Babak Hossein Khalaj

On the peak-to-average power of OFDM signals based on oversampling

Orthogonal frequency-division multiplexing (OFDM) introduces large amplitude variations in time, which can result in significant signal distortion in the presence of nonlinear amplifiers. We introduce a new bound for the peak of the continuous envelope of an OFDM signal, based on the maximum of its corresponding oversampled sequence; it is shown to be very tight as the oversampling rate increases. The bound is then used to derive a closed-form probability upper bound for the complementary cumulative distribution function of the peak-to-mean envelope power ratio of uncoded OFDM signals for sufficiently large numbers of subcarriers. As another application of the bound for oversampled sequences, we propose tight relative error bounds for computation of the peak power using two main methods: the oversampled inverse fast Fourier transform and the method introduced for coded systems based on minimum distance decoding of the code.

2D RAKE receivers for CDMA cellular systems

A major advantage of spread-spectrum communication systems is their ability to exploit the multipath structure of the received signal. Different paths can be characterized by their time delays, attenuations, and angles of arrival. A standard RAKE receiver recombines different path signals and mitigates the effects of multipath by separating the paths in time domain. A method of exploiting the spatial properties of the multipath environment using multiple element antenna arrays is proposed where both code and spatial structure of signals can be used to improve SINR. The simulation results present the performance of such a multichannel receiver.

An adaptive fuzzy logic based handoff algorithm for interworking between WLANs and mobile networks

Our goal is to propose an optimal handoff algorithm for hybrid networks (HNs), which are constructed by interworking between wireless LANs (WLANs) and mobile networks. The proposed algorithm is an adaptive fuzzy logic based algorithm that can adapt itself with the dynamic conditions in HNs. It uses mobile terminal speed estimation and traffic in the WLAN as additional input parameters. This algorithm is designed to the meet special requirements of HNs. Simulation results show that it has much better performance than conventional algorithms.

Automated direct patterned wafer inspection

A self-reference technique is developed for detecting the location of defects in repeated pattern wafers and masks. The application area of the proposed method includes inspection of memory chips, shift registers, switch capacitors, and CCD arrays. Using high resolution spectral estimation algorithms, the proposed technique first extracts the period and structure of repeated patterns from the image to sub-pixel resolution, and then produces a defect-free reference image for making comparison with the actual image. Since the technique acquires all its needed information from a single image, there is no need for a database image, a scaling procedure, or any a-priori knowledge about the repetition period of the patterns.<<ETX>>

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, component analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing, and rate of innovation. The redundancy introduced by channel coding in finite and real Galois fields is then related to over-sampling with similar reconstruction algorithms. The error locator polynomial (ELP) and iterative methods are shown to work quite effectively for both sampling and coding applications. The methods of Prony, Pisarenko, and MUltiple SIgnal Classification (MUSIC) are next shown to be targeted at analyzing signals with sparse frequency domain representations. Specifically, the relations of the approach of Prony to an annihilating filter in rate of innovation and ELP in coding are emphasized; the Pisarenko and MUSIC methods are further improvements of the Prony method under noisy environments. The iterative methods developed for sampling and coding applications are shown to be powerful tools in spectral estimation. Such narrowband spectral estimation is then related to multi-source location and direction of arrival estimation in array processing. Sparsity in unobservable source signals is also shown to facilitate source separation in sparse component analysis; the algorithms developed in this area such as linear programming and matching pursuit are also widely used in compressed sensing. Finally, the multipath channel estimation problem is shown to have a sparse formulation; algorithms similar to sampling and coding are used to estimate typical multicarrier communication channels.

Multi-Server Coded Caching

In this paper, we consider multiple cache-enabled clients connected to multiple servers through an intermediate network. We design several topology-aware coding strategies for such networks. Based on the topology richness of the intermediate network, and types of coding operations at internal nodes, we define three classes of networks, namely, dedicated, flexible, and linear networks. For each class, we propose an achievable coding scheme, analyze its coding delay, and also compare it with an information theoretic lower bound. For flexible networks, we show that our scheme is order-optimal in terms of coding delay and, interestingly, the optimal memory-delay curve is achieved in certain regimes. In general, our results suggest that, in the case of networks with multiple servers, type of network topology can be exploited to reduce service delay.

New results on the peak power of OFDM signals based on oversampling

In this paper, we introduce a new bound for the peak of the continuous envelope of an OFDM signal based on the maximum of its corresponding oversampled sequence, that is considerably tighter than the recent results presented in Sharif and Khalaj (2001). The proposed bound is then used as the basis of our study in two parts. In the first part, we find an upper bound for the regrowth of the peak of the digitally clipped oversampled sequence after discrete to continuous conversion. We also show that for oversampling rates greater than /spl pi///spl radic/2, the peak regrowth percentage depends only on oversampling rate, regardless of the number of subcarriers. In the second part, we propose new and tight relative error bounds for computation of the peak power using two main methods: the oversampled inverse fast Fourier transform (IFFT) and the method introduced for coded systems by Tarokh and Jafarkhani (2000).

Modeling IEEE 802.11 DCF Using Parallel Space–Time Markov Chain

Many performance evaluations for the IEEE 802.11 Distributed Coordination Function (DCF) have been previously reported in the literature; most studies are based on saturation analysis, and a few models under a finite-load condition adopt an M/G/1 queuing system. However, using M/G/1 queuing only considers the first moment of the frame service time to derive the probability of the transmission queue being vacant. In this paper, we model the DCF using the parallel space-time Markov chain, where the frame arrivals are tracked by monitoring the transmission queue during transitions between successive states of the space-time Markov chain. The proposed framework provides the possibility of simultaneously modeling the contention phase, the backoff and post-backoff procedures, and the transmission queue status. The proposed framework is also validated by simulation results.

Patterned wafer inspection by high resolution spectral estimation techniques

A new self-reference signal processing technique is proposed for detecting the location of irregularities and defects in a periodic two-dimensional signal or image. Using high-resolution spectral estimation algorithms, the proposed technique first extracts the period and structure of repeated patterns from the image. Then a defect-free reference image for comparison with the actual image is produced. Since the technique acquires all the information needed from a single image (in contrast to most existing methods), there is no need for a database image, a scaling or alignment procedure or any a priori knowledge about the repetition period of the patterns.Potential application fields for the proposed method range from the area of wafer and mask defect inspection, which includes inspection of memory chips, shift registers, switched capacitors, CCD arrays, and LCD displays to other areas that deal with repeated structures, such as crystallography. Some results of applying the proposed technique to real images from microlithography are presented.

Adaptive Consensus Averaging for Information Fusion over Sensor Networks

This paper introduces adaptive consensus, a spatio-temporal adaptive method to improve convergence behavior of the current consensus fusion schemes. This is achieved by introducing a time adaptive weighting method for updating each sensor data in each iteration. Adaptive consensus method will improve node convergence rate, average convergence rate and the variance of error over the network. A mathematical formulation of the method according to the adaptive filter theory as well as derivation of the time adaptive weights and convergence conditions are presented. The analytical results are verified by simulation as well