Sheetal Kalyani

Decision directed channel tracking (DDCT) in OFDM systems can suffer from error propagation at high fade rates, due to the combined effect of rapid variation of the channel, long frame length and frequency selectivity of the channel. Conventional estimators like the 2D-minimum mean square error (MMSE) channel estimator and the expectation maximization (EM) based Kalman channel estimator [3] show poor performance when they are applied to DDCT over large frame lengths, due to the error propagation induced by wrong symbol decisions. The poor symbols decisions usually act like leverage points in the regression matrix, and can be identified using the hat matrix as a leverage diagnostic. We use extreme value theory (EVT) on the hat matrix to define a channel estimator, which, in addition to exploiting time and frequency correlation of the channel, downweighs leverage points before utilizing them in the estimator structure. The proposed EVT-leverage weighted (LW) estimator reduces error propagation in the frame since it downweighs possible wrong decisions before using them in the channel estimator structure. The proposed EVT-LW estimator has a significantly better error rate performance when compared to both the 2D-MMSE estimator [2] and the EM based Kalman estimator [3]. © 2006 IEEE.

New expressions for symbol error probability (SEP) in the presence of peak-to-average-power-ratio/clipping are derived from the orthogonal frequency division multiplexing systems. These expressions are significantly tighter than the expression in the existing literature, since we model the peak behavior using a peak over threshold approach. An adaptive back-off scheme is also proposed and its SEP is derived. It is shown that with negligible SEP degradation, the adaptive back-off scheme saves power.

Delamination in composite structures is characterized by a resonant cavity wherein a fraction of an ultrasonic guided wave may be trapped. Based on this wave trapping phenomenon, we propose a baseline-free statistical approach for the identification and localization of delamination using sparse sampling and density-based spatial clustering of applications with noise (DBSCAN) technique. The proposed technique can be deployed for rapid inspection with minimal human intervention. The Performance of the proposed technique in terms of its ability to determine the precise location of such defects is quantified through the probability of detection measurements. The robustness of the proposed technique is tested through extensive simulations consisting of different random locations of defects on flat plate structures with different sizes and orientation as well as different values of signal to noise ratio of the simulated data. The simulation results are also validated using experimental data and the results are found to be in good agreement.

The κ-μ shadowed fading model is a very general fading model as it includes both κ-μ and η-μ as special cases. In this paper, we derive the expression for outage probability when the signal-of-interest (SoI) and interferers both experience κ-μ shadowed fading in an interference limited scenario. The derived expression is valid for arbitrary SoI parameters, arbitrary κ, and μ parameters for all interferers and any value of the parameter m for the interferers excepting the limiting value of m → ∞. The expression can be expressed in terms of Pochhammer integral, where the integrands of integral only contains elementary functions. The outage probability expression is then simplified for various special cases, especially when SoI experiences η-μ or κ-μ fading. Furthermore, the rate expression is derived when the SoI experiences κ-μ shadowed fading with the integer values of μ, and the interferers experience κ-μ shadowed fading with arbitrary parameters. The rate expression can be expressed in terms of sum of Lauricella's function of the fourth kind. The utility of our results is demonstrated by using the derived expression to study and compare fractional frequency reuse and soft frequency reuse in the presence of κ-μ shadowed fading. Extensive simulation results are provided and these further validate our theoretical results.

Device-to-device (D2D) communication underlying cellular networks, allows direct transmission between two devices in each other's proximity that reuse the cellular resource blocks in an effort to increase the network capacity and spectrum efficiency. However, this imposes severe interference that degrades the system's performance. This problem may be circumvented by incorporating fractional frequency reuse (FFR) or soft frequency reuse (SFR) in OFDMA cellular networks. By carefully considering the downlink resource reuse of the D2D links, we propose beneficial frequency allocation schemes, when the macrocell has employed FFR or SFR as its frequency reuse technique. The performance of these schemes is quantified using both the analytical and simulation results for characterizing both the coverage probability and the capacity of D2D links under the proposed schemes that are benchmarked against the radical unity frequency reuse scheme. The impact of the D2D links on the coverage probability of macrocellular users (CUs) is also quantified, revealing that the CUs performance is only modestly affected under the proposed frequency allocation schemes. Finally, we provide insights concerning the power control design in order to strike a beneficial tradeoff between the energy consumption and the performance of D2D links.

