IEEE Transactions on Signal Processing

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• Tensor Decomposition for Signal Processing and Machine Learning

Publication Year: 2017, Page(s):3551 - 3582
Cited by:  Papers (4)
| |PDF (1166 KB) | HTML Media

Tensors or multiway arrays are functions of three or more indices (i, j, k, . . . )-similar to matrices (two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a rich history, stretching over almost a century, and touching upon numerous disciplines; but they have only recently become ubiquitous in signal and data analytics at the confluence of signal processing,... View full abstract»

• Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

Publication Year: 2015, Page(s):6169 - 6183
Cited by:  Papers (51)
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This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot des... View full abstract»

• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

Publication Year: 2006, Page(s):4311 - 4322
Cited by:  Papers (3251)  |  Patents (27)
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In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity i... View full abstract»

• Variational Mode Decomposition

Publication Year: 2014, Page(s):531 - 544
Cited by:  Papers (222)
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During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like ... View full abstract»

• A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Publication Year: 2002, Page(s):174 - 188
Cited by:  Papers (5062)  |  Patents (108)
| |PDF (355 KB) | HTML

Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, w... View full abstract»

• Wireless Energy Beamforming Using Received Signal Strength Indicator Feedback

Publication Year: 2018, Page(s):224 - 235
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Multiple antenna techniques that allow energy beamforming have been looked upon as a possible candidate for increasing the transfer efficiency between the energy transmitter (ET) and the energy receiver in wireless power transfer. This paper introduces a novel scheme that facilitates energy beamforming by utilizing received signal strength indicator (RSSI) values to estimate the channel. First, in... View full abstract»

• Bayesian Compressive Sensing

Publication Year: 2008, Page(s):2346 - 2356
Cited by:  Papers (849)  |  Patents (3)
| |PDF (1561 KB) | HTML

The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directl... View full abstract»

• A sparse signal reconstruction perspective for source localization with sensor arrays

Publication Year: 2005, Page(s):3010 - 3022
Cited by:  Papers (847)  |  Patents (3)
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We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the ℓ1-norm. A number of recent theoretical results on sparsifying properties of ℓ1 penalties justify this choice. Explicitly enforcing the sparsit... View full abstract»

• Malicious User Detection Based on Low-Rank Matrix Completion in Wideband Spectrum Sensing

Publication Year: 2018, Page(s):5 - 17
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In cognitive radio networks, cooperative spectrum sensing (CSS) has been a promising approach to improve sensing performance by utilizing spatial diversity of participating secondary users (SUs). In current CSS networks, all cooperative SUs are assumed to be honest and genuine. However, the presence of malicious users sending out dishonest data can severely degrade the performance of CSS networks.... View full abstract»

• Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels

Publication Year: 2004, Page(s):461 - 471
Cited by:  Papers (1711)  |  Patents (75)
| |PDF (328 KB) | HTML

The use of space-division multiple access (SDMA) in the downlink of a multiuser multiple-input, multiple-output (MIMO) wireless communications network can provide a substantial gain in system throughput. The challenge in such multiuser systems is designing transmit vectors while considering the co-channel interference of other users. Typical optimization problems of interest include the capacity p... View full abstract»

• Semi-blind Channel Estimation for Multiuser Massive MIMO Systems

Publication Year: 2018, Page(s):540 - 553
| |PDF (1039 KB) | HTML

Motivated by recent developments in time-division duplex massive multiple-input multiple-output (MIMO) systems, this paper investigates semi-blind channel estimation for multiuser MIMO systems. An expectation-maximization (EM) algorithm is derived for semi-blind channel estimation and a tractable EM algorithm is obtained by assuming a Gaussian distribution for the unknown data symbols, which impro... View full abstract»

• Analysis and Design of Optimum Sparse Array Configurations for Adaptive Beamforming

Publication Year: 2018, Page(s):340 - 351
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In this paper, we analyze the effect of nonuniform array configurations on adaptive beamforming for enhanced signal-to-interference-plus-noise ratio (SINR). The array is configured using a given number of antennas or through a selection of subset of antennas from a larger available set, leading to a sparse array in both cases. The bounds on the highest achievable SINR for a given number of antenna... View full abstract»

• Sparse Bayesian Learning Approach for Outlier-Resistant Direction-of-Arrival Estimation

