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# IEEE Transactions on Signal Processing

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• ### $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

Publication Year: 2006, Page(s):4311 - 4322
Cited by:  Papers (2840)  |  Patents (27)
| | PDF (1725 KB) | HTML

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity i... View full abstract»

• ### A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Publication Year: 2002, Page(s):174 - 188
Cited by:  Papers (4687)  |  Patents (107)
| | PDF (355 KB) | HTML

Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, w... View full abstract»

• ### Variational Mode Decomposition

Publication Year: 2014, Page(s):531 - 544
Cited by:  Papers (132)
| | PDF (3074 KB) | HTML

During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like ... View full abstract»

• ### Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels

Publication Year: 2004, Page(s):461 - 471
Cited by:  Papers (1618)  |  Patents (70)
| | PDF (328 KB) | HTML

The use of space-division multiple access (SDMA) in the downlink of a multiuser multiple-input, multiple-output (MIMO) wireless communications network can provide a substantial gain in system throughput. The challenge in such multiuser systems is designing transmit vectors while considering the co-channel interference of other users. Typical optimization problems of interest include the capacity p... View full abstract»

• ### Super-Resolution Delay-Doppler Estimation for OFDM Passive Radar

Publication Year: 2017, Page(s):2197 - 2210
Cited by:  Papers (1)
| | PDF (1297 KB) | HTML

In this paper, we consider the problem of joint delay-Doppler estimation of moving targets in a passive radar that makes use of orthogonal frequency-division multiplexing communication signals. A compressed sensing algorithm is proposed to achieve supper resolution and better accuracy, using both the atomic norm and the ℓ1-norm. The atomic norm is used to manifest the signal spar... View full abstract»

• ### Improving Wireless Physical Layer Security via Cooperating Relays

Publication Year: 2010, Page(s):1875 - 1888
Cited by:  Papers (588)
| | PDF (943 KB) | HTML

Physical (PHY) layer security approaches for wireless communications can prevent eavesdropping without upper layer data encryption. However, they are hampered by wireless channel conditions: absent feedback, they are typically feasible only when the source-destination channel is better than the source-eavesdropper channel. Node cooperation is a means to overcome this challenge and improve the perf... View full abstract»

• ### Tensor Decomposition for Signal Processing and Machine Learning

Publication Year: 2017, Page(s):3551 - 3582
| | PDF (1166 KB) | HTML Media

Tensors or multiway arrays are functions of three or more indices $(i,j,k,\ldots)$—similar to matrices (two-way arrays), which are functions of two indices $(r,c)$ for (row, column). Tensors have a rich history, stretching over almos... View full abstract»

• ### Structured Compressed Sensing: From Theory to Applications

Publication Year: 2011, Page(s):4053 - 4085
Cited by:  Papers (341)  |  Patents (8)
| | PDF (1987 KB) | HTML

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, ... View full abstract»

• ### A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

Publication Year: 2006, Page(s):3852 - 3860
Cited by:  Papers (422)  |  Patents (18)
| | PDF (675 KB) | HTML

Conventional radio-frequency (RF) power amplifiers operating with wideband signals, such as wideband code-division multiple access (WCDMA) in the Universal Mobile Telecommunications System (UMTS) must be backed off considerably from their peak power level in order to control out-of-band spurious emissions, also known as "spectral regrowth." Adapting these amplifiers to wideband operation therefore... View full abstract»

• ### Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory

Publication Year: 2017, Page(s):1929 - 1944
Cited by:  Papers (2)
| | PDF (345 KB) | HTML Media

In this two-part paper, we propose a general algorithmic framework for the minimization of a nonconvex smooth function subject to nonconvex smooth constraints, and also consider extensions to some structured, nonsmooth problems. The algorithm solves a sequence of (separable) strongly convex problems and maintains feasibility at each iteration. Convergence to a stationary solution of the original n... View full abstract»

• ### Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning

Publication Year: 2017, Page(s):794 - 816
Cited by:  Papers (1)
| | PDF (1188 KB) | HTML

This paper gives an overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost. A general introduction of MM is presented, including a description of the basic principle and its convergence results. The extensions, acceleration schemes, and connection to other algorithmic frameworks are also cov... View full abstract»

• ### Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation

Publication Year: 2010, Page(s):4131 - 4143
Cited by:  Papers (97)  |  Patents (3)
| | PDF (1375 KB) | HTML

In multiple-input multiple-output (MIMO) systems, the use of many radio frequency (RF) and analog-to-digital converter (ADC) chains at the receiver is costly. Analog beamformers operating in the RF domain can reduce the number of antenna signals to a feasible number of baseband channels. Subsequently, digital beamforming is used to capture the desired user signal. In this paper, we consider the de... View full abstract»

• ### Parallel and Distributed Methods for Constrained Nonconvex Optimization-Part II: Applications in Communications and Machine Learning

Publication Year: 2017, Page(s):1945 - 1960
Cited by:  Papers (1)
| | PDF (1103 KB) | HTML Media

In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex objective function, subject to nonconvex constraints, based on inner convex approximations. This Part II is devoted to the (nontrivial) application of the framework to the following relevant large-scale problems ranging from communications to machine learning: 1) (generalizations of)... View full abstract»

• ### Direct Localization for Massive MIMO

Publication Year: 2017, Page(s):2475 - 2487
| | PDF (941 KB) | HTML

Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization, thanks to their high angular resolution. A difficult problem arising indoors and outdoors is localizing users over multipath channels. Localization based on angle of arrival (AOA) generally involves a two-step procedure, where signals are first ... View full abstract»

• ### Superimposed Pilots Are Superior for Mitigating Pilot Contamination in Massive MIMO

Publication Year: 2017, Page(s):2917 - 2932
| | PDF (750 KB) | HTML

In this paper, superimposed pilots are introduced as an alternative to time-multiplexed pilot and data symbols for mitigating pilot contamination in massive multiple-input multiple-output (MIMO) systems. We propose a non-iterative scheme for uplink channel estimation based on superimposed pilots and derive an expression for the uplink signal-to-interference-plus-noise ratio (SINR) at the output of... View full abstract»

• ### Sparse Signal Approximation via Nonseparable Regularization

Publication Year: 2017, Page(s):2561 - 2575
| | PDF (1408 KB) | HTML

The calculation of a sparse approximate solution to a linear system of equations is often performed using either L1-norm regularization and convex optimization or nonconvex regularization and nonconvex optimization. Combining these principles, this paper describes a type of nonconvex regularization that maintains the convexity of the objective function, thereby allowing the calculation of a sparse... View full abstract»

• ### Invariant Adaptive Detection of Range-Spread Targets Under Structured Noise Covariance

Publication Year: 2017, Page(s):3048 - 3061
| | PDF (787 KB) | HTML

The invariance principle is adopted to develop an exhaustive study for adaptive detection of range-spread targets in Gaussian noise sharing a block-diagonal covariance structure. For this problem, the usual generalized likelihood ratio principle is intractable. In this paper, we first determine the largest group of affine transformations that does not alter the decision problem. Then, a maximal in... View full abstract»

• ### A sparse signal reconstruction perspective for source localization with sensor arrays

Publication Year: 2005, Page(s):3010 - 3022
Cited by:  Papers (739)  |  Patents (3)
| | PDF (704 KB) | HTML

We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the ℓ1-norm. A number of recent theoretical results on sparsifying properties of ℓ1 penalties justify this choice. Explicitly enforcing the sparsit... View full abstract»

• ### Discrete Signal Processing on Graphs

Publication Year: 2013, Page(s):1644 - 1656
Cited by:  Papers (137)
| | PDF (2577 KB) | HTML

In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph. The resulting signals (data indexed by the nodes) are far removed from time or image signals indexed by well ordered ... View full abstract»

• ### Bayesian Compressive Sensing

Publication Year: 2008, Page(s):2346 - 2356
Cited by:  Papers (767)  |  Patents (3)
| | PDF (1561 KB) | HTML

The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directl... View full abstract»

• ### An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

Publication Year: 2011, Page(s):4331 - 4340
Cited by:  Papers (342)  |  Patents (1)
| | PDF (828 KB) | HTML

Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorith... View full abstract»

• ### Novel Low-Density Signature for Synchronous CDMA Systems Over AWGN Channel

Publication Year: 2008, Page(s):1616 - 1626
Cited by:  Papers (96)  |  Patents (2)
| | PDF (1540 KB) | HTML

