# IEEE Transactions on Signal Processing

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

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

Publication Year: 2017, Page(s):3551 - 3582
Cited by:  Papers (4)
| | PDF (1166 KB) | HTML Media

Tensors or multiway arrays are functions of three or more indices (i, j, k, . . . )-similar to matrices (two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a rich history, stretching over almost a century, and touching upon numerous disciplines; but they have only recently become ubiquitous in signal and data analytics at the confluence of signal processing,... View full abstract»

• ### Variational Mode Decomposition

Publication Year: 2014, Page(s):531 - 544
Cited by:  Papers (222)
| | 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»

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

Publication Year: 2002, Page(s):174 - 188
Cited by:  Papers (5062)  |  Patents (113)
| | 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»

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

Publication Year: 2006, Page(s):4311 - 4322
Cited by:  Papers (3251)  |  Patents (35)
| | 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»

• ### Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

Publication Year: 2015, Page(s):6169 - 6183
Cited by:  Papers (51)
| | PDF (3184 KB) | HTML

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot des... View full abstract»

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

Publication Year: 2005, Page(s):3010 - 3022
Cited by:  Papers (847)  |  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»

• ### Superfast Line Spectral Estimation

Publication Year: 2018, Page(s):2511 - 2526
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A number of recent works have proposed to solve the line spectral estimation problem by applying off-the-grid extensions of sparse estimation techniques. These methods are preferable over classical line spectral estimation algorithms because they inherently estimate the model order. However, they all have computation times that grow at least cubically in the problem size, thus limiting their pract... View full abstract»

• ### Bayesian Compressive Sensing

Publication Year: 2008, Page(s):2346 - 2356
Cited by:  Papers (849)  |  Patents (5)
| | 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»

• ### Fast Low-Rank Bayesian Matrix Completion With Hierarchical Gaussian Prior Models

Publication Year: 2018, Page(s):2804 - 2817
| | PDF (1810 KB) | HTML

The problem of low-rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a Wishart distribution is specified as a hyperprior over the precision matrix. We s... 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 (464)  |  Patents (27)
| | 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»

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

Publication Year: 2004, Page(s):461 - 471
Cited by:  Papers (1711)  |  Patents (83)
| | 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»

• ### Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

Publication Year: 2018, Page(s):2557 - 2571
| | PDF (1038 KB) | HTML

In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with 1-bit output resolution and an unknown quantization threshold is considered. Single-comparator ADCs are energy-efficient and can be operated at ultrahigh sampling rates. For analysis of such systems, a fixed and known quantization threshold is usually assum... View full abstract»

• ### Matching pursuits with time-frequency dictionaries

Publication Year: 1993, Page(s):3397 - 3415
Cited by:  Papers (4109)  |  Patents (128)
| | 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»

• ### Efficient Compressive Channel Estimation for Millimeter-Wave Large-Scale Antenna Systems

Publication Year: 2018, Page(s):2414 - 2428
| | PDF (1362 KB) | HTML

Large-scale antenna systems are considered as a viable technology to compensate for huge path loss in millimeter-wave (mmWave) communications. However, due to the massive antennas, the channel state information (CSI) acquisition is costly and challenging. In this paper, we develop a novel compressive channel estimation framework based on multiple measurement vectors (MMV). Compared with convention... View full abstract»

• ### Empirical Wavelet Transform

Publication Year: 2013, Page(s):3999 - 4010
Cited by:  Papers (140)  |  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»

• ### Sampling signals with finite rate of innovation

Publication Year: 2002, Page(s):1417 - 1428
Cited by:  Papers (491)  |  Patents (35)
| | PDF (488 KB) | HTML

The authors consider classes of signals that have a finite number of degrees of freedom per unit of time and call this number the rate of innovation. Examples of signals with a finite rate of innovation include streams of Diracs (e.g., the Poisson process), nonuniform splines, and piecewise polynomials. Even though these signals are not bandlimited, we show that they can be sampled uniformly at (o... View full abstract»

• ### Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems

Publication Year: 2018, Page(s):1676 - 1690
| | PDF (1232 KB) | HTML

Solving inverse problems with iterative algorithms is popular, especially for large data. Due to time constraints, the number of possible iterations is usually limited, potentially affecting the achievable accuracy. Given an error one is willing to tolerate, an important question is whether it is possible to modify the original iterations to obtain faster convergence to a minimizer achieving the a... View full abstract»

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

Publication Year: 2002, Page(s):425 - 437
Cited by:  Papers (752)  |  Patents (20)
| | 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»

• ### Massive MIMO Antenna Selection: Switching Architectures, Capacity Bounds, and Optimal Antenna Selection Algorithms

Publication Year: 2018, Page(s):1346 - 1360
| | PDF (945 KB) | HTML

Antenna selection is a multiple-input multiple-output (MIMO) technology, which uses radio frequency (RF) switches to select a good subset of antennas. Antenna selection can alleviate the requirement on the number of RF transceivers, thus being attractive for massive MIMO systems. In massive MIMO antenna selection systems, RF switching architectures need to be carefully considered. In this paper, w... View full abstract»

• ### Weakly Supervised Dictionary Learning

Publication Year: 2018, Page(s):2527 - 2541
| | PDF (2353 KB) | HTML

We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly lear... View full abstract»

• ### SILVar: Single Index Latent Variable Models

Publication Year: 2018, Page(s):2790 - 2803
| | PDF (10053 KB) | HTML

A semiparametric, nonlinear regression model in the presence of latent variables is introduced. These latent variables can correspond to unmodeled phenomena or unmeasured agents in a complex networked system. This new formulation allows joint estimation of certain nonlinearities in the system, the direct interactions between measured variables and the effects of unmodeled elements on the observed ... 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 (384)  |  Patents (2)
| | 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»

Publication Year: 2018, Page(s):2011 - 2026
| | PDF (1606 KB) | HTML

This paper investigates the relay hybrid precoding design in millimeter-wave massive multiple-input multiple-output systems. The optimal design of the relay hybrid precoding is highly nonconvex, due to the six-order polynomial objective function, six-order polynomial constraint, and constant-modulus constraints. To efficiently solve this challenging nonconvex problem, we first reformulate it into ... View full abstract»

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

Publication Year: 2015, Page(s):5165 - 5179
Cited by:  Papers (41)  |  Patents (1)
| | 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»

• ### Beam Design and User Scheduling for Nonorthogonal Multiple Access With Multiple Antennas Based on Pareto Optimality

Publication Year: 2018, Page(s):2876 - 2891
| | PDF (922 KB) | HTML

In this paper, the problem of transmit beam design and user scheduling is investigated for multiuser (MU) multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) downlink. First, Pareto-optimal beam design is solved for two-user MISO broadcast channels (BCs) with successive interference cancelation (SIC), and certain properties of Pareto-optimal beam vectors are obtained. Based on... View full abstract»

• ### Low-Rank Matrix Recovery From Noisy, Quantized, and Erroneous Measurements

Publication Year: 2018, Page(s):2918 - 2932
| | PDF (1370 KB) | HTML

This paper proposes a communication-reduced, cyber-resilient, and information-preserved data collection framework. Random noise and quantization are applied to the measurements before transmission to compress data and enhance data privacy. Leveraging the low-rank property of the data, we develop novel methods to recover the original data from quantized measurements even when partial measurements a... View full abstract»

• ### Correntropy: Properties and Applications in Non-Gaussian Signal Processing

Publication Year: 2007, Page(s):5286 - 5298
Cited by:  Papers (331)
| | PDF (682 KB) | HTML

The optimality of second-order statistics depends heavily on the assumption of Gaussianity. In this paper, we elucidate further the probabilistic and geometric meaning of the recently defined correntropy function as a localized similarity measure. A close relationship between correntropy and M-estimation is established. Connections and differences between correntropy and kernel methods are present... View full abstract»

• ### Fractional Programming for Communication Systems—Part I: Power Control and Beamforming

Publication Year: 2018, Page(s):2616 - 2630
| | PDF (807 KB) | HTML

Fractional programming (FP) refers to a family of optimization problems that involve ratio term(s). This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transform technique for tackling the multiple-ratio concave-convex ... View full abstract»

