# IEEE Transactions on Signal and Information Processing over Networks

### Early Access Articles

Early Access articles are made available in advance of the final electronic or print versions. Early Access articles are peer reviewed but may not be fully edited. They are fully citable from the moment they appear in IEEE Xplore.

## Filter Results

Displaying Results 1 - 25 of 25
• ### Consensus-based Distributed Discrete Optimal Transport for Decentralized Resource Matching

Publication Year: 2019, Page(s): 1
| | PDF (1415 KB)

Optimal transport has been used extensively in resource matching to promote the efficiency of resources usages by matching sources to targets. However, it requires a significant amount of computations and storage spaces for large-scale problems. In this paper, we take a consensus-based approach to decentralize discrete optimal transport problems and develop fully distributed algorithms with altern... View full abstract»

• ### On the Distributed Method of Multipliers for Separable Convex Optimization Problems

Publication Year: 2019, Page(s): 1
| | PDF (777 KB)

In this paper we present a novel method for convex optimization in distributed networks called the distributed method of multipliers (DMM). The proposed method is based on a combination of a particular dual lifting and classic monotone operator splitting approaches to produce an algorithm with guaranteed asymptotic convergence in undirected networks. The proposed method allows any separable convex... View full abstract»

• ### Asynchronous Online Learning in Multi-Agent Systems with Proximity Constraints

Publication Year: 2019, Page(s): 1
| | PDF (1631 KB)

We consider the problem of distributed learning from sequential data via online convex optimization. A multiagent system is considered where each agent has a private objective but is willing to cooperate in order to minimize the network cost, which is the sum of local cost functions. Different from the classical distributed settings, where the agents coordinate through the use of consensus constra... View full abstract»

• ### Decentralized Topology Reconfiguration in Multiphase Distribution Networks

Publication Year: 2019, Page(s): 1
| | PDF (5003 KB)

The cyber-physical nature of the modern power grid allows active power entities to exchange information signals with one another to make intelligent local actuation decisions. Exacting effective coordination amongst these cyber-enabled entities by way of strategic signal exchanges is essential for accommodating highly fluctuating power components (e.g. renewables, electric vehicles, etc.) that are... View full abstract»

• ### Fusion Rules for Distributed Detection in Clustered Wireless Sensor Networks with Imperfect Channels

Publication Year: 2019, Page(s): 1
| | PDF (492 KB)

In this paper we investigate fusion rules for distributed detection in large random clustered-wireless sensor networks (WSNs) with a three-tier hierarchy; the sensor nodes (SNs), the cluster heads (CHs) and the fusion center (FC). The CHs collect the SNs' local decisions and relay them to the FC that then fuses them to reach the ultimate decision. The SN-CH and the CH-FC channels suffer from addit... View full abstract»

• ### Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD

Publication Year: 2019, Page(s): 1
| | PDF (1026 KB)

Automatic modulation classification (AMC) is becoming increasingly important in spectrum monitoring and cognitive radio. However, most existing modulation classification algorithms neglect the complementarities between different features and the importance of features fusion. To remedy these flaws, this paper presents a scheme of features fusion for AMC using convolutional neural network (CNN).The... View full abstract»

• ### Position-Constrained Stochastic Inference for Cooperative Indoor Localization

Publication Year: 2019, Page(s): 1
| | PDF (10179 KB)

We address the problem of distributed cooperative localization in wireless networks, i.e. nodes without prior position knowledge (agents) wish to determine their own positions. In non-cooperative approaches, positioning is only based on information from reference nodes with known positions (anchors). However, in cooperative positioning, information from other agents is considered as well. Cooperat... View full abstract»

• ### On the Q-linear convergence of Distributed Generalized ADMM under non-strongly convex function components

Publication Year: 2019, Page(s): 1
| | PDF (470 KB)

Solving optimization problems in multi-agent networks where each agent only has partial knowledge of the problem has become an increasingly important problem. In this paper we consider the problem of minimizing the sum of $n$ convex functions. We assume that each function is only known by one agent. We show that Generalized Distributed ADMM converges Q-linearly to the... View full abstract»

