# 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

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

Publication Year: 2014, Page(s):831 - 846
Cited by:  Papers (318)
| | 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»

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

Publication Year: 2016, Page(s):436 - 453
Cited by:  Papers (123)
| | 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»

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

Publication Year: 2014, Page(s):742 - 758
Cited by:  Papers (460)
| | 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»

• ### Deep Learning Based Communication Over the Air

Publication Year: 2018, Page(s):132 - 143
| | 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»

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

Publication Year: 2015, Page(s):749 - 759
Cited by:  Papers (3)
| | 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
| | 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»

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

Publication Year: 2018, Page(s):119 - 131
| | PDF (1676 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»

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

Publication Year: 2018, Page(s):160 - 167
| | PDF (379 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»

• ### Light Field Image Processing: An Overview

Publication Year: 2017, Page(s):926 - 954
Cited by:  Papers (12)
| | 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»

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

Publication Year: 2017, Page(s):1301 - 1309
| | 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»

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

Publication Year: 2018, Page(s):144 - 159
| | 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
| | 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»

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

Publication Year: 2016, Page(s):485 - 500
Cited by:  Papers (48)
| | 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»

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

Publication Year: 2018, Page(s):353 - 367
| | PDF (795 KB)

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»

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

Publication Year: 2015, Page(s):770 - 779
Cited by:  Papers (13)  |  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»

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

Publication Year: 2007, Page(s):586 - 597
Cited by:  Papers (1331)  |  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»

Publication Year: 2011, Page(s):5 - 23
Cited by:  Papers (546)
| | 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»

• ### SNR Walls for Signal Detection

Publication Year: 2008, Page(s):4 - 17
Cited by:  Papers (741)  |  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»

• ### DFT-based Hybrid Beamforming Multiuser Systems: Rate Analysis and Beam Selection

Publication Year: 2018, Page(s): 1
| | PDF (5178 KB)

This paper considers the discrete Fourier transform (DFT) based hybrid beamforming multiuser system and studies the use of analog beam selection schemes. We first analyze the uplink ergodic achievable rates of the zero-forcing (ZF) receiver and the maximum-ratio combining (MRC) receiver under Ricean fading conditions. We then examine the downlink ergodic achievable rates for the ZF and maximum-rat... View full abstract»

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

Publication Year: 2018, Page(s):20 - 34
| | PDF (1269 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»

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

Publication Year: 2013, Page(s):1001 - 1016
Cited by:  Papers (145)  |  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»

• ### 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»

• ### Signal Processing for Music Analysis

Publication Year: 2011, Page(s):1088 - 1110
Cited by:  Papers (69)  |  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»

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

Publication Year: 2017, Page(s):1240 - 1253
| | 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»

• ### Deep Learning for Passive Synthetic Aperture Radar

Publication Year: 2018, Page(s):90 - 103
| | PDF (683 KB) | HTML

We introduce a deep learning (DL) framework for inverse problems in imaging, and demonstrate the advantages and applicability of this approach in passive synthetic aperture radar (SAR) image reconstruction. We interpret image reconstruction as a machine learning task and utilize deep networks as forward and inverse solvers for imaging. Specifically, we design a recurrent neural network (RNN) archi... 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 (915)  |  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»

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

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

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 (168)
| | 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»

• ### Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling

Publication Year: 2014, Page(s):876 - 890
Cited by:  Papers (84)
| | PDF (3917 KB) | HTML

Joint Spatial Division and Multiplexing (JSDM) is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable “massive MIMO” gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into groups with approximately similar channel covariance eigenvectors and serving these groups by using ... View full abstract»

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

Publication Year: 2016, Page(s):501 - 513
Cited by:  Papers (63)
| | 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»

• ### Spectral Efficiency Optimization For mmWave Multiuser MIMO Systems

Publication Year: 2018, Page(s): 1
| | PDF (3438 KB)

Millimeter wave (mmWave) communication is a key enabling technology for 5G wireless systems, and such technology strongly motivates the utilization of large-scale antenna arrays and highly directional beamforming. To achieve low complexity and low power consumption design, hybrid precoding architecture, which utilizes only a small number of RF chains, has been recently proposed. Despite extensive ... View full abstract»

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

Publication Year: 2018, Page(s):256 - 269
| | PDF (1149 KB)

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»

• ### Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory

Publication Year: 2014, Page(s):802 - 814
Cited by:  Papers (125)
| | PDF (3442 KB) | HTML

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cell... 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 (5)
| | 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»

• ### Digital Predistortion for Hybrid MIMO Transmitters

Publication Year: 2018, Page(s): 1
| | PDF (25455 KB) |  Media

This article investigates digital predistortion (DPD) linearization of hybrid beamforming large-scale antenna transmitters. We propose a novel DPD processing and learning technique for an antenna sub-array, 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 t... View full abstract»

• ### Joint Design of Beam Selection and Precoding Matrices for mmWave MU-MIMO Systems Relying on Lens Antenna Arrays

