Showing papers on "Signal-to-noise ratio published in 2012"

Split-spectrum amplitude-decorrelation angiography with optical coherence tomography

Abstract: Amplitude decorrelation measurement is sensitive to transverse flow and immune to phase noise in comparison to Doppler and other phase-based approaches. However, the high axial resolution of OCT makes it very sensitive to the pulsatile bulk motion noise in the axial direction. To overcome this limitation, we developed split-spectrum amplitude-decorrelation angiography (SSADA) to improve the signal-to-noise ratio (SNR) of flow detection. The full OCT spectrum was split into several narrower bands. Inter-B-scan decorrelation was computed using the spectral bands separately and then averaged. The SSADA algorithm was tested on in vivo images of the human macula and optic nerve head. It significantly improved both SNR for flow detection and connectivity of microvascular network when compared to other amplitude-decorrelation algorithms.

Large System Analysis of Linear Precoding in Correlated MISO Broadcast Channels Under Limited Feedback

Abstract: In this paper, we study the sum rate performance of zero-forcing (ZF) and regularized ZF (RZF) precoding in large MISO broadcast systems under the assumptions of imperfect channel state information at the transmitter and per-user channel transmit correlation. Our analysis assumes that the number of transmit antennas M and the number of single-antenna users K are large while their ratio remains bounded. We derive deterministic approximations of the empirical signal-to-interference plus noise ratio (SINR) at the receivers, which are tight as M, K → ∞. In the course of this derivation, the per-user channel correlation model requires the development of a novel deterministic equivalent of the empirical Stieltjes transform of large dimensional random matrices with generalized variance profile. The deterministic SINR approximations enable us to solve various practical optimization problems. Under sum rate maximization, we derive 1) for RZF the optimal regularization parameter; 2) for ZF the optimal number of users; 3) for ZF and RZF the optimal power allocation scheme; and 4) the optimal amount of feedback in large FDD/TDD multiuser systems. Numerical simulations suggest that the deterministic approximations are accurate even for small M, K.

Error Vector Magnitude as a Performance Measure for Advanced Modulation Formats

Abstract: We examine the relation between optical signal-to-noise ratio (OSNR), error vector magnitude (EVM), and bit-error ratio (BER). Theoretical results and numerical simulations are compared to measured values of OSNR, EVM, and BER. We conclude that the EVM is an appropriate metric for optical channels limited by additive white Gaussian noise. Results are supported by experiments with six modulation formats at symbol rates of 20 and 25 GBd generated by a software-defined transmitter.

Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay

Abstract: Recently, it has been proposed to estimate the noise power spectral density by means of minimum mean-square error (MMSE) optimal estimation. We show that the resulting estimator can be interpreted as a voice activity detector (VAD)-based noise power estimator, where the noise power is updated only when speech absence is signaled, compensated with a required bias compensation. We show that the bias compensation is unnecessary when we replace the VAD by a soft speech presence probability (SPP) with fixed priors. Choosing fixed priors also has the benefit of decoupling the noise power estimator from subsequent steps in a speech enhancement framework, such as the estimation of the speech power and the estimation of the clean speech. We show that the proposed speech presence probability (SPP) approach maintains the quick noise tracking performance of the bias compensated minimum mean-square error (MMSE)-based approach while exhibiting less overestimation of the spectral noise power and an even lower computational complexity.

Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction and Steering Vector Estimation

Abstract: Adaptive beamformers are sensitive to model mismatch, especially when the desired signal is present in training snapshots or when the training is done using data samples. In contrast to previous works, this correspondence attempts to reconstruct the interference-plus-noise covariance matrix instead of searching for the optimal diagonal loading factor for the sample covariance matrix. The estimator is based on the Capon spectral estimator integrated over a region separated from the desired signal direction. This is shown to be more robust than using the sample covariance matrix. Subsequently, the mismatch in the steering vector of the desired signal is estimated by maximizing the beamformer output power under a constraint that prevents the corrected steering vector from getting close to the interference steering vectors. The proposed adaptive beamforming algorithm does not impose a norm constraint. Therefore, it can be used even in applications where gain perturbations affect the steering vector. Simulation results demonstrate that the performance of the proposed adaptive beamformer is almost always close to the optimal value across a wide range of signal to noise and signal to interference ratios.

