# IEEE Journal of Selected Topics in Signal Processing

Includes the top 50 most frequently accessed documents for this publication according to the usage statistics for the month of

• ### An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

Publication Year: 2016, Page(s):436 - 453
Cited by:  Papers (359)
| | PDF (863 KB) | HTML

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc ... View full abstract»

• ### Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems

Publication Year: 2014, Page(s):831 - 846
Cited by:  Papers (563)
| | PDF (3623 KB) | HTML

Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver. Due to the high cost and power consumption of gigasample mixed-signal devices, mmWave precoding will li... View full abstract»

• ### Deep Learning Based Communication Over the Air

Publication Year: 2018, Page(s):132 - 143
Cited by:  Papers (8)
| | PDF (888 KB) | HTML

End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that ... View full abstract»

• ### An Overview of Massive MIMO: Benefits and Challenges

Publication Year: 2014, Page(s):742 - 758
Cited by:  Papers (743)
| | PDF (2077 KB) | HTML

Massive multiple-input multiple-output (MIMO) wireless communications refers to the idea equipping cellular base stations (BSs) with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple (linear) processing. In this paper, we present a comprehensive overview of state-of-the-art research... View full abstract»

• ### Light Field Image Processing: An Overview

Publication Year: 2017, Page(s):926 - 954
Cited by:  Papers (27)
| | PDF (1952 KB) | HTML

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher d... View full abstract»

• ### Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays

Publication Year: 2016, Page(s):501 - 513
Cited by:  Papers (172)
| | PDF (683 KB) | HTML

The potential of using of millimeter wave (mmWave) frequency for future wireless cellular communication systems has motivated the study of large-scale antenna arrays for achieving highly directional beamforming. However, the conventional fully digital beamforming methods which require one radio frequency (RF) chain per antenna element is not viable for large-scale antenna arrays due to the high co... View full abstract»

• ### A Real-Time End-to-End Multilingual Speech Recognition Architecture

Publication Year: 2015, Page(s):749 - 759
Cited by:  Papers (8)
| | PDF (2035 KB) | HTML

Automatic speech recognition (ASR) systems are used daily by millions of people worldwide to dictate messages, control devices, initiate searches or to facilitate data input in small devices. The user experience in these scenarios depends on the quality of the speech transcriptions and on the responsiveness of the system. For multilingual users, a further obstacle to natural interaction is the mon... View full abstract»

• ### Over-the-Air Deep Learning Based Radio Signal Classification

Publication Year: 2018, Page(s):168 - 179
Cited by:  Papers (2)
| | PDF (1731 KB) | HTML

We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification, and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequenc... View full abstract»

• ### End-to-End Multimodal Emotion Recognition Using Deep Neural Networks

Publication Year: 2017, Page(s):1301 - 1309
Cited by:  Papers (4)
| | PDF (1058 KB) | HTML

Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human-computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using audi... View full abstract»

• ### Deep Learning Methods for Improved Decoding of Linear Codes

Publication Year: 2018, Page(s):119 - 131
Cited by:  Papers (4)
| | PDF (1677 KB) | HTML

The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterat... View full abstract»

• ### An Iterative BP-CNN Architecture for Channel Decoding

Publication Year: 2018, Page(s):144 - 159
Cited by:  Papers (3)
| | PDF (1264 KB) | HTML

Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation - convolutional neural network (BP-CNN) architecture for channel decoding under correlated noise. This architecture concatenates a trained CNN with a standard BP decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and... View full abstract»

• ### Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

Publication Year: 2018, Page(s):180 - 190
Cited by:  Papers (3)
| | PDF (1495 KB) | HTML

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account S... View full abstract»

• ### MPEG-H 3D Audio—The New Standard for Coding of Immersive Spatial Audio

Publication Year: 2015, Page(s):770 - 779
Cited by:  Papers (21)  |  Patents (34)
| | PDF (845 KB) | HTML

The science and art of Spatial Audio is concerned with the capture, production, transmission, and reproduction of an immersive sound experience. Recently, a new generation of spatial audio technology has been introduced that employs elevated and lowered loudspeakers and thus surpasses previous `surround sound' technology without such speakers in terms of listener immersion and potential for spatia... View full abstract»

• ### Alternating Minimization Algorithms for Hybrid Precoding in Millimeter Wave MIMO Systems