Novel low-density signature (LDS) structure is proposed for transmission and detection of symbol-synchronous communication over memoryless Gaussian channel. Given N as the processing gain, under this new arrangement, users' symbols are spread over N chips but virtually only dv < N chips that contain nonzero-values. The spread symbol is then so uniquely interleaved as the sampled, at ... View full abstract»

• ### Matching pursuits with time-frequency dictionaries

Publication Year: 1993, Page(s):3397 - 3415
Cited by:  Papers (3821)  |  Patents (115)
| | PDF (2488 KB)

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit d... View full abstract»

• ### Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals

Publication Year: 2006, Page(s):884 - 893
Cited by:  Papers (368)  |  Patents (2)
| | PDF (384 KB) | HTML

In this paper, we study the performance of multiple-input multiple-output channel estimation methods using training sequences. We consider the popular linear least squares (LS) and minimum mean-square-error (MMSE) approaches and propose new scaled LS (SLS) and relaxed MMSE techniques which require less knowledge of the channel second-order statistics and/or have better performance than the convent... View full abstract»

• ### Multiple-Input Multiple-Output OFDM With Index Modulation: Low-Complexity Detector Design

Publication Year: 2017, Page(s):2758 - 2772
| | PDF (1529 KB)

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM), which provides a flexible trade-off between spectral efficiency and error performance, is recently proposed as a promising transmission technique for energy-efficient 5G wireless communications systems. However, due to the dependence of subcarrier symbols within each subblock and the str... View full abstract»

• ### Sampling and Exact Reconstruction of Pulses with Variable Width

Publication Year: 2017, Page(s):2629 - 2644
| | PDF (5160 KB) | HTML

Recent sampling results enable the reconstruction of signals composed of streams of fixed-shaped pulses. These results have found applications in topics as varied as channel estimation, biomedical imaging and radio astronomy. However, in many real signals, the pulse shapes vary throughout the signal. In this paper, we show how to sample and perfectly reconstruct Lorentzian pulses with variable wid... View full abstract»

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

Publication Year: 2017, Page(s):3329 - 3343
| | PDF (1084 KB) | HTML

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 asymptotical optimality of... View full abstract»

• ### Proximity Without Consensus in Online Multiagent Optimization

Publication Year: 2017, Page(s):3062 - 3077
| | PDF (883 KB) | HTML

We consider stochastic optimization problems in multiagent settings, where a network of agents aims to learn parameters that are optimal in terms of a global convex objective, while giving preference to locally observed streaming information. To do so, we depart from the canonical decentralized optimization framework where agreement constraints are enforced, and instead formulate a problem where e... View full abstract»

• ### Robust Multilinear Tensor Rank Estimation Using Higher Order Singular Value Decomposition and Information Criteria

Publication Year: 2017, Page(s):1196 - 1206
| | PDF (1624 KB) | HTML Media

Model selection in tensor decomposition is important for real applications if the rank of the original data tensor is unknown and the observed tensor is noisy. In the Tucker model, the minimum description length (MDL) or Bayesian information criteria have been applied to tensors via matrix unfolding, but these methods are sensitive to noise when the tensors have a multilinear low rank structure gi... View full abstract»

• ### Fast Unit-Modulus Least Squares With Applications in Beamforming

Publication Year: 2017, Page(s):2875 - 2887
| | PDF (1345 KB) | HTML Media

Unit-modulus least squares (ULS) problems arise in many applications, including phase-only beamforming, sensor network localization, synchronization, phase retrieval, and radar code design. ULS formulations can always be recast as unit-modulus quadratic programs, to which semidefinite relaxation (SDR) can be applied, and is often the state-of-the-art approach. However, SDR lifts the problem dimens... View full abstract»

• ### Bidirectional recurrent neural networks

Publication Year: 1997, Page(s):2673 - 2681
Cited by:  Papers (192)  |  Patents (7)
| | PDF (264 KB)

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are e... View full abstract»

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

Publication Year: 2017, Page(s):3344 - 3358
| | PDF (1259 KB) | HTML

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 precoder in a network with a random BS... View full abstract»

