IEEE Journal of Selected Topics in Signal Processing
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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.
Latest Published Articles
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Enhance Neighbor Reversibility in Subspace Learning for Image Retrieval
Wed Nov 07 00:00:00 EST 2018 Wed Nov 07 00:00:00 EST 2018 -
A General Framework for Understanding Compressed Subspace Clustering Algorithms
Sun Nov 04 00:00:00 EDT 2018 Sun Nov 04 00:00:00 EDT 2018 -
t-Schatten-
Norm for Low-Rank Tensor Recovery$p$ Thu Nov 01 00:00:00 EDT 2018 Thu Nov 01 00:00:00 EDT 2018 -
Coded Aperture Design for Compressive Spectral Subspace Clustering
Fri Oct 26 00:00:00 EDT 2018 Fri Oct 26 00:00:00 EDT 2018 -
Distributed Differentially Private Algorithms for Matrix and Tensor Factorization
Thu Oct 25 00:00:00 EDT 2018 Thu Oct 25 00:00:00 EDT 2018
Popular Articles
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Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
Tue Jul 01 00:00:00 EDT 2014 Tue Jul 01 00:00:00 EDT 2014 -
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
Mon Feb 08 00:00:00 EST 2016 Mon Feb 08 00:00:00 EST 2016 -
Deep Learning Based Communication Over the Air
Fri Dec 15 00:00:00 EST 2017 Fri Dec 15 00:00:00 EST 2017 -
An Overview of Massive MIMO: Benefits and Challenges
Tue Apr 15 00:00:00 EDT 2014 Tue Apr 15 00:00:00 EDT 2014 -
Over-the-Air Deep Learning Based Radio Signal Classification
Tue Jan 23 00:00:00 EST 2018 Tue Jan 23 00:00:00 EST 2018
Publish in this Journal
Meet Our Editors
Editor-in-Chief
Lina Karam
School of Electrical, Computer, and Energy Engineering
Arizona State University
Tempe, AZ 85287-5706 USAkaram@asu.edu
Popular Documents (January 2019)
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Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
Publication Year: 2014, Page(s):831 - 846
Cited by: Papers (619)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»
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An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
Publication Year: 2016, Page(s):436 - 453
Cited by: Papers (411)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»
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Deep Learning Based Communication Over the Air
Publication Year: 2018, Page(s):132 - 143
Cited by: Papers (16)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»
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An Overview of Massive MIMO: Benefits and Challenges
Publication Year: 2014, Page(s):742 - 758
Cited by: Papers (812)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»
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Over-the-Air Deep Learning Based Radio Signal Classification
Publication Year: 2018, Page(s):168 - 179
Cited by: Papers (6)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»
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Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays
Publication Year: 2016, Page(s):501 - 513
Cited by: Papers (195)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»
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Deep Learning Methods for Improved Decoding of Linear Codes
Publication Year: 2018, Page(s):119 - 131
Cited by: Papers (8)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»
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Alternating Minimization Algorithms for Hybrid Precoding in Millimeter Wave MIMO Systems
Publication Year: 2016, Page(s):485 - 500
Cited by: Papers (156)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»
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Light Field Image Processing: An Overview
Gaochang Wu ; Belen Masia ; Adrian Jarabo ; Yuchen Zhang ; Liangyong Wang ; Qionghai Dai ; Tianyou Chai ; Yebin LiuPublication Year: 2017, Page(s):926 - 954
Cited by: Papers (38)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»
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An Iterative BP-CNN Architecture for Channel Decoding
Publication Year: 2018, Page(s):144 - 159
Cited by: Papers (6)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»
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Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems
Publication Year: 2007, Page(s):586 - 597
Cited by: Papers (1593) | Patents (36)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»
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MPEG-H 3D Audio—The New Standard for Coding of Immersive Spatial Audio
Publication Year: 2015, Page(s):770 - 779
Cited by: Papers (23) | Patents (34)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»
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Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks
Publication Year: 2018, Page(s):160 - 167
Cited by: Papers (2)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»
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Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities
Publication Year: 2018, Page(s):180 - 190
Cited by: Papers (5)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»
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End-to-End Multimodal Emotion Recognition Using Deep Neural Networks
