# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 20 of 20
• ### SILVar: Single Index Latent Variable Models

Publication Year: 2018, Page(s):2790 - 2803
| | PDF (10053 KB) | HTML

A semiparametric, nonlinear regression model in the presence of latent variables is introduced. These latent variables can correspond to unmodeled phenomena or unmeasured agents in a complex networked system. This new formulation allows joint estimation of certain nonlinearities in the system, the direct interactions between measured variables and the effects of unmodeled elements on the observed ... View full abstract»

• ### Fast Low-Rank Bayesian Matrix Completion With Hierarchical Gaussian Prior Models

Publication Year: 2018, Page(s):2804 - 2817
| | PDF (1810 KB) | HTML

The problem of low-rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a Wishart distribution is specified as a hyperprior over the precision matrix. We s... View full abstract»

• ### Phase Retrieval via Reweighted Amplitude Flow

Publication Year: 2018, Page(s):2818 - 2833
| | PDF (1077 KB) | HTML

This paper deals with finding an $n$ -dimensional solution $boldsymbol {x}$ to a system of quadratic equations of the form $y_i=|langle boldsymbol {a}_i,boldsymbol {x}rangle |^2$ for View full abstract»

• ### On Nonconvex Decentralized Gradient Descent

Publication Year: 2018, Page(s):2834 - 2848
| | PDF (686 KB) | HTML

Consensus optimization has received considerable attention in recent years. A number of decentralized algorithms have been proposed for convex consensus optimization. However, to the behaviors or consensus nonconvex optimization, our understanding is more limited. When we lose convexity, we cannot hope that our algorithms always return global solutions though they sometimes still ... View full abstract»

• ### Application of Manifold Separation to Parametric Localization for Incoherently Distributed Sources

Publication Year: 2018, Page(s):2849 - 2860
| | PDF (1394 KB) | HTML

By using the manifold separation technique (MST), we develop a computationally efficient yet accurate estimator for localization of multiple incoherently distributed (ID) sources. In this paper, we have made the following main contributions: first, we use the MST to derive a closed-form expression for the ID signal covariance matrix that is applicable to the case with arbitrary array geometries or... View full abstract»

• ### Blind Interference Alignment for the $K$ -User MISO BC Under Limited Symbol Extension

Publication Year: 2018, Page(s):2861 - 2875
| | PDF (921 KB) | HTML

In this paper, we study the achievable degrees of freedom (DoFs) for $K$ -user multiple-input single-output broadcast channel (BC) in the absence of channel state information at the transmitter under the finite channel coherent time. For the considered $K$-... View full abstract»

• ### Beam Design and User Scheduling for Nonorthogonal Multiple Access With Multiple Antennas Based on Pareto Optimality

Publication Year: 2018, Page(s):2876 - 2891
| | PDF (922 KB) | HTML

In this paper, the problem of transmit beam design and user scheduling is investigated for multiuser (MU) multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) downlink. First, Pareto-optimal beam design is solved for two-user MISO broadcast channels (BCs) with successive interference cancelation (SIC), and certain properties of Pareto-optimal beam vectors are obtained. Based on... View full abstract»

• ### Using Joint Generalized Eigenvectors of a Set of Covariance Matrix Pencils for Deflationary Blind Source Extraction

Publication Year: 2018, Page(s):2892 - 2904
| | PDF (851 KB) | HTML

In this paper, we develop a new deflationary blind source extraction (BSE) algorithm that extracts source signals in a sequential fashion via the joint generalized eigenvectors of a set of covariance matrix pencils. The new concept of joint generalized eigenvector is defined. We prove that these vectors can be made unique and identical to the source extraction vectors with properly selected matrix... View full abstract»

• ### Learning the MMSE Channel Estimator

Publication Year: 2018, Page(s):2905 - 2917
| | PDF (1074 KB) | HTML

We present a method for estimating conditionally Gaussian random vectors with random covariance matrices, which uses techniques from the field of machine learning. Such models are typical in communication systems, where the covariance matrix of the channel vector depends on random parameters, e.g., angles of propagation paths. If the covariance matrices exhibit certain Toeplitz and shift-invarianc... View full abstract»

• ### Low-Rank Matrix Recovery From Noisy, Quantized, and Erroneous Measurements

Publication Year: 2018, Page(s):2918 - 2932
| | PDF (1370 KB) | HTML

This paper proposes a communication-reduced, cyber-resilient, and information-preserved data collection framework. Random noise and quantization are applied to the measurements before transmission to compress data and enhance data privacy. Leveraging the low-rank property of the data, we develop novel methods to recover the original data from quantized measurements even when partial measurements a... View full abstract»

• ### Massive Connectivity With Massive MIMO—Part I: Device Activity Detection and Channel Estimation