Molecular Communication in Fluid Media: The Additive Inverse Gaussian Noise Channel

Abstract: In this paper, we consider molecular communication, with information conveyed in the time of release of molecules. These molecules propagate to the transmitter through a fluid medium, propelled by a positive drift velocity and Brownian motion. The main contribution of this paper is the development of a theoretical foundation for such a communication system; specifically, the additive inverse Gaussian noise (AIGN) channel model. In such a channel, the information is corrupted by noise that follows an IG distribution. We show that such a channel model is appropriate for molecular communication in fluid media. Taking advantage of the available literature on the IG distribution, upper and lower bounds on channel capacity are developed, and a maximum likelihood receiver is derived. Results are presented which suggest that this channel does not have a single quality measure analogous to signal-to-noise ratio in the additive white Gaussian noise channel. It is also shown that the use of multiple molecules leads to reduced error rate in a manner akin to diversity order in wireless communications. Finally, some open problems are discussed that arise from the IG channel model.

EVD-based channel estimation in multicell multiuser MIMO systems with very large antenna arrays

Abstract: This paper considers multicell multiuser MIMO systems with very large antenna arrays at the base station. We propose an eigenvalue-decomposition-based approach to channel estimation, that estimates the channel blindly from the received data. The approach exploits the asymptotic orthogonality of the channel vectors in very large MIMO systems. We show that the channel to each user can be estimated from the covariance matrix of the received signals, up to a remaining scalar multiplicative ambiguity. A short training sequence is required to resolve this ambiguity. Furthermore, to improve the performance of our approach, we combine it with the iterative least-square with projection (ILSP) algorithm. Numerical results verify the effectiveness of our channel estimation approach.

Bias Reduction for an Explicit Solution of Source Localization Using TDOA

Abstract: This paper proposes two methods to reduce the bias of the well-known algebraic explicit solution (Chan and Ho, "A simple and efficient estimator for hyperbolic location," IEEE Trans. Signal Process., vol. 42, pp. 1905-1915, Aug. 1994) for source localization using TDOA. Bias of a source location estimate is significant when the measurement noise is large and the geolocation geometry is poor. Bias also dominates performance when multiple times of independent measurements are available such as in UWB localization or in target tracking. The paper starts by deriving the bias of the source location estimate from Chan and Ho. The bias is found to be considerably larger than that of the Maximum Likelihood Estimator. Two methods, called BiasSub and BiasRed, are developed to reduce the bias. The BiasSub method subtracts the expected bias from the solution of Chan and Ho's work, where the expected bias is approximated by the theoretical bias using the estimated source location and noisy data measurements. The BiasRed method augments the equation error formulation and imposes a constraint to improve the source location estimate. The BiasSub method requires the exact knowledge of the noise covariance matrix and BiasRed only needs the structure of it. Analysis shows that both methods reduce the bias considerably and achieve the CRLB performance for distant source when the noise is Gaussian and small. The BiasSub method can nearly eliminate the bias and the BiasRed method is able to lower the bias to the same level as the Maximum Likelihood Estimator. The BiasRed method is extended for TDOA and FDOA positioning. Simulations corroborate the performance of the proposed methods.

Continuous real-time correction and averaging for frequency comb interferometry

Abstract: Interferograms from a dual-comb spectrometer are continuously corrected and averaged in real-time. The algorithm is implemented on a field-programmable gate array (FPGA) development board. The chosen approach and the algorithm are described. Measurements with high signal-to-noise ratio, resolution and bandwidth are shown to demonstrate the accuracy of the optical referencing and the processing algorithm with 24 hours of averaging time, reaching a signal to noise ratio of 10,750,000 (>21 bits) in the interferogram and 316,000 in the spectrum at 100 MHz resolution. An interferogram where signal dominates the noise over the full delay range imposed by the 100 MHz repetition rate is reported for the first time.