• ### Grid Evolution Method for DOA Estimation

Publication Year: 2018, Page(s):2374 - 2383
| | PDF (767 KB) | HTML

Off-grid direction of arrival (OGDOA) estimation methods deal with the situations where true direction of arrivals (DOAs) are not on the discretized sampling grid. However, existing OGDOA estimation methods are faced with a tradeoff between density of initial grid and computational workload. Furthermore, these methods fail if more than one true DOA is located in a same grid interval. In order to s... View full abstract»

• ### Discrete Signal Processing on Graphs

Publication Year: 2013, Page(s):1644 - 1656
Cited by:  Papers (223)
| | 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»

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

Publication Year: 2008, Page(s):1616 - 1626
Cited by:  Papers (128)  |  Patents (7)
| | 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»

• ### Massive Connectivity With Massive MIMO—Part I: Device Activity Detection and Channel Estimation

Publication Year: 2018, Page(s):2933 - 2946
| | PDF (693 KB) | HTML

This two-part paper considers an uplink massive device communication scenario in which a large number of devices are connected to a base station (BS), but user traffic is sporadic so that in any given coherence interval, only a subset of users is active. The objective is to quantify the cost of active user detection and channel estimation and to characterize the overall achievable rate of a grant-... View full abstract»

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

Publication Year: 2017, Page(s):794 - 816
Cited by:  Papers (6)
| | 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»

Publication Year: 2018, Page(s):2441 - 2454
| | PDF (1142 KB)

In this paper, we study the feasibility of an opportunistic radar, which exploits the probing signals transmitted during the sector level sweep of the IEEE 802.11ad beamforming training protocol. Several solutions are presented to detect the presence of prospective obstacles and estimate their position, radial velocity, and backscattered signal amplitude, which differ in the amount of prior inform... View full abstract»

• ### Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems

Publication Year: 2017, Page(s):4075 - 4089
Cited by:  Papers (4)
| | PDF (1223 KB) | HTML

This paper considers channel estimation and system performance for the uplink of a single-cell massive multiple-input multiple-output system. Each receiver antenna of the base station is assumed to be equipped with a pair of one-bit analog-to-digital converters to quantize the real and imaginary part of the received signal. We first propose an approach for channel estimation that is applicable for... View full abstract»

• ### L1-Norm Principal-Component Analysis of Complex Data

Publication Year: 2018, Page(s):3256 - 3267
| | PDF (981 KB) | HTML

L1-norm Principal-Component Analysis (L1-PCA) of real-valued data has attracted significant research interest over the past decade. L1-PCA of complex-valued data remains to date unexplored despite the many possible applications (in communication systems, for example). In this paper, we establish theoretical and algorithmic foundations of L1-PCA of complex-valued data matrices. Specifically, we fir... View full abstract»

• ### Sparse Bayesian learning for basis selection

Publication Year: 2004, Page(s):2153 - 2164
Cited by:  Papers (419)
| | PDF (328 KB) | HTML

Sparse Bayesian learning (SBL) and specifically relevance vector machines have received much attention in the machine learning literature as a means of achieving parsimonious representations in the context of regression and classification. The methodology relies on a parameterized prior that encourages models with few nonzero weights. In this paper, we adapt SBL to the signal processing problem of... View full abstract»

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

Publication Year: 2010, Page(s):1875 - 1888
Cited by:  Papers (675)
| | 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»

• ### Joint Beamforming and Power-Splitting Control in Downlink Cooperative SWIPT NOMA Systems

Publication Year: 2017, Page(s):4874 - 4886
Cited by:  Papers (2)
| | PDF (1140 KB) | HTML

This paper investigates the application of simultaneous wireless information and power transfer (SWIPT) to cooperative non-orthogonal multiple access (NOMA). A new cooperative multiple-input single-output (MISO) SWIPT NOMA protocol is proposed, where a user with a strong channel condition acts as an energy-harvesting (EH) relay by adopting power splitting (PS) scheme to help a user with a poor cha... View full abstract»

• ### Space Time MUSIC: Consistent Signal Subspace Estimation for Wideband Sensor Arrays

