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Precoder Feedback Schemes for Robust Interference Alignment With Bounded CSI Uncertainty IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200520
Navneet Garg; Aditya K. Jagannatham; Govind Sharma; Tharmalingam RatnarajahThis article presents limited feedbackbased precoder quantization schemes for Interference Alignment (IA) with bounded channel state information (CSI) uncertainty. Initially, this work generalizes the minmax mean squared error (MSE) framework, followed by the development of robust precoder and decoder designs based on worst case MSE minimization. The proposed precoder and decoder designs capture

Collaborative MultiSensing in Energy Harvesting Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200518
Vini Gupta; Swades DeThis article presents an adaptive multisensing (MS) framework for a network of densely deployed solar energy harvesting wireless nodes. Each node is mounted with heterogeneous sensors to sense multiple crosscorrelated slowlyvarying parameters/signals. Inherent spatiotemporal correlations of the observed parameters are exploited to adaptively activate a subset of sensors of a few nodes and turnOFF

Mask Combination of MultiLayer Graphs for Global Structure Inference IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200518
Eda Bayram; Dorina Thanou; Elif Vural; Pascal FrossardStructure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although realworld data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well

Energy Efficient Spectrum Allocation and Mode Selection for D2D Communications in Heterogeneous Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200511
Apostolos Galanopoulos; Fotis Foukalas; Tamer KhattabIn this paper, we consider a heterogeneous network consisting of both macro Base Station (MBS) and pico Base Stations (PBSs) in order to provide a spectrum allocation and mode selection in devicetodevice (D2D) communications. A number of Component Carriers (CC) are considered available for allocation to the MBS and PBSs that are being utilized through carrier aggregation (CA) while mode selection

Bayesian Inference of Network Structure From Information Cascades IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200427
Caitlin Gray; Lewis Mitchell; Matthew RoughanContagion processes are strongly linked to the network structures on which they propagate, and learning these structures is essential for understanding and intervention on complex network processes such as epidemics and (mis)information propagation. However, using contagion data to infer network structure is a challenging inverse problem. In particular, it is imperative to have appropriate measures

IEEE Transactions on Signal and Information Processing over Networks publication information IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Resilient Distributed Diffusion in Networks With Adversaries IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191206
Jiani Li; Waseem Abbas; Xenofon KoutsoukosIn this article, we study resilient distributed diffusion for multitask estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusionbased information processing rules. In particular, by exploiting

CommunicationCensored Linearized ADMM for Decentralized Consensus Optimization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191206
Weiyu Li; Yaohua Liu; Zhi Tian; Qing LingIn this paper, we propose a communication and computationefficient algorithm to solve a convex consensus optimization problem defined over a decentralized network. A remarkable existing algorithm to solve this problem is the alternating direction method of multipliers (ADMM), in which at every iteration every node updates its local variable through combining neighboring variables and solving an optimization

Optimized Transmission for Parameter Estimation in Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191004
Shahin Khobahi; Mojtaba Soltanalian; Feng Jiang; A. Lee SwindlehurstA central problem in analog wireless sensor networks is to design the gain or phaseshifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an overparametrization of the original design problem

VectorValued Graph Trend Filtering With NonConvex Penalties IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191206
Rohan Varma; Harlin Lee; Jelena Kovačević; Yuejie ChiThis article studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vectorvalued. We extend the graph trend filtering framework to denoising vectorvalued graph signals with a family of nonconvex regularizers, which exhibit superior recovery performance over existing convex regularizers. Using an oracle

Online Distributed Learning Over Graphs With Multitask GraphFilter Models IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Fei Hua; Roula Nassif; Cédric Richard; Haiyan Wang; Ali H. SayedIn this article, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs, which can be addressed with diffusion LMS strategies. Most popular graphshift operators such as those based on the graph Laplacian matrix, or the adjacency matrix, are not energy preserving. This may result in an illconditioned

Minimal Sufficient Conditions for Structural Observability/Controllability of Composite Networks via Kronecker Product IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20191216
Mohammadreza Doostmohammadian; Usman A. KhanIn this article, we consider composite networks formed from the Kronecker product of smaller networks. We find the observability and controllability properties of the product network from those of its constituent smaller networks. The overall network is modeled as a LinearStructureInvariant (LSI) dynamical system where the underlying matrices have a fixed zero/nonzero structure but the nonzero

Fastest Mixing Reversible Markov Chain: Clique Lifted Graphs and Subgraphs IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Saber JafarizadehMarkov chains are one of the wellknown tools for modeling and analyzing stochastic systems. At the same time, they are used for constructing random walks that can achieve a given stationary distribution. This paper is concerned with determining the transition probabilities that optimize the mixing time of the reversible Markov chains towards a given equilibrium distribution. This problem is referred