Interference Alignment with Analog Channel State Feedback

Abstract: Interference alignment (IA) is a multiplexing gain optimal transmission strategy for the interference channel. While the achieved sum rate with IA is much higher than previously thought possible, the improvement comes at the cost of requiring network channel state information at the transmitters. This can be achieved by explicit feedback, a flexible yet potentially costly approach that incurs large overhead. In this paper we propose analog feedback as an alternative to limited feedback or reciprocity based alignment. We show that the full multiplexing gain observed with perfect channel knowledge is preserved by analog feedback and that the mean loss in sum rate is bounded by a constant when signal-to-noise ratio is comparable in both forward and feedback channels. When signal-to-noise ratios are not quite symmetric, a fraction of the multiplexing gain is achieved. We consider the overhead of training and feedback and use this framework to numerically optimize the system's effective throughput. We present simulation results to demonstrate the performance of IA with analog feedback, verify our theoretical analysis, and extend our conclusions on optimal training and feedback length.

Super-resolution methods in MRI: can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?

Abstract: Improving the resolution in magnetic resonance imaging comes at the cost of either lower signal-to-noise ratio, longer acquisition time or both. This study investigates whether so-called super-resolution reconstruction methods can increase the resolution in the slice selection direction and, as such, are a viable alternative to direct high-resolution acquisition in terms of the signal-to-noise ratio and acquisition time trade-offs. The performance of six super-resolution reconstruction methods and direct high-resolution acquisitions was compared with respect to these trade-offs. The methods are based on iterative back-projection, algebraic reconstruction, and regularized least squares. The algorithms were applied to low-resolution data sets within which the images were rotated relative to each other. Quantitative experiments involved a computational phantom and a physical phantom containing structures of known dimensions. To visually validate the quantitative evaluations, qualitative experiments were performed, in which images of three different subjects (a phantom, an ex vivo rat knee, and a postmortem mouse) were acquired with different magnetic resonance imaging scanners. The results show that super-resolution reconstruction can indeed improve the resolution, signal-to-noise ratio and acquisition time trade-offs compared with direct high-resolution acquisition.

Cooperative Spectrum Sensing in Multiple Antenna Based Cognitive Radio Network Using an Improved Energy Detector

Abstract: Performance of cooperative spectrum sensing with multiple antennas at each cognitive radio (CR) is discussed in this paper. The CRs utilize selection combining of the decision statistics obtained by an improved energy detector for making a binary decision of the presence or absence of a primary user (PU). The improved energy detector uses an arbitrary positive power p of amplitudes of samples of the PU's signals. The decision of each CR is orthogonally forwarded over imperfect reporting channels to a fusion center, which takes the final decision of a spectrum hole. We derive expressions of the probabilities of false alarm and missed detection of the proposed cooperative spectrum sensing scheme. By minimizing the total error rate (sum of the probability of missed detection and the probability of false alarm) we derive a closed-form solution of the optimal number of CRs required for cooperation. It is shown by simulations that by using multiple antennas at the CRs, it is possible to significantly improve reliability of spectrum sensing with extremely low interference levels to the PU at very low (much less than 0 dB) signal-to-noise ratio of the PU-CR link.

Characterization of semiconductor-laser phase noise and estimation of bit-error rate performance with low-speed offline digital coherent receivers.

Abstract: We develop a systematic method for characterizing semiconductor-laser phase noise, using a low-speed offline digital coherent receiver. The field spectrum, the FM-noise spectrum, and the phase-error variance measured with such a receiver can completely describe phase-noise characteristics of lasers under test. The sampling rate of the digital coherent receiver should be much higher than the phase-fluctuation speed. However, 1 GS/s is large enough for most of the single-mode semiconductor lasers. In addition to such phase-noise characterization, interpolating the taken data at 1.25 GS/s to form a data stream at 10 GS/s, we can predict the bit-error rate (BER) performance of multi-level modulated optical signals at 10 Gsymbol/s. The BER degradation due to the phase noise is well explained by the result of the phase-noise measurements.