Publication Year: 2018, Page(s):744 - 756
| |PDF (1311 KB) | HTML

Conventional direction-of-arrival (DOA) estimation methods are sensitive to outlier measurements. Therefore, their performance may degrade substantially in the presence of impulsive noise. In this paper, we address the problem of DOA estimation in additive outliers from the perspective of sparse Bayesian learning (SBL). A Bayes-optimal algorithm is devised for robust DOA estimation, which can achi... View full abstract»

• Empirical Wavelet Transform

Publication Year: 2013, Page(s):3999 - 4010
Cited by:  Papers (140)  |  Patents (1)
| |PDF (3301 KB) | HTML

Some recent methods, like the empirical mode decomposition (EMD), propose to decompose a signal accordingly to its contained information. Even though its adaptability seems useful for many applications, the main issue with this approach is its lack of theory. This paper presents a new approach to build adaptive wavelets. The main idea is to extract the different modes of a signal by designing an a... View full abstract»

• Sparse Signal Recovery Using Iterative Proximal Projection

Publication Year: 2018, Page(s):879 - 894
| |PDF (1947 KB)

This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Fur... View full abstract»

• A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

Publication Year: 2006, Page(s):3852 - 3860
Cited by:  Papers (464)  |  Patents (21)
| |PDF (675 KB) | HTML

Conventional radio-frequency (RF) power amplifiers operating with wideband signals, such as wideband code-division multiple access (WCDMA) in the Universal Mobile Telecommunications System (UMTS) must be backed off considerably from their peak power level in order to control out-of-band spurious emissions, also known as "spectral regrowth." Adapting these amplifiers to wideband operation therefore... View full abstract»

• The constrained total least squares technique and its applications to harmonic superresolution

Publication Year: 1991, Page(s):1070 - 1087
Cited by:  Papers (169)  |  Patents (2)
| |PDF (1124 KB)

The constrained total least squares (CTLS) method is a natural extension of TLS to the case when the noise components of the coefficients are algebraically related. The CTLS technique is developed, and some of its applications to superresolution harmonic analysis are presented. The CTLS problem is reduced to an unconstrained minimization problem over a small set of variables. A perturbation analys... View full abstract»

• Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs

Publication Year: 2018, Page(s):1141 - 1154
| |PDF (1959 KB)

We develop a broadband channel estimation algorithm for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters (ADCs). Our methodology exploits the joint sparsity of the mmWave MIMO channel in the angle and delay domains. We formulate the estimation problem as a noisy quantized compressed-sensing problem and solve it using efficient approxi... View full abstract»

• Antenna Array Manifolds for High-Resolution Direction Finding

Publication Year: 2018, Page(s):923 - 932
| |PDF (948 KB)

The antenna array manifold is central to high-resolution direction finding. The analytic manifold is defined here as the mathematical model of the array response calculated by solving the electromagnetic equations for the waves impinging on the array. Array manifolds commonly used in the array signal processing literature are approximations of the analytic manifold. This paper describes the charac... View full abstract»

• Improving Wireless Physical Layer Security via Cooperating Relays

Publication Year: 2010, Page(s):1875 - 1888
Cited by:  Papers (675)
| |PDF (943 KB) | HTML

Physical (PHY) layer security approaches for wireless communications can prevent eavesdropping without upper layer data encryption. However, they are hampered by wireless channel conditions: absent feedback, they are typically feasible only when the source-destination channel is better than the source-eavesdropper channel. Node cooperation is a means to overcome this challenge and improve the perf... View full abstract»

• Performance Analysis of Linear Receivers for Uplink Massive MIMO FBMC-OQAM Systems

Publication Year: 2018, Page(s):830 - 842
| |PDF (735 KB) | HTML

Offset-quadratic-amplitude-modulation-based filterbank multicarrier (FBMC-OQAM) has been shown to be a promising alternative to cyclic prefix-orthogonal frequency division multiplexing. More recently, the use of FBMC-OQAM has been proposed in combination with massive MIMO communications. In this context, it is interesting to study the overall effect of massive MIMO on the FBMC-OQAM intrinsic inter... View full abstract»

• An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

Publication Year: 2011, Page(s):4331 - 4340
Cited by:  Papers (384)  |  Patents (1)
| |PDF (828 KB) | HTML

Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorith... View full abstract»