Efficient Graph Learning From Noisy and Incomplete Data IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Peter Berger; Gabor Hannak; Gerald MatzWe consider the problem of learning a graph from a given set of smooth graph signals. Our graph learning approach is formulated as a constrained quadratic program in the edge weights. We provide an implicit characterization of the optimal solution and propose a tailored ADMM algorithm to solve this problem efficiently. Several nearest neighbor and smoothness based graph learning methods are shown to

NodeCentric Graph Learning From Data for Brain State Identification IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200106
Nafiseh Ghoroghchian; David M. Groppe; Roman Genov; Taufik A. Valiante; Stark C. DraperDatadriven graph learning models a network by determining the strength of connections between its nodes. The data refers to a graph signal which associates a value with each graph node. Existing graph learning methods either use simplified models for the graph signal, or they are prohibitively expensive in terms of computational and memory requirements. This is particularly true when the number of

Recovering the Structural Observability of Composite Networks via Cartesian Product IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200116
Mohammadreza DoostmohammadianObservability is a fundamental concept in system inference and estimation. This article is focused on structural observability analysis of Cartesian product networks. Cartesian product networks emerge in variety of applications including in parallel and distributed systems. We provide a structural approach to extend the structural observability of the constituent networks (referred as the factor networks)

ComputationEfficient Distributed Algorithm for Convex Optimization Over TimeVarying Networks With Limited Bandwidth Communication IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200116
Huaqing Li; Chicheng Huang; Zheng Wang; Guo Chen; Hafiz Gulfam Ahmad UmarA novel computationefficient quantized distributed optimization algorithm is presented in this article for solving a class of convex optimization problems over timevarying undirected networks with limited communication capacity. These convex optimization problems are usually relevant to the minimization of a sum of local convex objective functions using only local communication and local computation

OrthoNet: Multilayer Network Data Clustering IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200129
Mireille El Gheche; Giovanni Chierchia; Pascal FrossardNetwork data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some information about the network edges. In some cases, this network information can even be given with multiple views or layers, each one representing

Learning Graphs From Linear Measurements: Fundamental TradeOffs and Applications IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200224
Tongxin Li; Lucien Werner; Steven H. LowWe consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We present a sparsity characterization for distributions of random graphs (that are allowed to contain highdegree nodes), based on which we study fundamental tradeoffs between the number of measurements, the complexity of the graph class, and the probability

Distributed Learning Algorithms for Optimal Data Routing in IoT Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200221
Michele Rossi; Marco Centenaro; Aly Ba; Salma Eleuch; Tomaso Erseghe; Michele ZorziWe consider the problem of joint lossy data compression and data routing in distributed Internet of Things (IoT). Heterogeneous sources compress their data using a sourcespecific lossy compression scheme, where heterogeneity is meant in terms of signal type and/or transmission rates. The compressed data is thus disseminated in a multihop fashion until it reaches a data collector (the IoT gateway)

Distributed Discrete Hashing by Equivalent Continuous Formulation IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200220
Shengnan Wang; Chunguang Li; HuiLiang ShenHashing based approximate nearest neighbor search has attracted considerable attention in various fields. Most of the existing hashing methods are centralized, which cannot be used for many largescale applications with the data stored or collected in a distributed manner. In this article, we consider the distributed hashing problem. The main difficulty of hashing is brought by its inherent binary

StateSpace Network Topology Identification From Partial Observations IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200220
Mario Coutino; Elvin Isufi; Takanori Maehara; Geert LeusIn this article, we explore the statespace formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identification problem. This approach provides a unified view of network control and signal processing on graphs

Spectral Graph Based VertexFrequency Wiener Filtering for Image and Graph Signal Denoising IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200227
Ali Can Yağan; Mehmet Tankut ÖzgenIn this article, we propose and develop a spectral graph based vertex varying Wiener filtering framework in the joint vertexfrequency domain for denoising of graph signals defined on weighted, undirected and connected graphs. To this end, we first extend the Zadeh timefrequency filter concept to graph signals and obtain vertexfrequency transfer function of the proposed Wiener filter by transforming

Distributed Sensor Networks Based Shallow Subsurface Imaging and Infrastructure Monitoring IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200220
Fangyu Li; Maria Valero; Yifang Cheng; Tong Bai; WenZhan SongDistributed sensor networks can be used as passive seismic sensors to image and monitor subsurface and underground activities. Passive seismic surfacewave imaging adopts background ambient sounds from a farfield energy source. Because high frequency components decay a lot between the neighboring stations, conventional sparse sensor networks cannot image smallscale and shallow objects. In this article