• ### Graph-Based Compression for Distributed Particle Filters

Publication Year: 2019, Page(s): 1
| | PDF (503 KB)

A key challenge in designing distributed particle filters is to minimize the communication overhead without compromising tracking performance. In this paper we present two distributed particle filters that achieve robust performance with low communication overhead. The two filters construct a graph of the particles and exploit the graph Laplacian matrix in different manners to encode the particle ... View full abstract»

• ### Distributed Outlier-Robust Bayesian Filtering for State Estimation

Publication Year: 2018, Page(s): 1
| | PDF (532 KB)

We study the problem of distributed filtering for state space models over networks, which aims to collaboratively estimate the states by a network of nodes. Most of existing works on this problem assume that both process and measurement noises are Gaussian and their covariances are known in advance. In some cases, this assumption breaks down and no longer holds. In this paper, we consider the case... View full abstract»

• ### Expander Recovery Performance of Bipartite Graphs with Girth Greater than 4

Publication Year: 2018, Page(s): 1
| | PDF (510 KB)

Expander recovery is an iterative algorithm de- signed to recover sparse signals measured with 0-1 binary matrices with linear complexity. In the paper, we study the expander recovery performance of the bipartite graph with girth greater than 4, which can be associated with a binary matrix with column correlations equal to either 0 or 1. For such graph, expander recovery is proved to achieve the s... View full abstract»

• ### Distributed Bernoulli Filtering Using Likelihood Consensus

Publication Year: 2018, Page(s): 1
| | PDF (1952 KB)

We consider the detection and tracking of a target in a decentralized sensor network. The existence of the target is uncertain, and the sensor measurements are affected by clutter and missed detections. We propose a particle-based distributed Bernoulli filter (BF) that provides to each sensor an approximation of the Bayes-optimum estimate of the target state. The proposed method uses all the measu... View full abstract»

• ### Distributionally Robust Radio Frequency Localization

Publication Year: 2018, Page(s): 1
| | PDF (516 KB) |  Media

We consider the problem of estimating the location of an RF-device using observations such as received signal strengths, generated according to an uncertain distribution from a set of transmitters with known locations. We present a distributionally robust formulation of the localization problem that explicitly takes into account the uncertainty in the distribution that generates the observations. ... View full abstract»

• ### Online Sparse Multi-Output Gaussian Process Regression and Learning

Publication Year: 2018, Page(s): 1
| | PDF (1009 KB)

This paper proposes an approach for online training of a sparse multi-output Gaussian process (GP) model using sequentially obtained data. The considered model combines linearly multiple latent sparse GPs to produce correlated output variables. Each latent GP has its own set of inducing points to achieve sparsity. We show that given the model hyperparameters, the posterior over the inducing points... View full abstract»

• ### A Bayesian algorithm for distributed cooperative localization using distance and direction estimates

Publication Year: 2018, Page(s): 1
| | PDF (3427 KB)

A reliable, accurate, and affordable positioning service is highly required in wireless networks. High-resolution estimates of distance and direction data are available in most current and emerging wireless systems. Combining these two sensing modalities can improve the estimation performance and identifiability of the cooperative localization problem, and reduce its sensitivity to the anchor node... View full abstract»

• ### Real-time Cooperative Analytics for Ambient Noise Tomography in Sensor Networks

Publication Year: 2018, Page(s): 1
Cited by:  Papers (2)
| | PDF (5309 KB)

The transformative integration of sensor networks and geophysical imaging techniques enables the creation of a system which monitors and analyzes seismic data in real time as well as image various subsurface structures, properties, and dynamics. Ambient Noise Seismic Imaging (ANSI) is a technique widely used in geophysical exploration for investigating structures under the earth's surface via the ... View full abstract»

• ### Derivation and Analysis of the Primal-Dual Method of Multipliers Based on Monotone Operator Theory

Publication Year: 2018, Page(s): 1
| | PDF (481 KB)

In this paper we present a novel derivation of an existing algorithm for distributed optimization termed the primal-dual method of multipliers. In contrast to its initial derivation, monotone operator theory is used to connect PDMM with other first-order methods such as Douglas-Rachford splitting and the alternating direction method of multipliers thus providing insight into its operation. In part... View full abstract»