• LLR-Based Successive Cancellation List Decoding of Polar Codes

Publication Year: 2015, Page(s):5165 - 5179
Cited by:  Papers (41)
| |PDF (4241 KB) | HTML

We show that successive cancellation list decoding can be formulated exclusively using log-likelihood ratios. In addition to numerical stability, the log-likelihood ratio based formulation has useful properties that simplify the sorting step involved in successive cancellation list decoding. We propose a hardware architecture of the successive cancellation list decoder in the log-likelihood ratio ... View full abstract»

• Wireless Information and Power Transfer: Nonlinearity, Waveform Design, and Rate-Energy Tradeoff

Publication Year: 2018, Page(s):847 - 862
| |PDF (1213 KB)

The design of wireless information and power transfer (WIPT) has so far relied on an oversimplified and inaccurate linear model of the energy harvester. In this paper, we depart from this linear model and design WIPT considering the rectifier nonlinearity. We develop a tractable model of the rectifier nonlinearity that is flexible enough to cope with general multicarrier modulated input waveforms.... View full abstract»

• Robust Cooperative Spectrum Sensing for MIMO Cognitive Radio Networks Under CSI Uncertainty

Publication Year: 2018, Page(s):18 - 33
| |PDF (817 KB) | HTML

This paper considers the problem of cooperative spectrum sensing in multiuser multiple-input multiple-output cognitive radio networks considering the presence of uncertainty in the channel state information (CS!) of the secondary user channels available at the fusion center. Several schemes are proposed that employ cooperative decision rules based on local sensor decisions transmitted to the fusio... View full abstract»

• Robust Large Margin Deep Neural Networks

Publication Year: 2017, Page(s):4265 - 4280
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The generalization error of deep neural networks via their classification margin is studied in this paper. Our approach is based on the Jacobian matrix of a deep neural network and can be applied to networks with arbitrary nonlinearities and pooling layers, and to networks with different architectures such as feed forward networks and residual networks. Our analysis leads to the conclusion that a ... View full abstract»

• Network Localization and Synchronization Using Full-Duplex Radios

Publication Year: 2018, Page(s):714 - 728
| |PDF (1095 KB) | HTML

Both localization and synchronization of mobile nodes are important for wireless networks. In this paper, we propose new methods for network localization and synchronization (NLS) using full-duplex radios through only two frames of transmission. Specifically, all nodes simultaneously transmit their signature signals in the first frame, while receiving others' signals via full-duplex radios. In the... View full abstract»

• Massive MIMO Antenna Selection: Switching Architectures, Capacity Bounds, and Optimal Antenna Selection Algorithms

Publication Year: 2018, Page(s):1346 - 1360
| |PDF (945 KB) | HTML

Antenna selection is a multiple-input multiple-output (MIMO) technology, which uses radio frequency (RF) switches to select a good subset of antennas. Antenna selection can alleviate the requirement on the number of RF transceivers, thus being attractive for massive MIMO systems. In massive MIMO antenna selection systems, RF switching architectures need to be carefully considered. In this paper, w... View full abstract»

• The Sliding Singular Spectrum Analysis: A Data-Driven Nonstationary Signal Decomposition Tool

Publication Year: 2018, Page(s):251 - 263
| |PDF (2028 KB) | HTMLCode

Singular spectrum analysis (SSA) is a signal decomposition technique that aims at expanding signals into interpretable and physically meaningful components (e.g., sinusoids, noise, etc.). This paper presents new theoretical and practical results about the separability of the SSA and introduces a new method called sliding SSA. First, the SSA is combined with an unsupervised classification algorithm... View full abstract»

• Exploiting Spatial Channel Covariance for Hybrid Precoding in Massive MIMO Systems

Publication Year: 2017, Page(s):3818 - 3832
Cited by:  Papers (2)
| |PDF (1367 KB) | HTML

We propose a new hybrid precoding technique for massive multi-input multi-output (MIMO) systems using spatial channel covariance matrices in the analog precoder design. Applying a regularized zero-forcing precoder for the baseband precoding matrix, we find an unconstrained analog precoder that maximizes signal-to-leakage-plus-noise ratio (SLNR) while ignoring analog phase shifter constraints. Subs... View full abstract»