Incremental Coding for Extractable Compression in the Context of Massive Random Access IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200318
Thomas Maugey; Aline Roumy; Elsa Dupraz; Michel KiefferIn this paper, we study the problem of source coding with Massive Random Access (MRA). A set of correlated sources is encoded once for all and stored on a server while a large number of clients access various subsets of these sources. Due to the number of concurrent requests, the server is only able to extract a bitstream from the stored data: no reencoding can be performed before the transmission

Graph Laplacian Mixture Model IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200330
Hermina Petric Maretic; Pascal FrossardGraph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph. This is, however, not always the case, as data is often available in mixed form, yielding the need for methods

ClusteringAware Graph Construction: A Joint Learning Perspective IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200420
Yuheng Jia; Hui Liu; Junhui Hou; Sam KwongGraphbased clustering methods have demonstrated the effectiveness in various applications. Generally, existing graphbased clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not fit the requirements for the subsequent decomposition, leading to compromised clustering

Edge Caching in MultiUAVEnabled Radio Access Networks: 3D Modeling and Spectral Efficiency Optimization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200413
Fasheng Zhou; Ning Wang; Gaoyong Luo; Lisheng Fan; Wei ChenUnmannedaerialvehicle (UAV) enabled radio access is an effective technology to improve the wireless coverage, in particular for remote and disasterstruck areas. It will become a key enabler in the forthcoming 5G heterogeneous cellular networks to provide improved and resilient coverage. In this article, we study edge caching for multiple UAVenabled radio access networks (UAVRANs) and investigate

Multiplex Network Inference With Sparse Tensor Decomposition for Functional Connectivity IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200403
Gaëtan Frusque; Julien Jung; Pierre Borgnat; Paulo GonçalvesFunctional connectivity (FC) is a graphlike data structure commonly used by neuroscientists to study the dynamic behaviour of brain activity. However, these analyses rapidly become complex and timeconsuming, since the number of connectivity components to be studied is quadratic with the number of electrodes. In this work, we address the problem of clustering FC into relevant ensembles of simultaneously

Network Inference From Consensus Dynamics With Unknown Parameters IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200402
Yu Zhu; Michael T. Schaub; Ali Jadbabaie; Santiago SegarraWe explore the problem of inferring the graph Laplacian of a weighted, undirected network from snapshots of a single or multiple discretetime consensus dynamics, subject to parameter uncertainty, taking place on the network. Specifically, we consider three problems in which we assume different levels of knowledge about the diffusion rates, observation times, and the input signal power of the dynamics

Topology Identification of Directed Graphs via Joint Diagonalization of Correlation Matrices IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200402
Yanning Shen; Xiao Fu; Georgios B. Giannakis; Nicholas D. SidiropoulosDiscovering connectivity patterns of directed networks is a crucial step to understand complex systems such as brain, social, and financial networks. Several existing network topology inference approaches rely on structural equation models (SEMs). These presume that exogenous inputs are available, which may be unrealistic in certain applications. Recently, an alternative line of work reformulated

Multimodal Dynamic Brain Connectivity Analysis Based on Graph Signal Processing for Former Athletes With History of Multiple Concussions IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200323
Saurabh Sihag; Sebastien Naze; Foad Taghdiri; Charles Tator; Richard Wennberg; David Mikulis; Robin Green; Brenda Colella; Maria Carmela Tartaglia; James R. KozloskiThe study of structurefunction relationships in the brain has been an active area of research in neuroscience. The availability of brain imaging data that captures the structural connectivity and functional coactivation of the brain regions has led to the study of multimodal technical frameworks that can help disentangle the mechanisms linking cognitive abilities and brain structural alterations

Dynamic Computation Offloading in MultiAccess Edge Computing via UltraReliable and LowLatency Communications IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.153) Pub Date : 20200318
Mattia Merluzzi; Paolo Di Lorenzo; Sergio Barbarossa; Valerio FrascollaThe goal of this work is to propose an energyefficient algorithm for dynamic computation offloading, in a multiaccess edge computing scenario, where multiple mobile users compete for a common pool of radio and computational resources. We focus on delaycritical applications, incorporating an upper bound on the probability that the overall time required to send the data and process them exceeds a

DataDriven Tree Transforms and Metrics. IEEE Trans. Signal Inf. Process. Over Netw. Pub Date : 20180818
Gal Mishne,Ronen Talmon,Israel Cohen,Ronald R Coifman,Yuval KlugerWe consider the analysis of high dimensional data given in the form of a matrix with columns consisting of observations and rows consisting of features. Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey a notion of locality. Therefore, traditional transforms and metrics cannot be used for data organization