Publication Year: 2018, Page(s):2933 - 2946
| | PDF (693 KB) | HTML

This two-part paper considers an uplink massive device communication scenario in which a large number of devices are connected to a base station (BS), but user traffic is sporadic so that in any given coherence interval, only a subset of users is active. The objective is to quantify the cost of active user detection and channel estimation and to characterize the overall achievable rate of a grant-... View full abstract»

• ### Massive Connectivity With Massive MIMO—Part II: Achievable Rate Characterization

Publication Year: 2018, Page(s):2947 - 2959
| | PDF (536 KB) | HTML

This two-part paper aims to quantify the cost of device activity detection in an uplink massive connectivity scenario with a large number of devices but device activities are sporadic. Part I of this paper shows that in an asymptotic massive multiple-input multiple-output (MIMO) regime, device activity detection can always be made perfect. Part II of this paper subsequently shows that despite the ... View full abstract»

• ### Experiments and Models for Decision Fusion by Humans in Inference Networks

Publication Year: 2018, Page(s):2960 - 2971
| | PDF (953 KB) | HTML

With the advent of the Internet of Things (IoT) and a rapid deployment of smart devices and wireless sensor networks (WSNs), humans interact extensively with machine data. These human decision makers use sensors that provide information through a sociotechnical network. The sensors can be other human users or they can be IoT devices. The decision makers themselves are also part of the network, and... View full abstract»

• ### Blind Estimation of Sparse Broadband Massive MIMO Channels With Ideal and One-bit ADCs

Publication Year: 2018, Page(s):2972 - 2983
| | PDF (820 KB) | HTML

We study the maximum likelihood problem for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure, the temporal shifts across antennas in the broadband regime, and ultimately one-bit quantization at the receiver. The sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user'... View full abstract»

• ### Multipair Massive MIMO Relaying Systems With One-Bit ADCs and DACs

Publication Year: 2018, Page(s):2984 - 2997
| | PDF (857 KB) | HTML

This paper considers a multipair amplify-and-forward massive multipair multiple-input multiple-output relaying system with one-bit analog-to-digital converters and one-bit digital-to-analog converters at the relay. The channel state information is estimated via pilot training, and then utilized by the relay to perform simple maximum-ratio combining/maximum-ratio transmission processing. Leveraging... View full abstract»

• ### Constructing Binary Sequences With Good Correlation Properties: An Efficient Analytical-Computational Interplay

Publication Year: 2018, Page(s):2998 - 3007
| | PDF (1177 KB) | HTML

Binary sequence sets with asymptotically optimal auto/cross correlation peak sidelobe level (PSL) growth have been known in the literature for a long time, and their construction has been studied both analytically and numerically. In contrast, it has been a long-standing problem whether we can construct a family of binary sequences whose auto-correlation PSL grows in an optimal manner. In this pap... View full abstract»

• ### Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy

Publication Year: 2018, Page(s):3008 - 3023
| | PDF (1359 KB) | HTML

We define a metric, mutual information in frequency (MI-in-frequency), to detect and quantify the statistical dependence between different frequency components in the data, referred to as cross-frequency coupling and apply it to electrophysiological recordings from the brain to infer cross-frequency coupling. The current metrics used to quantify the cross-frequency coupling in neuroscience cannot ... View full abstract»

• ### Inner–Outer Support Set Pursuit for Distributed Compressed Sensing

Publication Year: 2018, Page(s):3024 - 3039
| | PDF (1026 KB) | HTML

We address the distributed compressed sensing problem of reconstructing a sequence of jointly sparse signals under the condition of an inaccurate and insufficient estimate of the common support set. Correlations between the jointly sparse signals are modeled by the joint sparsity model 1 (JSM-1). We propose a novel algorithm, namely inner–outer support set pursuit (IOSSP), which removes the... View full abstract»

• ### Multiple Object Tracking in Unknown Backgrounds With Labeled Random Finite Sets

Publication Year: 2018, Page(s):3040 - 3055
| | PDF (2400 KB) | HTML

This paper proposes an online multiple object tracker that can operate under unknown detection profile and clutter rate. In a majority of multiple object tracking applications, model parameters for background processes such as clutter and detection are unknown and vary with time; hence, the ability of the algorithm to adaptively learn these parameters is essential in practice. In this paper, we de... View full abstract»

• ### Rapidly Time-Varying Channel Estimation for Full-Duplex Amplify-and-Forward One-Way Relay Networks

Publication Year: 2018, Page(s):3056 - 3069
| | PDF (1223 KB)

Estimation of both cascaded and residual self-interference (RSI) channels and a new training frame structure are considered for full-duplex (FD) amplify-and-forward (AF) one-way relay networks with rapidly time-varying individual channels. To estimate the RSI and the rapidly time-varying cascaded channels, we propose a new training frame structure in which orthogonal training blocks are sent by th... 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