Yasir Rahmatallah

Peak-To-Average Power Ratio Reduction in OFDM Systems: A Survey And Taxonomy

The objective of this survey is to provide the readers and practitioners in the industry with a broader understanding of the high peak-to-average power ratio (PAPR) problem in orthogonal frequency division multiplexing (OFDM) systems and generate a taxonomy of the available solutions to mitigate the problem. Beginning with a description of OFDM systems, the survey describes the most commonly encountered impediment of OFDM systems, the PAPR problem and consequent impact on power amplifiers leading to nonlinear distortion. The survey clearly defines the metrics based on which the performance of PAPR reduction schemes can be evaluated. A taxonomy of PAPR reduction schemes classifies them into signal distortion, multiple signaling and probabilistic, and coding techniques with further classification within each category. We also provide complexity analyses for a few PAPR reduction methods to demonstrate the differences in complexity requirements between different methods. Moreover, the paper provides insights into the transmitted power constraint by showing the possibility of satisfying the constraint without added complexity by the use of companding transforms with suitably chosen companding parameters. The rapid growth in multimedia-based applications has triggered an insatiable thirst for high data rates and hence increased demand on OFDM-based wireless systems that can support high data rates and high mobility. As the data rates and mobility supported by the OFDM system increase, the number of subcarriers also increases, which in turn leads to high PAPR. As future OFDM-based systems may push the number of subcarriers up to meet the higher data rates and mobility demands, there will be also a need to mitigate the high PAPR that arises, which will likely spur new research activities. The authors believe that this survey will serve as a valuable pedagogical resource for understanding the current research contributions in the area of PAPR reduction in OFDM systems, the different techniques that are available for designers and their trade-offs towards developing more efficient and practical solutions, especially for future research in PAPR reduction schemes for high data rate OFDM systems.

Gene Sets Net Correlations Analysis (GSNCA): A multivariate differential coexpression test for gene sets

Motivation: To date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes. Results: In GSNCA, weight factors are assigned to genes in proportion to the genes’ cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA captures changes in the structure of genes’ cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses. Availability and implementation: Implementation of the GSNCA test in R is available upon request from the authors. Contact: YRahmatallah@uams.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline

Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret experimental results from microarrays, gene set analysis (GSA) has become the method of choice, in particular because it incorporates pre-existing biological knowledge (in a form of functionally related gene sets) into the analysis. Here we provide a brief review of several statistically different GSA approaches (competitive and self-contained) that can be adapted from microarrays practice as well as those specifically designed for RNA-seq. We evaluate their performance (in terms of Type I error rate, power, robustness to the sample size and heterogeneity, as well as the sensitivity to different types of selection biases) on simulated and real RNA-seq data. Not surprisingly, the performance of various GSA approaches depends only on the statistical hypothesis they test and does not depend on whether the test was developed for microarrays or RNA-seq data. Interestingly, we found that competitive methods have lower power as well as robustness to the samples heterogeneity than self-contained methods, leading to poor results reproducibility. We also found that the power of unsupervised competitive methods depends on the balance between up- and down-regulated genes in tested gene sets. These properties of competitive methods have been overlooked before. Our evaluation provides a concise guideline for selecting GSA approaches, best performing under particular experimental settings in the context of RNA-seq.

Gene set analysis for self-contained tests

MOTIVATION The analysis of differentially expressed gene sets became a routine in the analyses of gene expression data. There is a multitude of tests available, ranging from aggregation tests that summarize gene-level statistics for a gene set to true multivariate tests, accounting for intergene correlations. Most of them detect complex departures from the null hypothesis but when the null hypothesis is rejected, the specific alternative leading to the rejection is not easily identifiable. RESULTS In this article we compare the power and Type I error rates of minimum-spanning tree (MST)-based non-parametric multivariate tests with several multivariate and aggregation tests, which are frequently used for pathway analyses. In our simulation study, we demonstrate that MST-based tests have power that is for many settings comparable with the power of conventional approaches, but outperform them in specific regions of the parameter space corresponding to biologically relevant configurations. Further, we find for simulated and for gene expression data that MST-based tests discriminate well against shift and scale alternatives. As a general result, we suggest a two-step practical analysis strategy that may increase the interpretability of experimental data: first, apply the most powerful multivariate test to find the subset of pathways for which the null hypothesis is rejected and second, apply MST-based tests to these pathways to select those that support specific alternative hypotheses. CONTACT gvglazko@uams.edu or yrahmatallah@uams.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Comparative evaluation of gene set analysis approaches for RNA-Seq data

BackgroundOver the last few years transcriptome sequencing (RNA-Seq) has almost completely taken over microarrays for high-throughput studies of gene expression. Currently, the most popular use of RNA-Seq is to identify genes which are differentially expressed between two or more conditions. Despite the importance of Gene Set Analysis (GSA) in the interpretation of the results from RNA-Seq experiments, the limitations of GSA methods developed for microarrays in the context of RNA-Seq data are not well understood.ResultsWe provide a thorough evaluation of popular multivariate and gene-level self-contained GSA approaches on simulated and real RNA-Seq data. The multivariate approach employs multivariate non-parametric tests combined with popular normalizations for RNA-Seq data. The gene-level approach utilizes univariate tests designed for the analysis of RNA-Seq data to find gene-specific P-values and combines them into a pathway P-value using classical statistical techniques. Our results demonstrate that the Type I error rate and the power of multivariate tests depend only on the test statistics and are insensitive to the different normalizations. In general standard multivariate GSA tests detect pathways that do not have any bias in terms of pathways size, percentage of differentially expressed genes, or average gene length in a pathway. In contrast the Type I error rate and the power of gene-level GSA tests are heavily affected by the methods for combining P-values, and all aforementioned biases are present in detected pathways.ConclusionsOur result emphasizes the importance of using self-contained non-parametric multivariate tests for detecting differentially expressed pathways for RNA-Seq data and warns against applying gene-level GSA tests, especially because of their high level of Type I error rates for both, simulated and real data.