Linear Transceiver Design for Interference Alignment: Complexity and Computation

Abstract: Consider a multiple input-multiple output (MIMO) interference channel where each transmitter and receiver are equipped with multiple antennas. An effective approach to practically achieving high system throughput is to deploy linear transceivers (or beamformers) that can optimally exploit the spatial characteristics of the channel. The recent work of Cadambe and Jafar (IEEE Trans. Inf. Theory, vol. 54, no. 8) suggests that optimal beamformers should maximize the total degrees of freedom and achieve interference alignment in the high signal-to-noise ratio (SNR) regime. In this paper we first consider the interference alignment problem without channel extension and prove that the problem of maximizing the total achieved degrees of freedom for a given MIMO interference channel is NP-hard. Furthermore, we show that even checking the achievability of a given tuple of degrees of freedom for all receivers is NP-hard when each receiver is equipped with at least three antennas. Interestingly, the same problem becomes polynomial time solvable when each transmit/receive node is equipped with no more than two antennas. We also propose a distributed algorithm for transmit covariance matrix design that does not require the DoF tuple preassignment, under the assumption that each receiver uses a linear minimum mean square error (MMSE) beamformer. The simulation results show that the proposed algorithm outperforms the existing interference alignment algorithms in terms of system throughput.

The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding versus Dynamic Range

Abstract: Compressive sensing (CS) exploits the sparsity present in many signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, in theory, commensurate reductions in the size, weight, power consumption, and/or monetary cost of both signal sensors and any associated communication links. This paper examines the use of CS in the design of a wideband radio receiver in a noisy environment. We formulate the problem statement for such a receiver and establish a reasonable set of requirements that a receiver should meet to be practically useful. We then evaluate the performance of a CS-based receiver in two ways: via a theoretical analysis of its expected performance, with a particular emphasis on noise and dynamic range, and via simulations that compare the CS receiver against the performance expected from a conventional implementation. On the one hand, we show that CS-based systems that aim to reduce the number of acquired measurements are somewhat sensitive to signal noise, exhibiting a 3 dB SNR loss per octave of subsampling, which parallels the classic noise-folding phenomenon. On the other hand, we demonstrate that since they sample at a lower rate, CS-based systems can potentially attain a significantly larger dynamic range. Hence, we conclude that while a CS-based system has inherent limitations that do impose some restrictions on its potential applications, it also has attributes that make it highly desirable in a number of important practical settings.

Optimum SAR/GMTI Processing and Its Application to the Radar Satellite RADARSAT-2 for Traffic Monitoring

Abstract: This paper derives an optimum processing method for ground moving-target indication (GMTI) with a multichannel synthetic aperture radar (SAR) system. This method enables efficient detection of moving objects and accurate estimation of their parameters and does not require any knowledge of the street network. The processing is applied to data acquired with the Canadian RADARSAT-2 satellite. Results of the performed trial are compared with the expected GMTI performance of the radar in order to validate the theory.

Performance of Spatial Modulation in the Presence of Channel Estimation Errors

Abstract: This work investigates the negative effects of channel estimation errors on the performance of spatial modulation (SM) when operating over flat Rayleigh fading channels. The pairwise error probability of the SM scheme is derived in the presence of channel estimation errors and an upper bound on the average bit error probability is evaluated for M-PSK and M-QAM signalling. It is shown via computer simulations that the derived upper bound becomes very tight with increasing signal-to-noise ratio (SNR) and the SM scheme is quite robust to channel estimation errors.

High-throughput biophysical measurement of human red blood cells

Abstract: This paper reports a microfluidic system for biophysical characterization of red blood cells (RBCs) at a speed of 100-150 cells s(-1). Electrical impedance measurement is made when single RBCs flow through a constriction channel that is marginally smaller than RBCs' diameters. The multiple parameters quantified as mechanical and electrical signatures of each RBC include transit time, impedance amplitude ratio, and impedance phase increase. Histograms, compiled from 84,073 adult RBCs (from 5 adult blood samples) and 82,253 neonatal RBCs (from 5 newborn blood samples), reveal different biophysical properties across samples and between the adult and neonatal RBC populations. In comparison with previously reported microfluidic devices for single RBC biophysical measurement, this system has a higher throughput, higher signal to noise ratio, and the capability of performing multi-parameter measurements.

