Ahmed Abdul Quadeer

Impulse Noise Estimation and Removal for OFDM Systems

Orthogonal Frequency Division Multiplexing (OFDM) is a modulation scheme that is widely used in wired and wireless communication systems. While OFDM is ideally suited to deal with frequency selective channels and AWGN, its performance may be dramatically impacted by the presence of impulse noise. In fact, very strong noise impulses in the time domain might result in the erasure of whole OFDM blocks of symbols at the receiver. Impulse noise can be mitigated by considering it as a sparse signal in time, and using recently developed algorithms for sparse signal reconstruction. We propose an algorithm that utilizes the guard band null subcarriers for the impulse noise estimation and cancellation. Instead of relying on ℓ1 minimization as done in some popular general-purpose compressive sensing schemes, the proposed method jointly exploits the specific structure of this problem and the available a priori information for sparse signal recovery. The computational complexity of the proposed algorithm is very competitive with respect to sparse signal reconstruction schemes based on ℓ1 minimization. The proposed method is compared with respect to other state-of-the-art methods in terms of achievable rates for an OFDM system with impulse noise and AWGN.

Impulsive noise estimation and cancellation in DSL using orthogonal clustering

Impulsive noise is the bottleneck that limits the distance at which DSL communications can take place. By considering impulsive noise a sparse vector, recently developed sparse reconstruction algorithms can be utilized to combat it. We propose an algorithm that utilizes the guard band null carriers for the impulsive noise estimation and cancellation. Instead of relying on ℓ1 minimization as done in some popular general-purpose compressive sensing (CS) schemes, the proposed method exploits the structure present in the problem and the available a priori information jointly for sparse signal recovery. The computational complexity of the proposed algorithm is very low as compared to the sparse reconstruction algorithms based on ℓ1 minimization. A performance comparison of the proposed method with other techniques, including ℓ1 minimization and another recently developed scheme for sparse signal recovery, is provided in terms of achievable rates for a DSL line with impulse noise estimation and cancellation.

Impulsive noise estimation and cancellation in DSL using compressive sampling

Impulsive noise is the bottleneck that determines the maximum length of the DSL. Impulsive noise seldom occurs in DSL but when it occurs, it is very destructive and results in dropping the affected DSL symbols at the receiver as they cannot be recovered. By considering impulsive noise a sparse vector, recently developed sparse reconstruction algorithms can be utilized to combat it. We propose an algorithm that utilizes the null carriers for the impulsive noise estimation and cancellation. Specifically, we use compressive sampling for a coarse estimate of the impulse position, an a priori information based MAP metric for its refinement, followed by MMSE estimation for estimating the impulse amplitudes. We also present a comparison of the achievable rate in DSL using our algorithm and recently developed algorithms for sparse signal reconstruction.

A forward-backward Kalman filter-based STBC MIMO OFDM receiver

Orthogonal frequency division multiplexing () has emerged as a modulation scheme that can achieve high data rates over frequency selective fading channel by efficiently handling multipath effects. This paper proposes receiver design for space-time block coded transmission over frequency selective time-variant channels. Joint channel and data recovery are performed at the receiver by utilizing the expectation-maximization () algorithm. It makes collective use of the data constraints (pilots, cyclic prefix, the finite alphabet constraint, and space-time block coding) and channel constraints (finite delay spread, frequency and time correlation, and transmit and receive correlation) to implement an effective receiver. The channel estimation part of the receiver boils down to an -based forward-backward Kalman filter. A forward-only Kalman filter is also proposed to avoid the latency involved in estimation. Simulation results show that the proposed receiver outperforms other least-squares-based iterative receivers.Orthogonal frequency division multiplexing ( Open image in new window ) has emerged as a modulation scheme that can achieve high data rates over frequency selective fading channel by efficiently handling multipath effects. This paper proposes receiver design for space-time block coded Open image in new window transmission over frequency selective time-variant channels. Joint channel and data recovery are performed at the receiver by utilizing the expectation-maximization ( Open image in new window ) algorithm. It makes collective use of the data constraints (pilots, cyclic prefix, the finite alphabet constraint, and space-time block coding) and channel constraints (finite delay spread, frequency and time correlation, and transmit and receive correlation) to implement an effective receiver. The channel estimation part of the receiver boils down to an Open image in new window -based forward-backward Kalman filter. A forward-only Kalman filter is also proposed to avoid the latency involved in estimation. Simulation results show that the proposed receiver outperforms other least-squares-based iterative receivers.

Structure-Based Bayesian Sparse Reconstruction

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity.