• MIMO Radar Waveform Design With PAPR and Similarity Constraints

Publication Year: 2018, Page(s):968 - 981
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The paper investigates the joint design of transmit waveform and receive filter for multiple-input multiple-output radar in the presence of signal-dependent interference, subject to a peak-to-average-power ratio constraint as well as a waveform similarity constraint. Owing to this kind of signal dependence and constraints, the formulated optimization problem of the output signal-to-interference-pl... View full abstract»

• Polar-Coded Non-Orthogonal Multiple Access

Publication Year: 2018, Page(s):1374 - 1389
| |PDF (2003 KB) | HTML

Non-orthogonal multiple access (NOMA) is one of the key techniques to address the high spectral efficiency and massive connectivity requirements for the fifth generation (5G) wireless networks. To efficiently realize NOMA, we propose a framework combining the binary polar coding and the NOMA transmission, which adequately utilizes the reliability distinctions among users. In this polar-coded NOMA ... View full abstract»

• A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs

Publication Year: 2018, Page(s):817 - 829
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An emerging way to deal with high-dimensional noneuclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This paper aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as time-vertex signal processing, that links together the time-domain si... View full abstract»

• Joint Design of Overlaid Communication Systems and Pulsed Radars

Publication Year: 2018, Page(s):139 - 154
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The focus of this paper is on coexistence between a communication system and a pulsed radar sharing the same bandwidth. Based on the fact that the interference generated by the radar onto the communication receiver is intermittent and depends on the density of scattering objects (such as, e.g., targets), we first show that the communication system is equivalent to a set of independent parallel cha... View full abstract»

• Outlier-Robust Matrix Completion via $ell _p$ -Minimization

Publication Year: 2018, Page(s):1125 - 1140
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Matrix completion refers to recovering a low-rank matrix from only a subset of its possibly noisy entries, and has a variety of important applications because many real-world signals can be modeled by a $n_1 times n_2$ matrix with rank $r ll min (n_1, n_2)$ View full abstract»

• Particle filters for positioning, navigation, and tracking

Publication Year: 2002, Page(s):425 - 437
Cited by:  Papers (752)  |  Patents (19)
| |PDF (335 KB) | HTML

A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position de... View full abstract»

• Joint Beamforming and Power-Splitting Control in Downlink Cooperative SWIPT NOMA Systems

Publication Year: 2017, Page(s):4874 - 4886
Cited by:  Papers (2)
| |PDF (1140 KB) | HTML

This paper investigates the application of simultaneous wireless information and power transfer (SWIPT) to cooperative non-orthogonal multiple access (NOMA). A new cooperative multiple-input single-output (MISO) SWIPT NOMA protocol is proposed, where a user with a strong channel condition acts as an energy-harvesting (EH) relay by adopting power splitting (PS) scheme to help a user with a poor cha... View full abstract»

• Sparse Bayesian learning for basis selection

Publication Year: 2004, Page(s):2153 - 2164
Cited by:  Papers (419)
| |PDF (328 KB) | HTML

Sparse Bayesian learning (SBL) and specifically relevance vector machines have received much attention in the machine learning literature as a means of achieving parsimonious representations in the context of regression and classification. The methodology relies on a parameterized prior that encourages models with few nonzero weights. In this paper, we adapt SBL to the signal processing problem of... View full abstract»

• Nonuniform fast Fourier transforms using min-max interpolation

Publication Year: 2003, Page(s):560 - 574
Cited by:  Papers (454)  |  Patents (4)
| |PDF (879 KB) | HTML

The fast Fourier transform (FFT) is used widely in signal processing for efficient computation of the FT of finite-length signals over a set of uniformly spaced frequency locations. However, in many applications, one requires nonuniform sampling in the frequency domain, i.e., a nonuniform FT. Several papers have described fast approximations for the nonuniform FT based on interpolating an oversamp... View full abstract»

• Direct Estimation of Density Functionals Using a Polynomial Basis

Publication Year: 2018, Page(s):558 - 572
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A number of fundamental quantities in statistical signal processing and information theory can be expressed as integral functions of two probability density functions. Such quantities are called density functionals as they map density functions onto the real line. For example, information divergence functions measure the dissimilarity between two probability density functions and are useful in a n... View full abstract»