Analytical evaluation of outage probability (OP) and capacity for κ-μ and η-μ fading channel distributions, which model line-of-sight and non line-of-sight propagation effects, respectively, has been in focus lately. We derive an approximate closed form expression for OP and a simple expression for capacity in terms of hypergeometric functions, when the user experiences κ-μ fading and interferers experience η-μ fading with arbitrary parameters. Simulation results are presented along with analytical evaluation results, which prove that the approximations made for analytical evaluation are tight.

High SNR consistency of model order selection criteria in linear regression models has attracted a lot of attention in the signal processing community recently. It is now known that Exponentially Embedded Family, versions of Minimum Description Length etc. are high SNR consistent. However, a general framework for high SNR consistency in linear regression is still missing. This paper fills this gap by deriving necessary and sufficient conditions (NSCs) for model order selection criteria in both known and unknown noise variance situations to be high SNR consistent. Many popular model order selection criteria are proved to be high SNR consistent using these NSCs. We also provide a convergence rate analysis to discriminate between various high SNR consistent model order selection criteria. A direct application of model order selection techniques to the very important subset selection problem is computationally infeasible. This paper establish the high SNR consistency of an existing t-statistics based index ordering scheme that allows the conversion of a subset selection problem into a model order selection problem. Combining this index ordering with high SNR consistent model order selection criteria leads to a novel subset selection procedure (SSP) that is numerically shown to outperform existing high SNR consistent SSPs.

With the advent of the fifth generation of wireless standards and an increasing demand for higher throughput, methods to improve spectral efficiency of wireless systems have become very important. In the context of cognitive radio, a substantial increase in throughput is possible if the secondary user can make smart decisions regarding which channel to sense and when or how often to sense. Here, we propose an algorithm to not only select a channel for data transmission, but also to predict how long the channel will remain unoccupied so that the time spent on channel sensing can be minimized. Our algorithm learns in two stages - a reinforcement learning approach for channel selection and a Bayesian approach to determine the duration for which sensing can be skipped. Comparisons with other methods are provided through extensive simulations. We show that the number of sensing operations is minimized with negligible increase in primary user interference; this implies that less energy is spent by the secondary user in sensing, and also higher throughput is achieved by saving the time spent on sensing.

In this work, we study the outage probability (OP) at the destination of an intelligent reflecting surface (IRS) assisted communication system in a \kappa -\mu fading environment. A practical system model that takes into account the presence of phase error due to quantization at the IRS when a) source-destination (SD) link is present and b) SD link is absent is considered. First, an exact expression is derived, and then we derive three simple approximations for the OP using the following approaches: (i) uni-variate dimension reduction, (ii) moment matching and, (iii) Kullback-Leibler (KL) divergence minimization. The resulting expressions for OP are simple to evaluate and quite tight even in the tail region. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We also study the impact of the number of bits available for quantization, the position of IRS with respect to the source and destination and the number of IRS elements on the OP for systems with and without an SD link. We also demonstrate how the method of moment matching and KL divergence minimization can be used to analyze systems experiencing spatial correlation between the IRS elements.

This paper considers the joint power control and resource allocation for a device-to-device (D2D) underlay cellular system with a multi-antenna base station (BS) employing ADCs with different resolutions. We propose a four-step algorithm that optimizes the ADC resolution profile at the BS to reduce the energy consumption and perform joint power control and resource allocation for D2D users (DUEs) and cellular users (CUEs) and thereby improve the D2D reliability and maximize CUE rate.