FPGA-Based Time-Domain Channel Estimation in Gaussian Mixture Model

Author:

Khalid Muhammad1ORCID,Muhammad Khan Abid2ORCID,Rauf Muhammad3ORCID,Taha Jilani Muhammad4ORCID,Afzal Sheraz2ORCID

Affiliation:

1. Electrical Engineering Department, Bahria University, Karachi Campus, Karachi, Pakistan

2. Electronic Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan

3. Department of Electronic Engineering, Dawood University of Engineering and Technology, Karachi, Pakistan

4. Graduate School of Science and Engineering, PAF Karachi Institute of Economics and Technology, Karachi, Pakistan

Abstract

The performance of time-domain channel estimation deteriorates due to the presence of Gaussian mixture model (GMM) noise, which results in high mean squared error (MSE) as a challenging issue. The performance of the estimator further decreases when the complexity of the estimator is high due to the high convergence rate. In this paper, an optimized channel estimation method is proposed with low complexity and high accuracy in the GMM environment. In this channel estimation, an improved Gauss-Seidel iterative method is utilized with a minimum number of iterations. The convergence rate of the Gauss-Seidel method is improved by estimating an appropriate initial guess value when no guard bands are used in the orthogonal frequency-division multiplexing (OFDM) symbol. Simulation results provide an acceptable MSE for GMM environments, up to the probability of 5% impulsive noise component. This paper also presents the design and implementation of the proposed estimator in the NEXYS-2 FPGA platform that provides resources allocation, reconfigurability, schematic, and the timing diagram for detailed insight.

Funder

Higher Education Commission, Pakistan

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Reference33 articles.

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3. Pilot-aided Joint Channel Estimation for OFDM Based Cooperative Multi-cell Networks

4. Maximum-likelihood array processing in non-Gaussian noise with Gaussian mixtures

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