Bit Error Rate Performance of Linear Companding Transforms for PAPR Reduction in OFDM Systems

This paper provides an analytical framework to study the performance of linear companding techniques proposed in the OFDM literature, thus settling the numerous controversial claims that are based solely on simulation results. Linear companding transforms are widely employed to reduce the peak-to- average-power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. Two main linear companding classes have been considered in the literature: linear symmetrical transform (LST) and linear asymmetrical transform (LAST). In the literature, the bit error rate (BER) performance superiority of the basic LAST (with one discontinuity point) over the LST is claimed based on computer simulations. Also, it has been claimed that a LAST with two discontinuity points outperforms the basic LAST with one discontinuity point. These claims are however not substantiated with analytical results. Our analysis shows that these claims are, in general, not always true. We derive a sufficient condition, in terms of the companding parameters, under which the BER performance of a general LAST with M-1 discontinuity points is superior to that of LST. The derived condition explains the contradictions between different reported results in the literature and validates some other reported simulation results. It also serves as a guideline in the process of choosing proper values for companding parameters to obtain a specific trade-off between PAPR reduction capability and BER performance. In particular, the derived sufficient condition shows that the BER performance for LAST depends on the slopes of the LAST rather than on the number of discontinuity points as has been indicated so far. Moreover, we derive conditions for the companding parameters in order to keep the average transmitted power unchanged after companding. Our theoretical derivations are supported by simulation results.

Mutual coupling reduction of dual-band printed monopoles using MNG metamaterial

In this paper, a μ-Negative metamaterial (MNG) is utilized for mutual coupling reduction between dual-band printed monopole antennas used in Multiple Input Multiple Output (MIMO). A dual-band MNG metamaterial is designed to specifically possess negative effective permeability at the two resonant frequencies where the antennas are operating. MNG is inserted between the two printed monopoles (back to back) to decrease the correlation between them. The printed monopole antennas were designed to operate in the Wireless Local Area Network (WLAN) bands 2.45 GHz and 5.2 GHz. Antenna characteristics such as, scattering parameters far-field radiation patterns with and without the presence of MNG are provided. The design of the MNG unit cell and its effective constitutive parameters are also provided. Design and simulations are conducted using Ansofts HFSS software which is based on the Finite Element Method (FEM). The proposed technique achieved a 14 dB reduction in mutual coupling at 2.45 GHz and 13 dB at 5.2 GHz. A gain of 2 dB higher than the normal case at the second band is observed while it is maintained the same on the first band. Furthermore, the MNG based antenna system maintains a relatively low profile (16 mm) which is convenient for compact systems and hand-held devices.

Bit-Error-Rate Performance of Companding Transforms for OFDM

This paper provides a comprehensive analytical framework to assess the relative bit-error-rate (BER) performance of companding transforms (CTs) employed to reduce the peak-to-average-power ratio (PAPR) in orthogonal frequency-division multiplexing (OFDM) systems. This paper provides a quantitative basis for several claims, which are reported in the literature, based solely on simulation results. In particular, we consider three main classes of CTs and provide a set of necessary and sufficient conditions for the superiority of one CT relative to the others. The conditions are given in terms of the companding parameters, which are usually selected to achieve a target PAPR. Our analytical derivations are supported by simulation results.

On the performance of linear and nonlinear companding transforms in OFDM systems

Companding schemes are widely employed to reduce the peak-to-average-power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. This paper considers the classes of linear and nonlinear companding schemes and derives a sufficient condition under which the bit error rate (BER) performance of one is superior to that of the other. The high PAPR of the OFDM signal drives the transmitters power amplifier into its nonlinear region, thus causing nonlinear distortions. In the literature, signal companding is one of the widely used techniques for PAPR reduction. Furthermore, it has been claimed, based on simulation results, that linear companding performs better, in terms of BER, than nonlinear companding for an optimized set of companding parameters. In this paper, we derive sufficient conditions under which these claims are valid. The conditions derived also show that, in practice, nonlinear companding transforms perform better than linear companding transform. Our theoretical analysis is supported by simulation results.

16S rRNA Gene-Based Metagenomic Analysis of Ozark Cave Bacteria

The microbial diversity within cave ecosystems is largely unknown. Ozark caves maintain a year-round stable temperature (12–14 °C), but most parts of the caves experience complete darkness. The lack of sunlight and geological isolation from surface-energy inputs generate nutrient-poor conditions that may limit species diversity in such environments. Although microorganisms play a crucial role in sustaining life on Earth and impacting human health, little is known about their diversity, ecology, and evolution in community structures. We used five Ozark region caves as test sites for exploring bacterial diversity and monitoring long-term biodiversity. Illumina MiSeq sequencing of five cave soil samples and a control sample revealed a total of 49 bacterial phyla, with seven major phyla: Proteobacteria, Acidobacteria, Actinobacteria, Firmicutes, Chloroflexi, Bacteroidetes, and Nitrospirae. Variation in bacterial composition was observed among the five caves studied. Sandtown Cave had the lowest richness and most divergent community composition. 16S rRNA gene-based metagenomic analysis of cave-dwelling microbial communities in the Ozark caves revealed that species abundance and diversity are vast and included ecologically, agriculturally, and economically relevant taxa.