# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 13 of 13
• ### Sketched Subspace Clustering

Publication Year: 2018, Page(s):1663 - 1675
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The immense amount of daily generated and communicated data presents unique challenges in their processing. Clustering, the grouping of data without the presence of ground-truth labels, is an important tool for drawing inferences from data. Subspace clustering (SC) is a relatively recent method that is able to successfully classify nonlinearly separable data in a multitude of settings. In spite of... View full abstract»

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

Publication Year: 2018, Page(s):1676 - 1690
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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»

• ### MISO Channel Estimation and Tracking from Received Signal Strength Feedback

Publication Year: 2018, Page(s):1691 - 1704
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Downlink channel estimation is an important task in any wireless communication system, and 5G massive multiple-input multiple-output in particular—because the receiver must estimate and feed back to the transmitter a high-dimensional multiple-input single-output (MISO) vector channel for each receiving element. This is a serious burden in terms of mobile computation and power, as well as up... View full abstract»

• ### Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces

Publication Year: 2018, Page(s):1705 - 1720
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Dimension reduction plays an essential role when decreasing the complexity of solving large-scale problems. The well-known Johnson–Lindenstrauss (JL) lemma and restricted isometry property (RIP) admit the use of random projection to reduce the dimension while keeping the Euclidean distance, which leads to the boom of compressed sensing and the field of sparsity related signal processing. Re... View full abstract»

• ### Self-Interference Cancelation Through Advanced Sampling

Publication Year: 2018, Page(s):1721 - 1733
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Self-interference  is the main obstacle to overcome in order to enable a wireless device to simultaneously transmit and receive in overlapped frequency bands. There is much interest to suppress this interference employing compact and efficient cancelers, which operate in the digital domain, extending this way the benefits of in-band full-duplex to innumerable applications. However, this is ... View full abstract»

• ### Toward Information Privacy for the Internet of Things: A Nonparametric Learning Approach

Publication Year: 2018, Page(s):1734 - 1747
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In an Internet of things network, multiple sensors send information to a fusion center for it to infer a public hypothesis of interest. However, the same sensor information may be used by the fusion center to make inferences of a private nature that the sensors wish to protect. To model this, we adopt a decentralized hypothesis testing framework with binary public and private hypotheses. Each sens... View full abstract»

• ### Error Analysis of Reconstruction From Linear Canonical Transform Based Sampling

Publication Year: 2018, Page(s):1748 - 1760
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In this paper, we consider the performance of sampling associated with the linear canonical transform (LCT), which generalizes a large number of classical integral transforms and fundamental operations linked to signal processing and optics. First, we revisit sampling approximation in the LCT domain to introduce a generalized approximation operator. Then, we derive an exact closed-form expression ... View full abstract»

• ### Beyond Massive MIMO: The Potential of Positioning With Large Intelligent Surfaces

Publication Year: 2018, Page(s):1761 - 1774
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We consider the potential for positioning with a system where antenna arrays are deployed as a large intelligent surface (LIS), which is a newly proposed concept beyond massive multi-input multi-output (MIMO). In a first step, we derive Fisher-information matrix (FIM) and Cramér–Rao lower bound (CRLB) in closed form for positioning a terminal located perpendicular to the center of th... View full abstract»

• ### Fast Block LMS and RLS-Based Parameter Estimation and Two-Dimensional Imaging in Monostatic MIMO RADAR Systems With Multiple Mobile Targets

Publication Year: 2018, Page(s):1775 - 1790
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This paper presents novel adaptive schemes and the pertinent analysis for estimation of the number of targets, their associated radar cross section (RCS) values and Doppler velocities in monostatic MIMO radar systems. These schemes are based on the fast block least mean squares (FBLMS) and fast block recursive least squares (FBRLS) algorithms considering both stationary as well as mobile targets a... View full abstract»

Publication Year: 2018, Page(s):1791 - 1801
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This paper deals with adaptive radar detection of a point-like target in a homogeneous environment characterized by the presence of clutter, jamming, and radar internal noise. At the design stage, two training datasets, whose gathering is carefully motivated in the paper, are considered to get receiver adaptation. Hence, the maximum likelihood estimator of the interference covariance matrix for th... View full abstract»

• ### Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations

Publication Year: 2018, Page(s):1802 - 1816
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This paper addresses the problem of blind demixing of instantaneous mixtures in a multiple-input multiple-output communication system. The main objective is to present efficient blind source separation (BSS) algorithms dedicated to moderate or high-order quadratic-amplitude modulation (QAM) constellations. Four new iterative batch, BSS algorithms are presented dealing with the multimodulus (MM) an... View full abstract»

• ### Atomic Norm Minimization for Modal Analysis From Random and Compressed Samples

Publication Year: 2018, Page(s):1817 - 1831
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Modal analysis is the process of estimating a system's modal parameters, such as its natural frequencies and mode shapes. One application of modal analysis is in structural health monitoring (SHM), where a network of sensors may be used to collect vibration data from a physical structure, such as a building or bridge. There is a growing interest in developing automated techniques for SHM ba... View full abstract»

• ### Low-Complexity Massive MIMO Subspace Estimation and Tracking From Low-Dimensional Projections

Publication Year: 2018, Page(s):1832 - 1844
| |PDF (1580 KB)

Massive MIMO is a variant of multiuser MIMO, in which the number of antennas $M$ at the base-station is very large. It has been observed that in many realistic propagation scenarios, although the user channel vectors have a very high-dim $M$, they lie on l... 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

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## Meet Our Editors

Editor-in-Chief
Pier Luigi Dragotti
Imperial College London