OFDM Channel Estimation Along with Denoising Approach under Small SNR Environment using SSA

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