A Novel Two Stage Carrier Frequency Offset Estimation and Compensation Scheme in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing System Using Expectation and Maximization Iteration

Pandy,

doi:10.3844/jcssp.2013.1526.1533

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…When we use Gaussian models, each component assumes a multivariate normal distribution, where θj = {μj; ∑j}. This model is known as Gaussian Mixture Model (GMM) [3], [4]. Eq.…”

Section: (3)mentioning

confidence: 99%

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A Systematic Method for Detecting Parallelized Software Bottlenecks and Suggesting Modifications: The Case of the Expectation Maximization Algorithm

Oliveira¹,

Chella²,

Macedo³

et al. 2017

JSW

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Parallelized algorithms can distribute the workload on the available multi-core processors. Graphical Processing Units (GPU) began to be used in general purpose computing thanks to its ability to simultaneously perform thousands of operations in their parallel coprocessors. Unfortunately, providing parallelized versions of typical sequential routines is not a trivial task. Even with the advent of CUDA, the NVIDIA's more intuitive solution for GPU programming, developers need to acquire a deep knowledge of GPU architecture and the rationale of the target algorithms to optimize resources usage and reduce processing time. This paper proposes a systematic method for analyzing parallelized algorithms and propose guidelines for CUDA code refactoring in such a way faster and more efficient software, regarding hardware resources consumption, could be constructed. One of such kind of software is Automatic Speech Recognition (ASR) systems. Mainstream approaches for ASR use the Expectation Maximization (EM) algorithm to train Gaussian Mixture Models (GMM) to provide an Acoustic Model for ASR. These training phase is usually extensive time-consuming and so it's well suited for a parallelized solution approach. We show the feasibility of our method identifying important issues in a literature's parallelized implementation of EM and further refactoring suggestion to enhance memory occupancy and decrease processing time. The results show a processing speedup of the EM algorithm around 40x (minimum) and 61x (maximum) when compared to the control version. The method was also effective in the improvement of the values for all the concerned performance metrics for GPU-based solutions.

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“…When we use Gaussian models, each component assumes a multivariate normal distribution, where θj = {μj; ∑j}. This model is known as Gaussian Mixture Model (GMM) [3], [4]. Eq.…”

Section: (3)mentioning

confidence: 99%

“…The E-step computes conditional expectation of the logarithmic likelihood, conditionally to the set of observed data x and the current value of the parameters θ: (4) The M-step computes the (i+1)-th parameter vector θ that maximizes Q(θt+1, θ), given by:…”

Section: (3)mentioning

confidence: 99%

A Systematic Method for Detecting Parallelized Software Bottlenecks and Suggesting Modifications: The Case of the Expectation Maximization Algorithm

Oliveira¹,

Chella²,

Macedo³

et al. 2017

JSW

View full text Add to dashboard Cite

show abstract

“…It is yet possible to perform estimation of model parameters. The ExpectationMaximization (EM) allows learning of parameters that govern the distribution of the sample data with some missing features (Sujaritha and Annadurai, 2011;Poongothai and Sathiyabama, 2012;Malarvezhi and Kumar, 2013).…”

Section: Expectation-maximization Algorithm (Em)mentioning

confidence: 99%

Parallel Implementation of Expectation-Maximisation Algorithm for the Training of Gaussian Mixture Models

Araújo¹,

Macedo²,

Chella³

et al. 2014

Journal of Computer Science

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Most machine learning algorithms need to handle large data sets. This feature often leads to limitations on processing time and memory. The Expectation-Maximization (EM) is one of such algorithms, which is used to train one of the most commonly used parametric statistical models, the Gaussian Mixture Models (GMM). All steps of the algorithm are potentially parallelizable once they iterate over the entire data set. In this study, we propose a parallel implementation of EM for training GMM using CUDA. Experiments are performed with a UCI dataset and results show a speedup of 7 if compared to the sequential version. We have also carried out modifications to the code in order to provide better access to global memory and shared memory usage. We have achieved up to 56.4% of achieved occupancy, regardless the number of Gaussians considered in the set of experiments.

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A Diversity Enhanced Particle Filter for Carrier Frequency Offset Estimation in Nonlinear OFDM System

Malarvezhi

Kumar

2016

Wireless Pers Commun

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A Novel Two Stage Carrier Frequency Offset Estimation and Compensation Scheme in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing System Using Expectation and Maximization Iteration

Cited by 5 publications

References 9 publications

A Systematic Method for Detecting Parallelized Software Bottlenecks and Suggesting Modifications: The Case of the Expectation Maximization Algorithm

A Systematic Method for Detecting Parallelized Software Bottlenecks and Suggesting Modifications: The Case of the Expectation Maximization Algorithm

Parallel Implementation of Expectation-Maximisation Algorithm for the Training of Gaussian Mixture Models

A Diversity Enhanced Particle Filter for Carrier Frequency Offset Estimation in Nonlinear OFDM System

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