Publication Year: 2018, Page(s):2685 - 2699
| | PDF (1147 KB) | HTML

Wideband direction of arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical, and multimedia applications. Many state-of-the-art wideband DOA estimators coherently process frequency binned array outputs by approximate maximum likelihood (ML), weighted subspace fitting, or focusing techniques. This paper shows that bin signals obtained by filter-bank... View full abstract»

• ### FDD Massive MIMO Channel Estimation With Arbitrary 2D-Array Geometry

Publication Year: 2018, Page(s):2584 - 2599
| | PDF (1125 KB) | HTML

This paper addresses the problem of downlink channel estimation in frequency-division duplexing massive multiple-input multiple-output systems. The existing methods usually exploit hidden sparsity under a discrete Fourier transform (DFT) basis to estimate the downlink channel. However, there are at least two shortcomings of these DFT-based methods: first, they are applicable to uniform linear arra... View full abstract»

• ### Sparse Reconstruction by Separable Approximation

Publication Year: 2009, Page(s):2479 - 2493
Cited by:  Papers (636)  |  Patents (3)
| | PDF (763 KB) | HTML

Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing (CS) are a few well-known areas in which problems of this type appear. One standard approach is to m... View full abstract»

• ### Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping Spectrum Estimation With Missing Observations

Publication Year: 2018, Page(s):2153 - 2166
| | PDF (1041 KB) | HTML

In this paper, we address the problem of spectrum estimation of multiple frequency-hopping (FH) signals in the presence of random missing observations. The signals are analyzed within the bilinear time-frequency (TF) representation framework, where a TF kernel is designed by exploiting the inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts... View full abstract»

• ### Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting

Publication Year: 2018, Page(s):2245 - 2257
| | PDF (2733 KB) | HTML

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks. We consider the case where two types of sensors are deployed: the first consists of expensive, high quality sensors; and the second, of cheap low quality sensors, which are activated only if the intensity of the spatial field exceeds a pre-defined activation threshold (e.g... View full abstract»

• ### A Noise Resistant Correlation Method for Period Detection of Noisy Signals

Publication Year: 2018, Page(s):2700 - 2710
| | PDF (1784 KB) | HTML Media

This paper develops a novel method called the noise resistant correlation method for detecting the hidden period from the contaminated (noisy) signals with strong white Gaussian noise. A novel correlation function is proposed based on a newly constructed periodic signal and the contaminated signal to effectively detect the target hidden period. In contrast with the conventional autocorrelation ana... View full abstract»

• ### Estimating Time-Evolving Partial Coherence Between Signals via Multivariate Locally Stationary Wavelet Processes

Publication Year: 2014, Page(s):5240 - 5250
Cited by:  Papers (6)
| | PDF (2213 KB) | HTML

We consider the problem of estimating time-localized cross-dependence in a collection of nonstationary signals. To this end, we develop the multivariate locally stationary wavelet framework, which provides a time-scale decomposition of the signals and, thus, naturally captures the time-evolving scale-specific cross-dependence between components of the signals. Under the proposed model, we rigorous... 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 (394)  |  Patents (3)
| | 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»

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

Publication Year: 2011, Page(s):4053 - 4085
Cited by:  Papers (384)  |  Patents (10)
| | 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»

• ### System Architecture and Signal Processing for Frequency-Modulated Continuous-Wave Radar Using Active Backscatter Tags

Publication Year: 2018, Page(s):2258 - 2272
| | PDF (3931 KB)

In this paper, we study a frequency-modulated continuous-wave based radio-frequency identification system, which is composed of a reader with single transmitter and multiple receiving antennas as well as multiple active backscatter tags. In particular, our contribution is twofold in this paper. First, we construct a general architecture for achieving the functionality of wireless positioning based... View full abstract»

• ### Learning the MMSE Channel Estimator

Publication Year: 2018, Page(s):2905 - 2917
| | PDF (1074 KB) | HTML

We present a method for estimating conditionally Gaussian random vectors with random covariance matrices, which uses techniques from the field of machine learning. Such models are typical in communication systems, where the covariance matrix of the channel vector depends on random parameters, e.g., angles of propagation paths. If the covariance matrices exhibit certain Toeplitz and shift-invarianc... 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
Pier Luigi Dragotti
Imperial College London