Publication Year: 2016, Page(s):485 - 500
Cited by:  Papers (127)
| | PDF (924 KB) | HTML

Millimeter wave (mmWave) communications has been regarded as a key enabling technology for 5G networks, as it offers orders of magnitude greater spectrum than current cellular bands. In contrast to conventional multiple-input-multiple-output (MIMO) systems, precoding in mmWave MIMO cannot be performed entirely at baseband using digital precoders, as only a limited number of signal mixers and analo... View full abstract»

• ### Proposal on Millimeter-Wave Channel Modeling for 5G Cellular System

Publication Year: 2016, Page(s):454 - 469
Cited by:  Papers (86)
| | PDF (3851 KB) | HTML

This paper presents 28 GHz wideband propagation channel characteristics for millimeter wave (mmWave) urban cellular communication systems. The mmWave spectrum is considered as a key-enabling feature of 5G cellular communication systems to provide an enormous capacity increment; however, mmWave channel models are lacking today. The paper compares measurements conducted with a spherical scanning 28 ... View full abstract»

• ### Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks

Publication Year: 2018, Page(s):160 - 167
Cited by:  Papers (1)
| | PDF (380 KB) | HTML

With the increasing presence of cognitive radio networks as a means to address limited spectral resources, improved wireless security has become a necessity. In particular, the potential of a node to impersonate a licensed user demonstrates the need for techniques to authenticate a radio's true identity. In this paper, we use deep learning to detect physical-layer attributes for the identification... View full abstract»

• ### Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

Publication Year: 2007, Page(s):586 - 597
Cited by:  Papers (1534)  |  Patents (36)
| | PDF (1109 KB) | HTML

Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ) error term combined with a sparseness-inducing regularization term. Basis pursuit, the least absolute shrinkage and selection operato... View full abstract»

• ### Digital Predistortion for Hybrid MIMO Transmitters

Publication Year: 2018, Page(s):445 - 454
| | PDF (1542 KB) | HTML Media

This paper investigates digital predistortion (DPD) linearization of hybrid beamforming large-scale antenna transmitters. We propose a novel DPD processing and learning technique for an antenna subarray, which utilizes a combined signal of the individual power amplifier (PA) outputs in conjunction with a decorrelation-based learning rule. In effect, the proposed approach results in minimizing the ... View full abstract»

• ### Distributed State Estimation and Energy Management in Smart Grids: A Consensus${+}$Innovations Approach

Publication Year: 2014, Page(s):1022 - 1038
Cited by:  Papers (69)
| | PDF (2583 KB) | HTML

This paper reviews signal processing research for applications in the future electric power grid, commonly referred to as smart grid. Generally, it is expected that the grid of the future would differ from the current system by the increased integration of distributed generation, distributed storage, demand response, power electronics, and communications and sensing technologies. The consequence i... View full abstract»

• ### Machine Learning Techniques for Coherent CFAR Detection Based on Statistical Modeling of UHF Passive Ground Clutter

Publication Year: 2018, Page(s):104 - 118
| | PDF (1936 KB) | HTML

Ultra high frequency (UHF) passive ground clutter statistical models were determined from real data acquired by a passive radar for the design of approximations to the Neyman-Pearson detector based on machine learning techniques. The cross-ambiguity function was the input space without any preprocessing. The Gaussian model was proved to be suitable for high Doppler values. Other models were propos... View full abstract»

• ### Fully Deep Blind Image Quality Predictor

Publication Year: 2017, Page(s):206 - 220
Cited by:  Papers (29)
| | PDF (1378 KB) | HTML

In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not... View full abstract»

• ### Hybrid CTC/Attention Architecture for End-to-End Speech Recognition

Publication Year: 2017, Page(s):1240 - 1253
Cited by:  Papers (1)
| | PDF (976 KB) | HTML

Conventional automatic speech recognition (ASR) based on a hidden Markov model (HMM)/deep neural network (DNN) is a very complicated system consisting of various modules such as acoustic, lexicon, and language models. It also requires linguistic resources, such as a pronunciation dictionary, tokenization, and phonetic context-dependency trees. On the other hand, end-to-end ASR has become a popular... View full abstract»

• ### Feasibility of Mobile Cellular Communications at Millimeter Wave Frequency

Publication Year: 2016, Page(s):589 - 599
Cited by:  Papers (31)
| | PDF (2221 KB) | HTML