• ### Sub-Nyquist Cyclostationary Detection for Cognitive Radio

Publication Year: 2017, Page(s):3004 - 3019
| | PDF (1018 KB) | HTML

Cognitive radio requires efficient and reliable spectrum sensing of wideband signals. In order to cope with the sampling rate bottleneck, new sampling methods have been proposed that sample below the Nyquist rate. However, such techniques decrease the signal-to-noise ratio (SNR), deteriorating the performance of subsequent energy detection. Cyclostationary detection, which exploits the periodic pr... View full abstract»

• ### Particle filters for positioning, navigation, and tracking

Publication Year: 2002, Page(s):425 - 437
Cited by:  Papers (697)  |  Patents (18)
| | PDF (335 KB) | HTML

A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position de... View full abstract»

• ### Source Association, DOA, and Fading Coefficients Estimation for Multipath Signals

Publication Year: 2017, Page(s):2773 - 2786
Cited by:  Papers (1)
| | PDF (1057 KB)

This paper addresses the source association (SA), direction of arrival (DOA), and fading coefficients (FCs) estimation problem in multipath environment. First, we establish a rank reduction property for a multipath signal model with the existence of multiple groups of coherent signals. Subsequently, based on this property, effective algorithms for SA, DOA, and FCs estimation have been developed. T... View full abstract»

• ### BDMA in Multicell Massive MIMO Communications: Power Allocation Algorithms

Publication Year: 2017, Page(s):2962 - 2974
| | PDF (653 KB) | HTML

We investigate power allocation strategies for beam division multiple access transmission in multicell massive multiple-input-multiple-output (MIMO) communications. Focusing on massive MIMO downlink with only statistical channel state information at serving base stations and multiantenna terminals, the eigenmatrices of channel transmit covariance matrices become identical and independent of termin... View full abstract»

• ### Magnetic MIMO Signal Processing and Optimization for Wireless Power Transfer

Publication Year: 2017, Page(s):2860 - 2874
| | PDF (1338 KB)

In magnetic resonant coupling (MRC) enabled multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems, multiple transmitters (TXs) are used to enhance the efficiency of simultaneous power transfer to multiple receivers (RXs) by constructively combining their induced magnetic fields, a technique termed “magnetic beamforming”. In this paper, we study the optimal magne... View full abstract»

• ### Received-Signal-Strength Threshold Optimization Using Gaussian Processes

Publication Year: 2017, Page(s):2164 - 2177
| | PDF (1329 KB) | HTML

There is a big trend nowadays to use event-triggered proximity report for indoor positioning. This paper presents a generic received-signal-strength (RSS) threshold optimization framework for generating informative proximity reports. The proposed framework contains five main building blocks, namely the deployment information, RSS model, positioning metric selection, optimization process and manage... View full abstract»

• ### Transmit beamforming for physical-layer multicasting

Publication Year: 2006, Page(s):2239 - 2251
Cited by:  Papers (533)  |  Patents (5)
| | PDF (752 KB) | HTML

This paper considers the problem of downlink transmit beamforming for wireless transmission and downstream precoding for digital subscriber wireline transmission, in the context of common information broadcasting or multicasting applications wherein channel state information (CSI) is available at the transmitter. Unlike the usual "blind" isotropic broadcasting scenario, the availability of CSI all... View full abstract»

• ### High Dimensional Low Rank Plus Sparse Matrix Decomposition

Publication Year: 2017, Page(s):2004 - 2019
| | PDF (1868 KB) | HTML

This paper is concerned with the problem of lowrank plus sparse matrix decomposition for big data. Conventional algorithms for matrix decomposition use the entire data to extract the low-rank and sparse components, and are based on optimization problems with complexity that scales with the dimension of the data, which limits their scalability. Furthermore, existing randomized approaches mostly rel... View full abstract»

• ### Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization

Publication Year: 2003, Page(s):2381 - 2401
Cited by:  Papers (726)  |  Patents (9)
| | PDF (1133 KB) | HTML

This paper addresses the joint design of transmit and receive beamforming or linear processing (commonly termed linear precoding at the transmitter and equalization at the receiver) for multicarrier multiple-input multiple-output (MIMO) channels under a variety of design criteria. Instead of considering each design criterion in a separate way, we generalize the existing results by developing a uni... View full abstract»

• ### Group Sparse Bayesian Learning Via Exact and Fast Marginal Likelihood Maximization

