result of longterm meditation is the increase of individuals’ resilience to affective disorders. In this study, we applied a machine-learning approach to develop a methodology of classification of people according to their participation in meditation. the amplitudes of two ERP peaks (premotor and postmotor) were used as input data. We developed four convolutional network models that were trained and tested on approximately 100 healthy participants (half of whom participated in meditation). then, all models were checked on an additional sample of 25 participants. We selected parameters for convolutional networks that allowed us to achieve $\mathbf{8 2 \%}$ classification accuracy and model stability against overfitting. the proposed approach allows to classify individuals based on their stress resilience through ERP data processing.

关键词： Motor drives Accuracy machine learning Brain modeling Motors Convolutional neural networks Biological neural networks

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