High data rate at high mobile speed will still be an essential requirement for the future 5G mobile cellular system. High frequency bands above 6 GHz are particularly promising for the 5G system because of large signal bandwidths such high frequencies can offer. By using high gain beamforming antennas, the problem of high propagation loss at high frequencies can be overcome. However, the use of be... View full abstract»

• ### Standardized Extensions of High Efficiency Video Coding (HEVC)

Publication Year: 2013, Page(s):1001 - 1016
Cited by:  Papers (203)  |  Patents (6)
| | PDF (1473 KB) | HTML

This paper describes extensions to the High Efficiency Video Coding (HEVC) standard that are active areas of current development in the relevant international standardization committees. While the first version of HEVC is sufficient to cover a wide range of applications, needs for enhancing the standard in several ways have been identified, including work on range extensions for color format and b... View full abstract»

• ### SNR Walls for Signal Detection

Publication Year: 2008, Page(s):4 - 17
Cited by:  Papers (807)  |  Patents (13)
| | PDF (1122 KB) | HTML

This paper considers the detection of the presence/absence of signals in uncertain low SNR environments. Small modeling uncertainties are unavoidable in any practical system and so robustness to them is a fundamental performance metric. The impact of these modeling uncertainties can be quantified by the position of the "SNR wall" below which a detector will fail to be robust, no matter how long it... View full abstract»

• ### Channel Estimation and Hybrid Precoding for Frequency Selective Multiuser mmWave MIMO Systems

Publication Year: 2018, Page(s):353 - 367
Cited by:  Papers (4)
| | PDF (795 KB) | HTML

Configuring the hybrid precoders and combiners in a millimeter wave multiuser multiple-input multiple-output system is challenging in frequency selective channels. In this paper, we develop a system that uses compressive estimation on the uplink to configure precoders and combiners for the downlink. In the first step, the base station (BS) simultaneously estimates the channels from all the mobile ... View full abstract»

• ### An End-to-End Deep Learning Approach to Simultaneous Speech Dereverberation and Acoustic Modeling for Robust Speech Recognition

Publication Year: 2017, Page(s):1289 - 1300
Cited by:  Papers (1)
| | PDF (820 KB) | HTML

We propose an integrated end-to-end automatic speech recognition (ASR) paradigm by joint learning of the front-end speech signal processing and back-end acoustic modeling. We believe that “only good signal processing can lead to top ASR performance” in challenging acoustic environments. This notion leads to a unified deep neural network (DNN) framework for distant speech processing that can achiev... View full abstract»

• ### Structured Data Fusion

Publication Year: 2015, Page(s):586 - 600
Cited by:  Papers (59)
| | PDF (2312 KB) | HTML

We present structured data fusion (SDF) as a framework for the rapid prototyping of knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set-stored as a dense, sparse, or incomplete tensor-is factorized with a matrix or tensor decomposition. Factorizations can be coupled, or fused, with each other by indicating which factors should be shared between data sets. At the... View full abstract»

• ### Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning

Publication Year: 2011, Page(s):912 - 926
Cited by:  Papers (303)
| | PDF (1133 KB) | HTML

We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlation and thus their performance degrades significantly with the correlation. In this paper, we propose a block sparse Bayesian learning framework which models t... View full abstract»

• ### Spectral Efficiency Optimization For Millimeter Wave Multiuser MIMO Systems

Publication Year: 2018, Page(s):455 - 468
Cited by:  Papers (1)
| | PDF (1389 KB) | HTML

As a key enabling technology for 5G wireless, millimeter wave (mmWave) communication motivates the utilization of large-scale antenna arrays for achieving highly directional beamforming. However, the high cost and power consumption of RF chains stand in the way of adoption of the optimal fully digital precoding in large-array systems. To reduce the number of RF chains while still maintaining the s... View full abstract»

• ### A Robust Generalized-Maximum Likelihood Unscented Kalman Filter for Power System Dynamic State Estimation

Publication Year: 2018, Page(s):578 - 592
| | PDF (1287 KB) | HTML

This paper develops a new robust generalized maximum-likelihood-type unscented Kalman filter (GM-UKF) that is able to suppress observation and innovation outliers while filtering out non-Gaussian process and measurement noise. Because the errors of the real and reactive power measurements calculated using phasor measurement units (PMUs) follow long-tailed probability distributions, the conventiona... View full abstract»

