Diversity Gain for MIMO Neyman–Pearson Signal Detection
For a multiple-input multiple-output (MIMO) system adopting the Neyman-Pearson (NP) criterion, we initially derive the diversity gain for a signal-present versus signal-absent scalar hypothesis test statistic and also for a vector signal-present versus ...
Linear Precoders for the Detection of a Gaussian Process in Wireless Sensors Networks
We investigate the performance of Neyman-Pearson detection of a stationary Gaussian process in noise, using a large wireless sensor network (WSN). In our model, each sensor compresses its observation sequence using a linear precoder and a final decision ...
Limit of the Accuracy of Parameter Estimation for Moving Single Molecules Imaged by Fluorescence Microscopy
In this paper, we consider the problem of the accuracy of estimating the location and other attributes of a moving single molecule whose trajectory is imaged by fluorescence microscopy. As accuracy in parameter estimation is closely related to the ...
A Novel Power-Bearing Approach and Asymptotically Optimum Estimator for Target Motion Analysis
The problem of target motion analysis (TMA) has been extensively investigated using bearing-only (BO), Doppler-bearing (DB), and other measurement data. For radio frequency (RF) emitters, signal powers follow the well-known path loss law that can be ...
Mean-Square Error in Periodogram Approaches With Adaptive Windowing
Modified periodogram approaches are nonparametric power spectral density (PSD) estimators. Here, we present a method for estimating the mean-square error (MSE) of these PSD estimators. The proposed approach uses the observed data to estimate not only ...
Online Sparse System Identification and Signal Reconstruction Using Projections Onto Weighted \ell _{1} Balls
This paper presents a novel projection-based adaptive algorithm for sparse signal and system identification. The sequentially observed data are used to generate an equivalent sequence of closed convex sets, namely hyperslabs. Each hyperslab is the ...
Filter Bank Fusion Frames
In this paper we characterize and construct novel oversampled filter banks implementing fusion frames. A fusion frame is a sequence of orthogonal projection operators whose sum can be inverted in a numerically stable way. When properly designed, fusion ...
Extension of Wirtinger's Calculus to Reproducing Kernel Hilbert Spaces and the Complex Kernel LMS
Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. The primary mathematical tool employed in these methods is the notion of the reproducing kernel Hilbert space (RKHS). However, ...
On a Rational Transfer Function-Based Approach to {cal H}_{\infty } Filtering Design for Time-Delay Linear Systems
This paper introduces a new procedure for H∞ filter design of time-delay linear systems. A finite-order LTI system, called comparison system, is defined in such a way that its H∞ norm is proven to be strongly related to the one of the time-...
Stochastic Models for Sparse and Piecewise-Smooth Signals
We introduce an extended family of continuous-domain stochastic models for sparse, piecewise-smooth signals. These are specified as solutions of stochastic differential equations, or, equivalently, in terms of a suitable innovation model; the latter is ...
A Unique “Nonnegative” Solution to an Underdetermined System: From Vectors to Matrices
This paper investigates the uniqueness of a nonnegative vector solution and the uniqueness of a positive semidefinite matrix solution to underdetermined linear systems. A vector solution is the unique solution to an underdetermined linear system only if ...
Particle Smoothing in Continuous Time: A Fast Approach via Density Estimation
We consider the particle smoothing problem for state-space models where the transition density is not available in closed form, in particular for continuous-time, nonlinear models expressed via stochastic differential equations (SDEs). Conventional ...
A Perturbative Approach to Novelty Detection in Autoregressive Models
We propose a new method to perform novelty detection in dynamical systems governed by linear autoregressive models. The method is based on a perturbative expansion to a statistical test whose leading term is the classical F-test, and whose O(1/n) ...
Weight Adjusted Tensor Method for Blind Separation of Underdetermined Mixtures of Nonstationary Sources
In this paper, a novel algorithm to blindly separate an instantaneous linear underdetermined mixture of nonstationary sources is proposed. It means that the number of sources exceeds the number of channels of the available data. The separation is based ...