Variance analysis of x-ray CT sinograms in the presence of electronic noise background.

Abstract: Purpose: Low-dose x-ray computed tomography (CT) is clinically desired Accurate noise modeling is a fundamental issue for low-dose CT image reconstruction via statistics-based sinogram restoration or statistical iterative image reconstruction In this paper, the authors analyzed the statistical moments of low-dose CT data in the presence of electronic noise background Methods: The authors first studied the statistical moment properties of detected signals in CT transmission domain, where the noise of detected signals is considered as quanta fluctuation upon electronic noise background Then the authors derived, via the Taylor expansion, a new formula for the mean–variance relationship of the detected signals in CT sinogram domain, wherein the image formation becomes a linear operation between the sinogram data and the unknown image, rather than a nonlinear operation in the CT transmission domain To get insight into the derived new formula by experiments, an anthropomorphic torso phantom was scanned repeatedly by a commercial CT scanner at five different mAs levels from 100 down to 17 Results: The results demonstrated that the electronic noise background is significant when low-mAs (or low-dose) scan is performed Conclusions: The influence of the electronic noise background should be considered in low-dose CT imaging

Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER

Abstract: Measuring the quality of optical signals is one of the most important tasks in optical communications. A variety of metrics are available, namely the general shape of the eye diagram, the optical signal-to-noise power ratio (OSNR), the Q-factor as a measure of the eye opening, the error vector magnitude (EVM) that is especially suited for quadrature amplitude modulation (QAM) formats, and the bit error ratio (BER). While the BER is the most conclusive quality determinant, it is sometimes difficult to quantify, especially for simulations and off-line processing. We compare various metrics analytically, by simulation, and through experiments. We further discuss BER estimates derived from OSNR, Q-factor and EVM data and compare them to measurements employing six modulation formats at symbol rates of 20 GBd and 25 GBd, which were generated by a software-defined transmitter. We conclude that for optical channels with additive Gaussian noise the EVM metric is a reliable quality measure. For nondata-aided reception, BER below 0.01 can be estimated from measured EVM.

Base Station Cooperation on the Downlink: Large System Analysis

Abstract: This paper considers maximizing the network-wide minimum supported rate in the downlink of a two-cell system, where each base station (BS) is endowed with multiple antennas. This is done for different levels of cell cooperation. At one extreme, we consider single cell processing where the BS is oblivious to the interference it is creating at the other cell. At the other extreme, we consider full cooperative macroscopic beamforming. In between, we consider coordinated beamforming, which takes account of inter-cell interference, but does not require full cooperation between the BSs. We combine elements of Lagrangian duality and large system analysis to obtain limiting SINRs and bit-rates, allowing comparison between the considered schemes. The main contributions of the paper are theorems which provide concise formulas for optimal transmit power, beamforming vectors, and achieved signal to interference and noise ratio (SINR) for the considered schemes. The formulas obtained are valid for the limit in which the number of users per cell, K, and the number of antennas per base station, N, tend to infinity, with fixed ratio β = K/N. These theorems also provide expressions for the effective bandwidths occupied by users, and the effective interference caused in the adjacent cell, which allow direct comparisons between the considered schemes.