• ### Hybrid Precoder and Combiner Design With Low-Resolution Phase Shifters in mmWave MIMO Systems

Publication Year: 2018, Page(s):256 - 269
Cited by:  Papers (1)
| | PDF (1149 KB) | HTML

Millimeter-wave (mmWave) communications have been considered as a key technology for next-generation cellular systems and Wi-Fi networks because of its advances in providing orders-of-magnitude wider bandwidth than current wireless networks. Economical and energy-efficient analog/digital hybrid precoding and combining transceivers have been often proposed for mmWave massive multiple-input multiple... View full abstract»

Publication Year: 2011, Page(s):5 - 23
Cited by:  Papers (635)
| | PDF (740 KB) | HTML

With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the s... View full abstract»

• ### From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals

Publication Year: 2010, Page(s):375 - 391
Cited by:  Papers (571)  |  Patents (18)
| | PDF (931 KB) | HTML

Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support occupies only a small portion of a wide spectrum. Our primary design goals are efficient hardware implementation and low computational load on the sup... View full abstract»

• ### A Modulo-Based Architecture for Analog-to-Digital Conversion

Publication Year: 2018, Page(s):825 - 840
| | PDF (1571 KB) | HTML

Systems that capture and process analog signals must first acquire them through an analog-to-digital converter. While subsequent digital processing can remove statistical correlations present in the acquired data, the dynamic range of the converter is typically scaled to match that of the input analog signal. The present paper develops an approach for analog-to-digital conversion that aims at mini... View full abstract»

• ### The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation

Publication Year: 2010, Page(s):756 - 766
Cited by:  Papers (148)  |  Patents (3)
| | PDF (1208 KB) | HTML

There is a growing concern about chronic diseases and other health problems related to diet including obesity and cancer. The need to accurately measure diet (what foods a person consumes) becomes imperative. Dietary intake provides valuable insights for mounting intervention programs for prevention of chronic diseases. Measuring accurate dietary intake is considered to be an open research problem... View full abstract»

• ### Heterogeneous Sensor Data Fusion By Deep Multimodal Encoding

Publication Year: 2017, Page(s):479 - 491
Cited by:  Papers (2)
| | PDF (1328 KB) | HTML

Heterogeneous sensor data fusion is a challenging field that has gathered significant interest in recent years. Two of these challenges are learning from data with missing values, and finding shared representations for multimodal data to improve inference and prediction. In this paper, we propose amultimodal data fusion framework, the deep multimodal encoder (DME), based on deep learning technique... View full abstract»

• ### Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

Publication Year: 2016, Page(s):242 - 255
Cited by:  Papers (23)
| | PDF (3437 KB) | HTML

We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We consider the problem of minimizing a strongly convex function represented as the sum of an average of a large number of smooth convex functions, and a simple nonsmooth convex regularizer. Our method first performs a determ... View full abstract»

• ### A Comparison of Hybrid Beamforming and Digital Beamforming With Low-Resolution ADCs for Multiple Users and Imperfect CSI

Publication Year: 2018, Page(s):484 - 498
| | PDF (858 KB) | HTML

For 5G, it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omnidirectional coverage with a similar effective antenna aperture compared to state-of-the-art cellular systems, an antenna array is required at both the mobile and base station. Due to the large bandwidth and inefficient amplifiers available in CMOS for mmWave, the analog front end of the... View full abstract»

• ### An Overview of Low-Rank Matrix Recovery From Incomplete Observations

Publication Year: 2016, Page(s):608 - 622
Cited by:  Papers (40)
| | PDF (408 KB) | HTML

Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. This paper provides an overview of modern techniques for e... View full abstract»

• ### Data-Driven-Based Analog Beam Selection for Hybrid Beamforming Under mm-Wave Channels

Publication Year: 2018, Page(s):340 - 352
| | PDF (1048 KB) | HTML

Hybrid beamforming is a promising low-cost solution for large multiple-input multiple-output systems, where the base station is equipped with fewer radio frequency chains. In these systems, the selection of codewords for analog beamforming is essential to optimize the uplink sum rate. In this paper, based on machine learning, we propose a data-driven method of analog beam selection to achieve a ne... View full abstract»

• ### A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Publication Year: 2011, Page(s):606 - 617
Cited by:  Papers (218)
| | PDF (1529 KB) | HTML

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively impro... View full abstract»