Maximum Likelihood Direction Finding in Spatially Colored Noise Fields Using Sparse Sensor Arrays
We consider the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation of narrowband signals using sparse sensor arrays, which consist of widely separated subarrays such that the unknown spatially colored noise field is uncorrelated ...
Target Tracking With Target State Dependent Detection
Target tracking algorithms usually treat the probability of detection as independent of the target state. In most cases, this assumption is not true, with subsequent degradation in the target tracking performance from both expected and optimal levels. ...
Least Squares Estimation and Cramér–Rao Type Lower Bounds for Relative Sensor Registration Process
An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors ...
Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging
Through waveform diversity, multiple-input multiple-output (MIMO) radar can provide higher resolution, improved sensitivity, and increased parameter identifiability compared to more traditional phased-array radar schemes. Existing methods for target ...
Analysis of Inverse Crosstalk Channel Estimation Using SNR Feedback
Digital subscriber line (DSL) data rates for short loops are typically limited by crosstalk between adjacent lines rather than by background noise. Precoding can reduce crosstalk in the downstream from the access node to the customer premises equipment ...
On the Energy Efficiency of LT Codes in Proactive Wireless Sensor Networks
This paper presents an in-depth analysis on the energy efficiency of Luby transform (LT) codes with frequency shift keying (FSK) modulation in a wireless sensor network (WSN) over Rayleigh fading channels with path-loss. We describe a proactive system ...
Optimal Beamforming in Interference Networks with Perfect Local Channel Information
We consider settings in which T multi-antenna transmitters and K single-antenna receivers concurrently utilize the available communication resources. Each transmitter sends useful information only to its intended receivers and can degrade the ...
Coordinated Beamforming for MISO Interference Channel: Complexity Analysis and Efficient Algorithms
In a cellular wireless system, users located at cell edges often suffer significant out-of-cell interference. Assuming each base station is equipped with multiple antennas, we can model this scenario as a multiple-input single-output (MISO) interference ...
Random Access Game in Fading Channels With Capture: Equilibria and Braess-like Paradoxes
The Nash equilibrium point of the transmission probabilities in a slotted ALOHA system with selfish nodes is analyzed. The system consists of a finite number of heterogeneous nodes, each trying to minimize its average transmission probability (or power ...
Optimal Wideband Spectrum Sensing Framework for Cognitive Radio Systems
First Page of the Article
Robust MIMO Cognitive Radio Via Game Theory
Cognitive radio (CR) systems improve the spectral efficiency by allowing the coexistence in harmony of primary users (PUs), the legacy users, with secondary users (SUs). This coexistence is built on the premises that no SU can generate interference ...
QoS-Based Transmit Beamforming in the Presence of Eavesdroppers: An Optimized Artificial-Noise-Aided Approach
Secure transmission techniques have been receiving growing attention in recent years, as a viable, powerful alternative to blocking eavesdropping attempts in an open wireless medium. This paper proposes a secret transmit beamforming approach using a ...
Exploiting Long-Term Channel Correlation in Limited Feedback SDMA Through Channel Phase Codebook
Improving channel information quality at the base station (BS) is crucial for the optimization of frequency division duplexed (FDD) multi-antenna multiuser downlink systems with limited feedback. To this end, this paper proposes to estimate a particular ...
Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributed Stochastic Learning
In this paper, we propose a two-timescale delay-optimal dynamic clustering and power allocation design for downlink network MIMO systems. The dynamic clustering control is adaptive to the global queue state information (GQSI) only and computed at the ...
Closed-Form Error Exponent for the Neyman–Pearson Fusion of Dependent Local Decisions in a One-Dimensional Sensor Network
We consider a distributed detection system formed by a large number of local detectors and a data fusion center that performs a Neyman-Pearson fusion of the binary quantizations of the sensor observations. In the analyzed two-stage detection system the ...
Algorithms and Bounds for Distributed TDOA-Based Positioning Using OFDM Signals
One main drawback of using time difference of arrival (TDOA) methods for source localization and navigation is that they require centralization of multiple copies of a signal. This paper considers blindly estimating the location of a cyclic prefix (CP) ...