Statistical Linkage Analysis of Substitutions in Patient-Derived Sequences of Genotype 1a Hepatitis C Virus Nonstructural Protein 3 Exposes Targets for Immunogen Design

ABSTRACT Chronic hepatitis C virus (HCV) infection is one of the leading causes of liver failure and liver cancer, affecting around 3% of the worlds population. The extreme sequence variability of the virus resulting from error-prone replication has thwarted the discovery of a universal prophylactic vaccine. It is known that vigorous and multispecific cellular immune responses, involving both helper CD4+ and cytotoxic CD8+ T cells, are associated with the spontaneous clearance of acute HCV infection. Escape mutations in viral epitopes can, however, abrogate protective T-cell responses, leading to viral persistence and associated pathologies. Despite the propensity of the virus to mutate, there might still exist substitutions that incur a fitness cost. In this paper, we identify groups of coevolving residues within HCV nonstructural protein 3 (NS3) by analyzing diverse sequences of this protein using ideas from random matrix theory and associated methods. Our analyses indicate that one of these groups comprises a large percentage of residues for which HCV appears to resist multiple simultaneous substitutions. Targeting multiple residues in this group through vaccine-induced immune responses should either lead to viral recognition or elicit escape substitutions that compromise viral fitness. Our predictions are supported by published clinical data, which suggested that immune genotypes associated with spontaneous clearance of HCV preferentially recognized and targeted this vulnerable group of residues. Moreover, mapping the sites of this group onto the available protein structure provided insight into its functional significance. An epitope-based immunogen is proposed as an alternative to the NS3 epitopes in the peptide-based vaccine IC41. IMPORTANCE Despite much experimental work on HCV, a thorough statistical study of the HCV sequences for the purpose of immunogen design was missing in the literature. Such a study is vital to identify epistatic couplings among residues that can provide useful insights for designing a potent vaccine. In this work, ideas from random matrix theory were applied to characterize the statistics of substitutions within the diverse publicly available sequences of the genotype 1a HCV NS3 protein, leading to a group of sites for which HCV appears to resist simultaneous substitutions possibly due to deleterious effect on viral fitness. Our analysis leads to completely novel immunogen designs for HCV. In addition, the NS3 epitopes used in the recently proposed peptide-based vaccine IC41 were analyzed in the context of our framework. Our analysis predicts that alternative NS3 epitopes may be worth exploring as they might be more efficacious.

Enhanced channel estimation using Cyclic Prefix in MIMO STBC OFDM sytems

Channel estimation is an important part of any receiver design. This paper presents an improved iterative joint channel estimation and data detection algorithm for Space Time Block Coded (STBC) MIMO OFDM systems in fast fading environments. The algorithm utilizes both time and frequency correlation information. We show how the Cyclic Prefix (CP) can be used to enhance the joint channel estimation and data detection process. We present two variations of the Expectation Maximization (EM) based Forward Backward (FB) Kalman filter algorithm utilizing the CP information and provide their performance comparison. Simulation results show that the proposed use of CP to aid the EM based FB Kalman algorithm results in improved performance.

Cyclic Prefix Based Enhanced Data Recovery in OFDM

In this correspondence, we show how the cyclic prefix (CP) can be used to enhance the performance of an orthogonal-frequency-division multiplexing (OFDM) receiver. Specifically, we show how an OFDM symbol transmitted over a block fading channel can be blindly detected using the output symbol and associated CP. The algorithm boils down to a nonlinear relationship involving the input and output data only that can be used to search for the maximum-likelihood (ML) estimate of the input. This relationship becomes much simpler for constant modulus (CM) data. We also propose iterative methods to reduce the computational complexity involved in the ML search of the input for CM data.

Structure based Bayesian sparse reconstruction using non-Gaussian prior

In this paper, we present a fast Bayesian method for sparse signal recovery that makes a collective use of the sparsity information, a priori statistical properties, and the structure involved in the problem to obtain near optimal estimates at very low complexity. Specifically, we utilize the rich structure present in the sensing matrix encountered in many signal processing applications to develop a fast reconstruction algorithm when the statistics of the sparse signal are non-Gaussian or unknown. The proposed method outperforms the widely used convex relaxation approaches as well as greedy matching pursuit techniques all while operating at a much lower complexity.

Compressed sensing techniques for receiver based post-compensation of transmitter's nonlinear distortions in OFDM systems

In this paper, compressed sensing techniques are proposed to linearize commercial power amplifiers driven by orthogonal frequency division multiplexing signals. The nonlinear distortion is considered as a sparse phenomenon in the time-domain, and three compressed sensing based algorithms are presented to estimate and compensate for these distortions at the receiver using a few and, at times, even no frequency-domain free carriers (i.e. pilot carriers). The first technique is a conventional compressed sensing approach, while the second incorporates a priori information about the distortions to enhance the estimation. Finally, the third technique involves an iterative data-aided algorithm that does not require any pilot carriers and hence allows the system to work at maximum bandwidth efficiency. The performances of all the proposed techniques are evaluated on a commercial power amplifier and compared. The error vector magnitude and symbol error rate results show the ability of compressed sensing to compensate for the amplifiers nonlinear distortions. HighlightsCompressed Sensing is used for receiver based power amplifier linearization.Accurate measurement based model of commercial power amplifier is used.Weighted compressed sensing is proposed for enhanced estimation.Bandwidth efficient iterative data aided algorithm is proposed.Data aided enhanced channel estimation scheme is proposed.Simulation of the channel effect on the performance of the developed algorithm.