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# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 22 of 22
• ### A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning

Publication Year: 2017, Page(s):4702 - 4716
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Unlike the time-division duplexing systems, the downlink (DL) and uplink (UL) channels are not reciprocal in the case of frequency-division duplexing (FDD). However, some long-term parameters, e.g., the time delays and angles of arrival of the channel paths, enjoy reciprocity. In this paper, by efficiently exploiting the aforementioned limited reciprocity, we address the DL channel state informati... View full abstract»

• ### Rank-One NMF-Based Initialization for NMF and Relative Error Bounds Under a Geometric Assumption

Publication Year: 2017, Page(s):4717 - 4731
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We propose a geometric assumption on nonnegative data matrices such that under this assumption, we are able to provide upper bounds (both deterministic and probabilistic) on the relative error of nonnegative matrix factorization (NMF). The algorithm we propose first uses the geometric assumption to obtain an exact clustering of the columns of the data matrix; subsequently, it employs several rank-... View full abstract»

• ### Downlink Training Sequence Design for FDD Multiuser Massive MIMO Systems

Publication Year: 2017, Page(s):4732 - 4744
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We consider the problem of downlink training sequence design for frequency-division-duplex multiuser massive multiple-input multiple-output systems in the general case where users have distinct spatial correlations. The training sequences leverage spatial correlations and are designed to minimize the channel estimation weighted sum mean square error (MSE) under the assumption that users employ min... View full abstract»

• ### Low Complexity Moving Target Parameter Estimation for MIMO Radar Using 2D-FFT

Publication Year: 2017, Page(s):4745 - 4755
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In multiple-input multiple-output radar, to localize a target and estimate its reflection coefficient, a given cost function is usually optimized over a grid of points. The performance of such algorithms is directly affected by the grid resolution. Increasing the number of grid points enhances the resolution of the estimator but also increases its computational complexity exponentially. In this pa... View full abstract»

• ### Quadratic Optimization With Similarity Constraint for Unimodular Sequence Synthesis

Publication Year: 2017, Page(s):4756 - 4769
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This paper considers unimodular sequence synthesis under similarity constraint for both the continuous and discrete phase cases. A computationally efficient iterative algorithm for the continuous phase case (IA-CPC) is proposed to sequentially optimize the quadratic objective function. The quadratic optimization problem is turned into multiple one-dimensional optimization problems with closed-form... View full abstract»

• ### A Stochastic Majorize-Minimize Subspace Algorithm for Online Penalized Least Squares Estimation

Publication Year: 2017, Page(s):4770 - 4783
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Stochastic approximation techniques play an important role in solving many problems encountered in machine learning or adaptive signal processing. In these contexts, the statistics of the data are often unknown a priori or their direct computation is too intensive, and they have thus to be estimated online from the observed signals. For batch optimization of an objective function... View full abstract»

• ### Boosted KZ and LLL Algorithms

Publication Year: 2017, Page(s):4784 - 4796
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There exist two issues among popular lattice reduction algorithms that should cause our concern. The first one is Korkine–Zolotarev (KZ) and Lenstra–Lenstra–Lovász (LLL) algorithms may increase the lengths of basis vectors. The other is KZ reduction suffers worse performance than Minkowski reduction in terms of providing short basis vectors, despite its superior theoret... View full abstract»

• ### A Sampling Theorem for Fractional Wavelet Transform With Error Estimates

Publication Year: 2017, Page(s):4797 - 4811
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As a generalization of the ordinary wavelet transform, the fractional wavelet transform (FRWT) is a very promising tool for signal analysis and processing. Many of its fundamental properties are already known; however, little attention has been paid to its sampling theory. In this paper, we first introduce the concept of multiresolution analysis associated with the FRWT, and then propose a samplin... View full abstract»

• ### Adaptive Detection of a Subspace Signal in Signal-Dependent Interference

Publication Year: 2017, Page(s):4812 - 4820
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This paper deals with the problem of adaptive detection of subspace signals embedded in thermal noise and clutter that depends on the transmitted signal. To this end, at the design stage, we assume that the signal-dependent (SD) clutter shares the same subspace as the target signals. As customary, a set of secondary data, free of signal components, is also assumed available. Two adaptive detectors... View full abstract»

• ### Bridging Mixture Model Estimation and Information Bounds Using I-MMSE

Publication Year: 2017, Page(s):4821 - 4832
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We derive bounds on mutual information for arbitrary estimation problems in additive noise, modeled using Gaussian mixtures. Previous work exploiting the I-minimum-mean-squared-error (MMSE) formula to formulate a bridge between bounds on the MMSE for Gaussian mixture model estimation problems and bounds on the mutual information are generalized to allow arbitrary noise modeling. A novel upper boun... View full abstract»