• ### A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs

Publication Year: 2017, Page(s):742 - 753
Cited by:  Papers (2)
| | PDF (1881 KB) | HTML

Accurately predicting students' future performance based on their ongoing academic records is crucial for effectively carrying out necessary pedagogical interventions to ensure students' on-time and satisfactory graduation. Although there is a rich literature on predicting student performance when solving problems or studying for courses using data-driven approaches, predicting student performance... View full abstract»

• ### Short-Range Leakage Cancelation in FMCW Radar Transceivers Using an Artificial On-Chip Target

Publication Year: 2015, Page(s):1650 - 1660
Cited by:  Papers (11)
| | PDF (2159 KB) | HTML

A major drawback of frequency modulated continuous-wave (FMCW) radar systems is the permanent leakage from the transmit into the receive path. Besides leakage within the radar device itself, signal reflections from a fixed object in front of the antennas additionally introduce so-called short-range (SR) leakage. It causes a strong degradation of detection sensitivity due to the unpreventable phase... View full abstract»

• ### An Interior-Point Method for Large-Scale$\ell_1$-Regularized Least Squares

Publication Year: 2007, Page(s):606 - 617
Cited by:  Papers (1025)  |  Patents (5)
| | PDF (1442 KB) | HTML

Recently, a lot of attention has been paid to regularization based methods for sparse signal reconstruction (e.g., basis pursuit denoising and compressed sensing) and feature selection (e.g., the Lasso algorithm) in signal processing, statistics, and related fields. These problems can be cast as -regularized least-squares programs (LSPs), which can be reformulated as convex quadratic programs, and... View full abstract»

• ### Spectrum Access In Cognitive Radio Using a Two-Stage Reinforcement Learning Approach

Publication Year: 2018, Page(s):20 - 34
Cited by:  Papers (1)
| | PDF (1270 KB) | HTML

With the advent of the fifth generation of wireless standards and an increasing demand for higher throughput, methods to improve spectral efficiency of wireless systems have become very important. In the context of cognitive radio, a substantial increase in throughput is possible if the secondary user can make smart decisions regarding which channel to sense and when or how often to sense. Here, w... View full abstract»

• ### An Overview of Tiles in HEVC

Publication Year: 2013, Page(s):969 - 977
Cited by:  Papers (64)  |  Patents (2)
| | PDF (1348 KB) | HTML

Tiles is a new feature in the High Efficiency Video Coding (HEVC) standard that divides a picture into independent, rectangular regions. This division provides a number of advantages. Specifically, it increases the “parallel friendliness” of the new standard by enabling improved coding efficiency for parallel architectures, as compared to previous sliced based methods. Additionally, tiles facilita... View full abstract»

• ### Signal Processing for Music Analysis

Publication Year: 2011, Page(s):1088 - 1110
Cited by:  Papers (82)  |  Patents (2)
| | PDF (5693 KB) | HTML

Music signal processing may appear to be the junior relation of the large and mature field of speech signal processing, not least because many techniques and representations originally developed for speech have been applied to music, often with good results. However, music signals possess specific acoustic and structural characteristics that distinguish them from spoken language or other nonmusica... View full abstract»

• ### On the Fundamental Limit of Multipath Matching Pursuit

Publication Year: 2018, Page(s):916 - 927
| | PDF (521 KB) | HTML

Multipath matching pursuit (MMP) is a recent extension of the orthogonal matching pursuit algorithm that recovers sparse signals with a tree-searching strategy. In this paper, we present a new analysis for the MMP algorithm using the restricted isometry property. Our result shows that if the sampling matrix A ∈ Rm×nsatisfies the RIP of order K + L with isometry constant δK + L View full abstract»

• ### Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit

Publication Year: 2010, Page(s):310 - 316
Cited by:  Papers (355)  |  Patents (4)
| | PDF (469 KB) | HTML

We demonstrate a simple greedy algorithm that can reliably recover a vector <i>v</i> ¿ ¿<sup>d</sup> from incomplete and inaccurate measurements <i>x</i> = ¿<i>v</i> + <i>e</i>. Here, ¿ is a <i>N</i> x <i>d</i> measurement matrix with <i>N</i>&lt;&lt;d, and <i>e</i> is an error vecto... View full abstract»

## Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief

Shrikanth (Shri) S. Narayanan
Viterbi School of Engineering
University of Southern California
Los Angeles, CA 90089 USA
shri@sipi.usc.edu