Panagiotis Tzirakis ; George Trigeorgis ; Mihalis A. Nicolaou ; Björn W. Schuller ; Stefanos ZafeiriouPublication Year: 2017, Page(s):1301 - 1309
Cited by: Papers (4)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»
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Channel Estimation and Hybrid Precoding for Frequency Selective Multiuser mmWave MIMO Systems
José P. González-Coma ; Javier Rodríguez-Fernández ; Nuria González-Prelcic ; Luis Castedo ; Robert W. HeathPublication Year: 2018, Page(s):353 - 367
Cited by: Papers (5)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»
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A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification
Publication Year: 2011, Page(s):606 - 617
Cited by: Papers (223)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»
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A Real-Time End-to-End Multilingual Speech Recognition Architecture
Javier Gonzalez-Dominguez ; David Eustis ; Ignacio Lopez-Moreno ; Andrew Senior ; Françoise Beaufays ; Pedro J. MorenoPublication Year: 2015, Page(s):749 - 759
Cited by: Papers (8)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»
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Coded Aperture Design for Compressive Spectral Subspace Clustering
Publication Year: 2018, Page(s):1589 - 1600Compressive spectral imaging (CSI) acquires compressed observations of a spectral scene by applying different coding patterns at each spatial location and then performing a spectral-wise integration. Relying on compressive sensing, spectral image reconstruction is achieved by using nonlinear and relatively expensive optimization-based algorithms. In the CSI literature, several works have focused o... View full abstract»
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Advances in cognitive radio networks: A survey
Publication Year: 2011, Page(s):5 - 23
Cited by: Papers (647)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»
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Proposal on Millimeter-Wave Channel Modeling for 5G Cellular System
Sooyoung Hur ; Sangkyu Baek ; Byungchul Kim ; Youngbin Chang ; Andreas F. Molisch ; Theodore S. Rappaport ; Katsuyuki Haneda ; Jeongho ParkPublication Year: 2016, Page(s):454 - 469
Cited by: Papers (97)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»
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Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Publication Year: 2011, Page(s):912 - 926
Cited by: Papers (326)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»
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Fully Deep Blind Image Quality Predictor
Publication Year: 2017, Page(s):206 - 220
Cited by: Papers (41)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»
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Digital Predistortion for Hybrid MIMO Transmitters
Publication Year: 2018, Page(s):445 - 454This 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»
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Improved Robust Tensor Principal Component Analysis via Low-Rank Core Matrix
Publication Year: 2018, Page(s):1378 - 1389Robust principal component analysis (RPCA) has been widely used for many data analysis problems in matrix data. Robust tensor principal component analysis (RTPCA) aims to extract the low rank and sparse components of multidimensional data, which is a generation of RPCA. The current RTPCA methods are directly based on tensor singular value decomposition (t-SVD), which is a new tensor decomposition ... 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.
Meet Our Editors
Editor-in-Chief
Lina Karam
School of Electrical, Computer, and Energy Engineering
Arizona State University
Tempe, AZ 85287-5706 USAkaram@asu.edu
Further Links
Aims & Scope
The scope of the IEEE Journal of Selected Topics in Signal Processing (JSTSP) is the Field of Interest of the IEEE Signal Processing Society: “The theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques. The term “signal” includes audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and other signals.” The format of the journal allows the exploration, in depth, of a signal processing topic by providing a tutorial article supported by several briefer articles in the topical area that provides the broadest possible examination of the topic considering the submissions. This format allows the Society to not only provide review issues on more mature signal processing topical areas, but also to explore new areas, particularly those at the nexus of other engineering disciplines that are dependent upon signal processing (e.g., biomedical engineering; language), as well as those not traditionally part of the engineering landscape (e.g., genetics; security; atmospheric prediction).
Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=4200690 More »
Frequency: 6
ISSN: 1932-4553
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Subjects
- Signal Processing & Analysis
Contacts
Editor-in-Chief
Lina Karam
School of Electrical, Computer, and Energy Engineering
Arizona State University
Tempe, AZ 85287-5706 USA
karam@asu.edu
About this Journal
Contacts
Editor-in-Chief
Lina Karam
School of Electrical, Computer, and Energy Engineering
Arizona State University
Tempe, AZ 85287-5706 USAkaram@asu.edu