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# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 9 of 9
• ### A Unified Successive Pseudoconvex Approximation Framework

Publication Year: 2017, Page(s):3313 - 3328
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In this paper, we propose a successive pseudoconvex approximation algorithm to efficiently compute stationary points for a large class of possibly nonconvex optimization problems. The stationary points are obtained by solving a sequence of successively refined approximate problems, each of which is much easier to solve than the original problem. To achieve convergence, the approximate problem only... View full abstract»

• ### Optimal Training Sequences for Large-Scale MIMO-OFDM Systems

Publication Year: 2017, Page(s):3329 - 3343
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This paper considers the optimal design of training sequences for channel estimation in large-scale multiple-input multiple-output orthogonal frequency-division multiplexing systems. The application scenario of interest is when the number of transmit antennas for the downlink (or the number of receive antennas for the uplink) is large, but not large enough to benefit the asymptoti... View full abstract»

• ### Cell-Edge-Aware Precoding for Downlink Massive MIMO Cellular Networks

Publication Year: 2017, Page(s):3344 - 3358
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We propose a cell-edge-aware (CEA) zero forcing (ZF) precoder that exploits the excess spatial degrees of freedom provided by a large number of base station (BS) antennas to suppress inter-cell interference at the most vulnerable user equipments (UEs). We evaluate the downlink performance of CEA-ZF, as well as that of a conventional cell-edge-unaware (CEU) ZF prec... View full abstract»

• ### Finite-Resolution Effects in $p$ -Leader Multifractal Analysis

Publication Year: 2017, Page(s):3359 - 3368
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Multifractal analysis has become a standard signal processing tool, for which a promising new formulation, the $boldsymbol {p}$-leader multifractal formalism, has recently been proposed. It relies on novel multiscale quantities, the $boldsymbol {p}$-leaders... View full abstract»

• ### Hourglass Arrays and Other Novel 2-D Sparse Arrays With Reduced Mutual Coupling

Publication Year: 2017, Page(s):3369 - 3383
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Linear [one-dimensional (1-D)] sparse arrays such as nested arrays and minimum redundancy arrays have hole-free difference coarrays with $O(N^2)$ virtual sensor elements, where $N$ is the number of physical sensors. The hole-free property makes it easier to ... View full abstract»

• ### Labeled Random Finite Sets With Moment Approximation

Publication Year: 2017, Page(s):3384 - 3398
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The probability hypothesis density (PHD) filter was proposed as a practical approximation of the multitarget Bayes filter. The cardinalized PHD (CPHD) filter improves on the PHD filter by propagating cardinality distribution. However, both the PHD and CPHD filters have limitations in dealing with missed detections, extracting target state in their particle implementations, and maintaining track co... View full abstract»

• ### Maximum-Likelihood Approach With Bayesian Refinement for Multichannel-Wiener Postfiltering

Publication Year: 2017, Page(s):3399 - 3413
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Multichannel signal enhancement is an important task, e.g., in modern speech communication. It can be approached in a mean-square error sense by multichannel Wiener filtering, which is often implemented as a cascade of spatial beamforming and spectral enhancement postfiltering. In this paper, we propose a novel Bayesian perspective on the problem of postfilter adaptation. Specifically, we present ... View full abstract»

• ### Discriminative GoDec+ for Classification

Publication Year: 2017, Page(s):3414 - 3429
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GoDec+ is a robust low-rank representation model, which adopts correntropy to model noise and corruptions. To extend GoDec+ for classification, this paper proposes discriminative GoDec+ (D-GoDec+). In the model, each class is represented by a shared underlying subspace and a specific transformation matrix. Structural label information and the Fisher discrimination crite... View full abstract»

• ### Utility Maximizing Sequential Sensing Over a Finite Horizon

Publication Year: 2017, Page(s):3430 - 3445
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We consider the problem of optimally utilizing $N$ resources, each in an unknown binary state. The state of each resource can be inferred from state-dependent noisy measurements. Depending on its state, utilizing a resource results in either a reward or a penalty per unit time. The objective is a sequential strategy governing ... 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

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## Meet Our Editors

Editor-in-Chief
Sergios Theodoridis
University of Athens