Second-Order Cyclostationarity of Mobile WiMAX and LTE OFDM Signals and Application to Spectrum Awareness in Cognitive Radio Systems

Abstract: Spectrum sensing and awareness are challenging requirements in cognitive radio (CR). To adequately adapt to the changing radio environment, it is necessary for the CR to detect the presence and classify the on-the-air signals. The wireless industry has shown great interest in orthogonal frequency division multiplexing (OFDM) technology. Hence, classification of OFDM signals has been intensively researched recently. Generic signals have been mainly considered, and there is a need to investigate OFDM standard signals, and their specific discriminating features for classification. In this paper, realistic and comprehensive mathematical models of the OFDM-based mobile Worldwide Interoperability for Microwave Access (WiMAX) and third-Generation Partnership Project Long Term Evolution (3GPP LTE) signals are developed, and their second-order cyclostationarity is studied. Closed-from expressions for the cyclic autocorrelation function (CAF) and cycle frequencies (CFs) of both signal types are derived, based on which an algorithm is proposed for their classification. The proposed algorithm does not require carrier, waveform, and symbol timing recovery, and is immune to phase, frequency, and timing offsets. The classification performance of the algorithm is investigated versus signal-to-noise ratio (SNR), for diverse observation intervals and channel conditions. In addition, the computational complexity is explored versus the signal type. Simulation results show the efficiency of the algorithm is terms of classification performance, and the complexity study proves the real time applicability of the algorithm.

Cyclostationarity-Based Robust Algorithms for QAM Signal Identification

Abstract: This letter proposes two novel algorithms for the identification of quadrature amplitude modulation (QAM) signals. The cyclostationarity-based features used by these algorithms are robust with respect to timing, phase, and frequency offsets, and phase noise. Based on theoretical analysis and simulations, the identification performance of the proposed algorithms compares favorably with that of alternative approaches.

Mimicking Full-Duplex Relaying Using Half-Duplex Relays With Buffers

Abstract: We propose a new relaying scheme referred to as space full-duplex max-max relay selection (SFD-MMRS), which uses relay selection and half-duplex (HD) relays with buffers to mimic full-duplex (FD) relaying. SFD-MMRS allows the selection of different relays for reception and transmission, which, in turn, enables the relays selected for reception and transmission to simultaneously receive and transmit. With SFD-MMRS, the prelog factor 1/2 is removed from the capacity expression, and better performance in terms of both throughput and outage probability is achieved. We provide a comprehensive analysis of the capacity and outage probability of the proposed scheme for a decode-and-forward (DF) protocol in Rayleigh fading. This analysis reveals that the proposed scheme provides better performance, compared with HD MMRS and HD best relay selection (BRS). Moreover, our simulation results show that the capacity of the proposed scheme with HD relays exceeds twice the capacity of BRS with HD relays for any number of relays. Furthermore, the proposed scheme provides full diversity and large signal-to-noise ratio (SNR) gains, compared with competing schemes in the literature.

ECG Signal Denoising Using Wavelet Thresholding Techniques in Human Stress Assessment

Abstract: In recent years, Electrocardiogram (ECG) plays an imperative role in heart disease diagnostics, Human Computer Interface (HCI), stress and emotional states assessment, etc. In general, ECG signals affected by noises such as baseline wandering, power line interference, electromagnetic interference, and high frequency noises during data acquisition. In order to retain the ECG signal morphology, several researches have adopted using different preprocessing methods. In this work, the stroop color word test based mental stress inducement have done and ECG signals are acquired from 10 female subjects in the age range of 20 years to 25 years. We have considered the Discrete Wavelet Transform (DWT) based wavelet denoising have incorporated using different thresholding techniques to remove three major sources of noises from the acquired ECG signals namely, power line interference, baseline wandering, and high frequency noises. Three wavelet functions ("db4", "coif5" and "sym7") and four different thresholding methods are used to denoise the noise in ECG signals. The experimental result shows the significant reduction of above considered noises and it retains the ECG signal morphology effectively. Four different performance measures were considered to select the appropriate wavelet function and thresholding rule for efficient noise removal methods such as, Signal to Interference Ratio (SIR), noise power, Percentage Root Mean Square Difference (PRD) and finally periodogramof Power Spectral Density (PSD). The experimental result shows the "coif5" wavelet andrigrsurethresholding rule is optimal for unknown Signal to Noise Ratio (SNR) in the real time ECG signals.