• Learning Convex Regularizers for Optimal Bayesian Denoising

Publication Year: 2018, Page(s):1093 - 1105
| |PDF (1517 KB) | HTML

We propose a data-driven algorithm for the Bayesian estimation of stochastic processes from noisy observations. The primary statistical properties of the sought signal are specified by the penalty function (i.e., negative logarithm of the prior probability density function). Our alternating direction method of multipliers (ADMM) based approach translates the estimation task into successive applica... View full abstract»

• Novel Low-Density Signature for Synchronous CDMA Systems Over AWGN Channel

Publication Year: 2008, Page(s):1616 - 1626
Cited by:  Papers (128)  |  Patents (4)
| |PDF (1540 KB) | HTML

Novel low-density signature (LDS) structure is proposed for transmission and detection of symbol-synchronous communication over memoryless Gaussian channel. Given N as the processing gain, under this new arrangement, users' symbols are spread over N chips but virtually only dv < N chips that contain nonzero-values. The spread symbol is then so uniquely interleaved as the sampled, at ... View full abstract»

• Fast Resampling of Three-Dimensional Point Clouds via Graphs

Publication Year: 2018, Page(s):666 - 681
| |PDF (1555 KB) | HTML Media

To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we propose a randomized resampling strategy that selects a representative subset of points while preserving application-dependent features. The strategy is based on graphs, which can represent underlying surfaces and lend themselves well to efficient computation. We use a general feature-extraction operator to re... View full abstract»

• Sparsity-Based Two-Dimensional DOA Estimation for Coprime Array: From Sum–Difference Coarray Viewpoint

Publication Year: 2017, Page(s):5591 - 5604
| |PDF (5595 KB) | HTML

This paper addresses the issue of two-dimensional (2-D) direction of arrival (DOA) estimation with coprime planar arrays (CPPAs) via sparse representation. Our work differs from the partial spectral search approach [25], which suppresses the phase ambiguity by searching the common peaks of two subarrays. We focus on the coprime property of CPPA, where the sparse array extension model with sum-diff... View full abstract»

• Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning

Publication Year: 2017, Page(s):794 - 816
Cited by:  Papers (6)
| |PDF (1188 KB) | HTML

This paper gives an overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost. A general introduction of MM is presented, including a description of the basic principle and its convergence results. The extensions, acceleration schemes, and connection to other algorithmic frameworks are also cov... View full abstract»

• Matching pursuits with time-frequency dictionaries

Publication Year: 1993, Page(s):3397 - 3415
Cited by:  Papers (4109)  |  Patents (120)
| |PDF (2488 KB)

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit d... View full abstract»

• Sampling Continuous-Time Sparse Signals: A Frequency-Domain Perspective

Publication Year: 2018, Page(s):1410 - 1424
| |PDF (1480 KB) |  Media

We address the problem of sampling and reconstruction of sparse signals with finite rate of innovation. We derive general conditions under which perfect reconstruction is possible for sampling kernels satisfying Strang-Fix conditions. Previous results on the subject consider two particular cases; when the kernel is able to reproduce (complex) exponentials, or when it has the polynomial reproductio... View full abstract»

• Discrete Signal Processing on Graphs

Publication Year: 2013, Page(s):1644 - 1656
Cited by:  Papers (223)
| |PDF (2577 KB) | HTML

In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph. The resulting signals (data indexed by the nodes) are far removed from time or image signals indexed by well ordered ... View full abstract»

• Compressive Sensing-Based Detection With Multimodal Dependent Data

Publication Year: 2018, Page(s):627 - 640
| |PDF (1101 KB) | HTML

Detection with high-dimensional multimodal data is a challenging problem when there are complex inter- and intra- modal dependencies. While several approaches have been proposed for dependent data fusion (e.g., based on copula theory), their advantages come at a high price in terms of computational complexity. In this paper, we treat the detection problem with compressive sensing (CS) where compre... View full abstract»

• An Online Convex Optimization Approach to Proactive Network Resource Allocation

Publication Year: 2017, Page(s):6350 - 6364
Cited by:  Papers (1)
| |PDF (958 KB) | HTML

Existing approaches to online convex optimization make sequential one-slot-ahead decisions, which lead to (possibly adversarial) losses that drive subsequent decision iterates. Their performance is evaluated by the so-called regret that measures the difference of losses between the online solution and the best yet fixed overall solution in hindsight. The present paper deals with online convex opti... View full abstract»

Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Pier Luigi Dragotti
Imperial College London