• ### Widely-Linear Complex-Valued Diffusion Subband Adaptive Filter Algorithm

Publication Year: 2018, Page(s): 1
| | PDF (1093 KB)

The adaptive algorithms applied to distributed networks are usually Real-valued Diffusion subband adaptive filter (RDSAF) algorithms. However, it can not be used for processing the complex-valued signals. In this paper, a novel augmented complex-valued diffusion normalized subband adaptive filter (D-ACNSAF) algorithm is proposed for distributed estimation over networks. In order to deal with the n... View full abstract»

• ### Cross-layer MAC Protocol for Unbiased Average Consensus under Random Interference

Publication Year: 2018, Page(s): 1
| | PDF (750 KB)

Wireless Sensor Networks have been revealed as a powerful technology to solve many different problems through sensor nodes cooperation. One important cooperative process is the so-called average gossip algorithm, which constitutes a building block to perform many inference tasks in an efficient and distributed manner. From the theoretical designs proposed in most previous work, this algorithm requ... View full abstract»

• ### Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification

Publication Year: 2018, Page(s): 1
Cited by:  Papers (1)
| | PDF (4983 KB)

This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter (a function of graph Laplacian matrices). In order to solve the proposed proble... View full abstract»

• ### Sampling of graph signals via randomized local aggregations

Publication Year: 2018, Page(s): 1
| | PDF (477 KB)

Sampling of signals defined over the nodes of a graph is one of the crucial problems in graph signal processing. While in classical signal processing sampling is a well defined operation, when we consider a graph signal many new challenges arise and defining an efficient sampling strategy is not straightforward. Recently, several works have addressed this problem. The most common techniques select... View full abstract»

• ### Distributed estimation from relative measurements of heterogeneous and uncertain quality

Publication Year: 2018, Page(s): 1
| | PDF (790 KB)

This paper studies the problem of estimation from relative measurements in a graph, in which a vector indexed over the nodes has to be reconstructed from pairwise measurements of differences between its components associated to nodes connected by an edge. In order to model heterogeneity and uncertainty of the measurements, we assume them to be affected by additive noise distributed according to a ... View full abstract»

• ### Improved Bounds for Max Consensus in Wireless Networks

Publication Year: 2018, Page(s): 1
| | PDF (1436 KB)

In consensus problems, the goal is for the nodes of a network to converge to a certain quantity or a function of their values using local communications. In the maximum value consensus problem, the objective of these communications is for all the nodes to converge to the maximum of their initial values. There are two existing algorithms for the maximum value consensus problem in asynchronous netwo... View full abstract»

• ### Distributed Estimation Over an Adaptive Diffusion Network Based on the Family of Affine Projection Algorithms

Publication Year: 2018, Page(s): 1
| | PDF (1107 KB)

This paper utilizes the family of affine projection algorithms (APAs) for distributed estimation in the adaptive diffusion networks. The diffusion AP algorithm (DAPA), the diffusion selective partial update (SPU) APA (DSPU-APA), the diffusion selective regressor (SR) APA (DSR-APA), and the diffusion dynamic selection (DS) APA (DDS-APA) are introduced in a unified way. In DSPU-APA, the weight coeff... View full abstract»

• ### Online Contextual Influence Maximization with Costly Observations

Publication Year: 2018, Page(s): 1
| | PDF (2191 KB)

In the Online Contextual Influence Maximization Problem with Costly Observations, the learner faces a series of epochs in each of which a different influence spread process takes place over a network. At the beginning of each epoch, the learner exogenously influences (activates) a set of seed nodes in the network. Then, the influence spread process takes place over the network, through which other... View full abstract»

## Aims & Scope

The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Antonio Ortega
Signal and Image Processing Institute
Department of Electrical and Computer Engineering
University of Southern California
3740 McClintock Ave., EEB 436
Los Angeles, CA 90089-2564 USA
Tel: +1 213-740-2320
Fax: +1 213-740-4651
antonio.ortega@sipi.usc.edu