Publication Year: 2017, Page(s):2741 - 2753
| | PDF (1320 KB) | HTML

This paper concerns the sparse Bayesian learning (SBL) problem for group sparse signals. Group sparsity means that the signal coefficients can be divided into groups and that the entries in one group are simultaneously zero or nonzero. In SBL, each group is controlled by a hyperparameter, which is estimated by solving the marginal likelihood maximization (MLM) problem. MLM is used to maximize the ... View full abstract»

• ### Empirical Wavelet Transform

Publication Year: 2013, Page(s):3999 - 4010
Cited by:  Papers (92)  |  Patents (1)
| | PDF (3301 KB) | HTML

Some recent methods, like the empirical mode decomposition (EMD), propose to decompose a signal accordingly to its contained information. Even though its adaptability seems useful for many applications, the main issue with this approach is its lack of theory. This paper presents a new approach to build adaptive wavelets. The main idea is to extract the different modes of a signal by designing an a... View full abstract»

• ### Orthogonal Frequency Division Multiplexing With Index Modulation

Publication Year: 2013, Page(s):5536 - 5549
Cited by:  Papers (65)
| | PDF (2852 KB) | HTML

In this paper, a novel orthogonal frequency division multiplexing (OFDM) scheme, called OFDM with index modulation (OFDM-IM), is proposed for operation over frequency-selective and rapidly time-varying fading channels. In this scheme, the information is conveyed not only by M-ary signal constellations as in classical OFDM, but also by the indices of the subcarriers, which are activated according t... View full abstract»

• ### Stationary Signal Processing on Graphs

Publication Year: 2017, Page(s):3462 - 3477
| | PDF (1410 KB) | HTML

Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or networks. In this paper, we generalize the traditional concept of wide sense stationarity to signals defined over the vertices of arbitrary weighted un... View full abstract»

• ### Weight Optimization for Consensus Algorithms With Correlated Switching Topology

Publication Year: 2010, Page(s):3788 - 3801
Cited by:  Papers (32)
| | PDF (590 KB) | HTML

We design the weights in consensus algorithms for spatially correlated random topologies. These arise with 1) networks with spatially correlated random link failures and 2) networks with randomized averaging protocols. We show that the weight optimization problem is convex for both symmetric and asymmetric random graphs. With symmetric random networks, we choose the consensus mean-square error (MS... View full abstract»

• ### Relative location estimation in wireless sensor networks

Publication Year: 2003, Page(s):2137 - 2148
Cited by:  Papers (944)  |  Patents (30)
| | PDF (1120 KB) | HTML

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining loc... View full abstract»

• ### Fast Approximation Algorithms for a Class of Non-convex QCQP Problems Using First-Order Methods

Publication Year: 2017, Page(s):3494 - 3509
| | PDF (710 KB) | HTML Media

A number of important problems in engineering can be formulated as non-convex quadratically constrained quadratic programming (QCQP). The general QCQP problem is NP-Hard. In this paper, we consider a class of non-convex QCQP problems that are expressible as the maximization of the point-wise minimum of homogeneous convex quadratics over a “simple” convex set. Existing approximation s... View full abstract»

• ### LLR-Based Successive Cancellation List Decoding of Polar Codes

Publication Year: 2015, Page(s):5165 - 5179
Cited by:  Papers (25)
| | PDF (4241 KB) | HTML

We show that successive cancellation list decoding can be formulated exclusively using log-likelihood ratios. In addition to numerical stability, the log-likelihood ratio based formulation has useful properties that simplify the sorting step involved in successive cancellation list decoding. We propose a hardware architecture of the successive cancellation list decoder in the log-likelihood ratio ... View full abstract»

• ### Recovery of Sparse Signals Using Multiple Orthogonal Least Squares

Publication Year: 2017, Page(s):2049 - 2062
| | PDF (1040 KB) | HTML

Sparse recovery aims to reconstruct sparse signals from compressed linear measurements. In this paper, we propose a sparse recovery algorithm called multiple orthogonal least squares (MOLS), which extends the well-known orthogonal least squares (OLS) algorithm by allowing multiple L indices to be selected per iteration. Owing to its ability to catch multiple support indices in each selection, MOLS... 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
Sergios Theodoridis
University of Athens