Original scientific paper
https://doi.org/10.24138/jcomss.v18i1.1082
OFDM Channel Estimation Along with Denoising Approach under Small SNR Environment using SSA
E. Hari Krishna
; Department of Electronics & Communication Engineering, University College of Engineering, Kakatiya University, Kothagudem, Telangana, India
K. Sivani
; Department of Electronics & Instrumentation Engineering, Kakatiya Institute of Technology & Science, Warangal, Telangana, India
K. Ashoka Reddy
; Department of Electronics & Communication Engineering, Kakatiya Institute of Technology & Science, Warangal, Telangana, India
Abstract
In this paper, a de-noising approach in conjunction
with channel estimation (CE) algorithm for OFDM systems using
singular spectrum analysis (SSA) is presented. In the proposed
algorithm, the initial CE is computed with the aid of traditional
linear minimum mean square error (LMMSE) algorithm, and
then further channel is evaluated by considering the low rank
eigenvalue approximation of channel correlation matrix related
to channel using SSA. Simulation results on bit error rate (BER)
revealed that the method attains an improvement of 7 dB, 5 dB
and 3 dB compared to common LSE, MMSE and SVD based
methods respectively. With the help of statistical correlation coefficient (C) and kurtosis (k), the SSA method utilized to de-noise
the received OFDM signal in addition to CE. In the process of denoising, the received OFDM signal will be decomposed into
different empirical orthogonal functions (EOFs) based on the
singular values. It was established that the correlation coefficients
worked well in identifying useful EOFs only up to moderate
\(SNR \geq 12dB\). For low SNR<12 dB, kurtosis was found to be a
useful measure for identifying the useful EOFs. In addition to
outperforming the existing methods, with this de-noising
approach, the mean square error (MSE) of channel estimator is
further improved approximately 1 dB more in SNR at the cost of
computational complexity.
Keywords
BER; Channel estimation; OFDM; SVD; SSA; Wireless communications
Hrčak ID:
272232
URI
Publication date:
31.3.2022.
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