Publication Year: 2018, Page(s):313 - 325
| | PDF (1529 KB)

Wireless transmission relying on lens antenna arrays is becoming more and more attractive for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems using a limited number of radio frequency chains due to the lens’ energy-focusing capability. In this paper, we consider the joint design of the beam selection and precoding matrices in order to maximize the sum-rate of a downli... View full abstract»

• ### Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

Publication Year: 2018, Page(s):6 - 19
| | PDF (774 KB) | HTML

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) accessing the frequency band of a primary user (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU, and subsequently, eavesdrops the reverse PU link to acquire the binary ACK/NACK... 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): 1
| | PDF (1368 KB)

For 5G it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omni- directional 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 basestation. Due to the large bandwidth and inefficient amplifiers available in CMOS for mmWave, the analog front-end of the... View full abstract»

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

Publication Year: 2017, Page(s):742 - 753
| | 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»

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

Publication Year: 2011, Page(s):912 - 926
Cited by:  Papers (230)
| | 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»

Publication Year: 2018, Page(s):45 - 60
| | PDF (1008 KB) | HTML

Most existing approaches to coexisting communication/radar systems assume that the radar and communication systems are coordinated, i.e., they share information, such as relative position, transmitted waveforms, and channel state. In this paper, we consider an uncoordinated scenario where a communication receiver is to operate in the presence of a number of radars, of which only a subset may be ac... 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 (474)  |  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»

• ### Power Scaling of Uplink Massive MIMO Systems With Arbitrary-Rank Channel Means

Publication Year: 2014, Page(s):966 - 981
Cited by:  Papers (98)
| | PDF (4288 KB) | HTML

This paper investigates the uplink achievable rates of massive multiple-input multiple-output (MIMO) antenna systems in Ricean fading channels, using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming perfect and imperfect channel state information (CSI). In contrast to previous relevant works, the fast fading MIMO channel matrix is assumed to have an arbitrary-rank determinis... View full abstract»

• ### On the Graph Fourier Transform for Directed Graphs

Publication Year: 2017, Page(s):796 - 811
Cited by:  Papers (1)
| | PDF (1717 KB) | HTML

The analysis of signals defined over a graph is relevant in many applications, such as social and economic networks, big data or biological networks, and so on. A key tool for analyzing these signals is the so-called graph Fourier transform (GFT). Alternative definitions of GFT have been suggested in the literature, based on the eigen-decomposition of either the graph Laplacian or adjacency matrix... View full abstract»

• ### Cognitive Target Tracking via Angle-Range-Doppler Estimation With Transmit Subaperturing FDA Radar

Publication Year: 2018, Page(s):76 - 89
| | PDF (1109 KB) | HTML

Cognitive radar is an intelligent active sensing technique, which can learn the interactions between radar and its surrounding environment and adaptively adjust the transmit waveforms or parameters for improved performance. In this paper, we propose a cognitive target tracking scheme via angle-range-Doppler estimation with transmit subaperturing frequency diverse array (TS-FDA) radar. FDA is an em... View full abstract»

• ### Fully Deep Blind Image Quality Predictor

Publication Year: 2017, Page(s):206 - 220
Cited by:  Papers (3)
| | 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»

• ### Tracking Moving Agents via Inexact Online Gradient Descent Algorithm

Publication Year: 2018, Page(s):202 - 217
| | PDF (778 KB) | HTML Media

Multiagent systems are being increasingly deployed in challenging environments for performing complex tasks such as multitarget tracking, search-and-rescue, and intrusion detection. Not with standing the computational limitations of individual robots, such systems rely on collaboration to sense and react to the environment. This paper formulates the generic target tracking problem as a time-varyin... View full abstract»

• ### Generalized Global Bandit and Its Application in Cellular Coverage Optimization

Publication Year: 2018, Page(s):218 - 232
| | PDF (1027 KB) | HTML

Motivated by the engineering problem of cellular coverage optimization, we propose a novel multiarmed bandit model called generalized global bandit. We develop a series of greedy algorithms that have the capability to handle nonmonotonic but decomposable reward functions, multidimensional global parameters, and switching costs. The proposed algorithms are rigorously analyzed under the multiarmed b... View full abstract»

• ### Energy Efficiency of mmWave Massive MIMO Precoding With Low-Resolution DACs

Publication Year: 2018, Page(s):298 - 312
| | PDF (1074 KB)

With the congestion of the sub-6 GHz spectrum, the interest in massive multiple-input multiple-output (MIMO) systems operating on millimeter wave spectrum grows. In order to reduce the power consumption of such massive MIMO systems, hybrid analog/digital transceivers and application of low-resolution digital-to-analog/analog-to-digital converters have been recently proposed. In this work, we inves... View full abstract»

• ### Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations

Publication Year: 2014, Page(s):916 - 929
Cited by:  Papers (74)
| | PDF (2954 KB) | HTML

Large-scale (or massive) multiple-input multiple-out put (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose-to the best of our knowledge-the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)... 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