• ### Bipartite Graph Filter Banks: Polyphase Analysis and Generalization

Publication Year: 2017, Page(s):4833 - 4846
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The work by Narang and Ortega [“Perfect reconstruction two-channel wavelet filter banks for graph structured data,” IEEE Trans. Signal Process., vol. 60, no. 6, pp. 2786–2799, Jun. 2012], [“Compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs,” IEEE Trans. Signal Process., vol. 61,... View full abstract»

• ### Competitive Robust Estimation for Uncertain Linear Dynamic Models

Publication Year: 2017, Page(s):4847 - 4861
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In this paper, two types of robust linear estimation problems for dynamic channel model uncertainty are considered with the aim of characterizing (in computationally effective ways) competitive robust estimators, i.e., robust estimators that improve on the pointwise performance of minimax MSE (mean-squared error) estimators over the uncertain model set, at the expense of a moderate increase in the... View full abstract»

• ### Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks

Publication Year: 2017, Page(s):4862 - 4873
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We consider the problem of decentralized hypothesis testing in a network of energy harvesting sensors, where sensors make noisy observations of a phenomenon and send quantized information about the phenomenon towards a fusion center. The fusion center makes a decision about the present hypothesis using the aggregate received data during a time interval. We explicitly consider a scenario under whic... View full abstract»

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

Publication Year: 2017, Page(s):4874 - 4886
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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»

• ### Generalized Quadratic Matrix Programming: A Unified Framework for Linear Precoding With Arbitrary Input Distributions

Publication Year: 2017, Page(s):4887 - 4901
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This paper investigates a new class of nonconvex optimization, which provides a unified framework for linear precoding in single/multiuser multiple-input multiple-output channels with arbitrary input distributions. The new optimization is called generalized quadratic matrix programming (GQMP). Due to the nondeterministic polynomial time hardness of GQMP problems, instead of seeking globally optima... View full abstract»

• ### Joint Channel and Clipping Level Estimation for OFDM in IoT-based Networks

Publication Year: 2017, Page(s):4902 - 4911
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We consider scenarios such as IoT-based 5G or IoT-based machine type communication, where a low-cost low-power transmitter communicates with a high-quality receiver. Then, digital predistortion of the nonlinear power amplifier may be too expensive. In order to investigate the feasibility of receiver-side compensation of the transmitter RF impairments, we study joint maximum-likelihood estimation o... View full abstract»

• ### Constant Modulus Waveform Design for MIMO Radar Transmit Beampattern

Publication Year: 2017, Page(s):4912 - 4923
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A multiple-input multiple-output radar has great flexibility to design the transmit beampattern via selecting the probing waveform. The idea of current transmit beampattern design is to approximate the disired transimit beampattern and minimize the cross-correlation sidelobes. In this paper, under the constant modulus constraint, two algorithms are proposed to design the probing waveform directly.... View full abstract»

• ### Event-Based Estimation With Information-Based Triggering and Adaptive Update

Publication Year: 2017, Page(s):4924 - 4939
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This paper is motivated by recent advancements of cyber-physical systems and significance of managing limited communication resources in their applications. We propose an open-loop estimation strategy with an information-based triggering mechanism coupled with an adaptive event-based fusion framework. In the open-loop topology considered in this paper, a sensor transfers its measurements to a remo... View full abstract»

• ### Matrix Characterization for GFDM: Low Complexity MMSE Receivers and Optimal Filters

Publication Year: 2017, Page(s):4940 - 4955
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In this paper, a new matrix-based characterization of generalized-frequency-division-multiplexing (GFDM) transmitter matrices is proposed, as opposed to traditional vector-based characterization with prototype filters. The characterization facilitates deriving properties of GFDM (transmitter) matrices, including conditions for GFDM matrices being nonsingular and unitary, respectively. Using the ne... View full abstract»

• ### Multiple Conversions of Measurements for Nonlinear Estimation

Publication Year: 2017, Page(s):4956 - 4970
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A multiple conversion approach (MCA) to nonlinear estimation is proposed in this paper. It jointly considers multiple hypotheses on the joint distribution of the quantity to be estimated and its measurement. The overall MCA estimate is a probabilistically weighted sum of the hypothesis conditional estimates. To describe the hypothesized joint distributions used to match the truth, a general distri... View full abstract»

• ### Corrections to “On Decentralized Estimation With Active Queries”

Publication Year: 2017, Page(s):4971 - 4972
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We provide a counterexample to a key lemma used in the proofs of the convergence of decentralized estimation algorithms in [2]. We also provide an alternative lemma that establishes a new proof of the convergence results in the paper [2]. View full abstract»

• ### Corrections to “Asymptotic Achievability of the Cramér–Rao Bound for Noisy Compressive Sampling”

Publication Year: 2017, Page(s):4973 - 4974
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Given $N$ noisy measurements denoted by ${\mathbf y}$ and an overcomplete Gaussian dictionary, ${\mathbf A}$, the authors of [1] establish the exis... 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
Sergios Theodoridis
University of Athens