Analysis of Dual Band Power and Data Telemetry for Biomedical Implants

Abstract: Inductive coupling is commonly used for wireless power and data transfer in biomedical telemetry systems. To increase data bandwidth while maintaining power transfer efficiency, a multiband telemetry system transmitting power and data using different frequencies has been adopted. However, the power link and data link interact with each other, complicating the operation of both power and data transmission. In this paper, we demonstrate that to achieve high performance data transmission, the cross-coupling between the power coils and data coils have to be taken into consideration. Design equations have been derived and shown that the signal to noise (interference) ratio could be significantly reduced and the resulting data transmission could fail if only the data link coupling is optimized without considering the cross-coupling between the power link and the data link. Design examples have been constructed to demonstrate that there could be more than 30 dB difference in the signal to noise ratio. The analysis has been verified with simulation and measurement results.

Theoretical Analysis of the Capture Probability in Wireless Systems with Multiple Packet Reception Capabilities

Abstract: In this paper, we address the problem of computing the probability that r out of n interfering wireless signals are "captured," i.e., received with sufficiently large Signal to Interference plus Noise Ratio (SINR) to correctly decode the signals by a receiver with multi-packet reception (MPR) and Successive Interference Cancellation (SIC) capabilities. We start by considering the simpler case of a pure MPR system without SIC, for which we provide an expression for the distribution of the number of captured packets, whose computational complexity scales with n and r. This analysis makes it possible to investigate the system throughput as a function of the MPR capabilities of the receiver. We then generalize the analysis to SIC systems. In addition to the exact expressions for the capture probability and the normalized system throughput, we also derive approximate expressions that are much easier to compute and provide accurate results in some practical scenarios. Finally, we present selected results for some case studies with the purpose of illustrating the potential of the proposed mathematical framework and validating the approximate methods.

On the Overhead of Interference Alignment: Training, Feedback, and Cooperation

Abstract: Interference alignment (IA) is a cooperative transmission strategy that, under some conditions, achieves the interference channel's maximum number of degrees of freedom. Realizing IA gains, however, is contingent upon providing transmitters with sufficiently accurate channel knowledge. In this paper, we study the performance of IA in multiple-input multiple-output systems where channel knowledge is acquired through training and analog feedback. We design the training and feedback system to maximize IA's effective sum-rate: a non-asymptotic performance metric that accounts for estimation error, training and feedback overhead, and channel selectivity. We characterize effective sum-rate with overhead in relation to various parameters such as signal-to-noise ratio, Doppler spread, and feedback channel quality. A main insight from our analysis is that, by properly designing the CSI acquisition process, IA can provide good sum-rate performance in a very wide range of fading scenarios. Another observation from our work is that such overhead-aware analysis can help solve a number of practical network design problems. To demonstrate the concept of overhead-aware network design, we consider the example problem of finding the optimal number of cooperative IA users based on signal power and mobility.

Informed RESTORE: A method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts

Abstract: Physiological noise artifacts, especially those originating from cardiac pulsation and subject motion, are common in clinical Diffusion tensor-MRI acquisitions. Previous works show that signal perturbations produced by artifacts can be severe and neglecting to account for their contribution can result in erroneous diffusion tensor values. The Robust Estimation of Tensors by Outlier Rejection (RESTORE) method has been shown to be an effective strategy for improving tensor estimation on a voxelby-voxel basis in the presence of artifactual data points in diffusion-weighted images. In this article, we address potential instabilities that may arise when using RESTORE and propose practical constraints to improve its usability. Moreover, we introduce a method, called informed RESTORE designed to remove physiological noise artifacts in datasets acquired with low redundancy (less than 30–40 diffusion-weighted image volumes)—a condition in which the original RESTORE algorithm may converge to an incorrect solution. This new method is based on the notion that physiological noise is more likely to result in signal dropouts than signal increases. Results from both Monte Carlo simulation and clinical diffusion data indicate that informed RESTORE performs very well in removing physiological noise artifacts for low redundancy diffusion-weighted image datasets. Magn Reson Med 68:1654–1663, 2012. V C 2012