Blind Channel Estimation Using Wavelet Denoising of Independent Component Analysis for LTE

Abstract

<p>A new proposal of blind channel estimation method for long term evoluation (LTE) based on combining advantages of denoising property of wavelet transform (WT) with blind estimation capability of independent component analysis (ICA) called wavelet denoising of ICA (WD-ICA) was presented. This new method increased the spectral efficiency compared to training based methods, and provided considerable performance enhancement over conventional ICA methods. The conventional blind channel estimation methods based on ICA were performed individually for each orthogonal frequency division multiplexing (OFDM) subcarrier. To reduce complexity of implementation of WD-ICA method, channel interpolation was used. This method was presented for multiple-input-multiple-output (MIMO) downlink LTE system. WD-ICA method was compared to conventional ICA methods and the Performance was evaluated by calculating normalized mean square error (NMSE) and bit error rate (BER). WD-ICA method as compared to the other known ICA channel estimation methods has lower complexity, lower value of NMSE, and lower value of BER, which indicates the superiority of the proposed method.</p>

An Enhanced Impulsive Noise Suppression Method Based on Wavelet Denoising and ICA for Power Line Communication

“…Therefore, under the condition of low SNR, this time the wavelet denoising is used to preprocess the mixed signal, the signals After preprocessing the mixed signals using wavelet denoising and then it is separated by PowerICA. Experimental simulations verify that the combination of the two algorithms(ie, WD-PowerICA) can achieve stable and efficient separation [25]. The specific steps are:…”

“…In the experiment, the signal noise model is shown in section Ⅱ. The useful signal s is the OFDM signal, and v is the impulse noise, they are seen as two input source signals, the carrier frequency is 1000 and sample frequency is 2000, and the SNR of input Following the previous work [13], this article studies the separation [25]. The specific steps are: In the simulation experiment, other conditions remain unchanged, only the SNR is changed, and the separation effect of the three algorithms is shown in Figure 7.…”

Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

“…Under the background that the wavelet transform speech denoising algorithm is widely used in speech recognition technology, a large number of scientific research institutions and speech recognition experts have studied and analyzed the wavelet transform speech recognition technology. Its main research characteristics are concentrated in three levels: maximum value, multiresolution, and data compression [18]. Relevant research institutions in Europe and America first proposed to filter the noise signal in the speech signal by using wavelet decomposition and corresponding reconstruction technology based on the multiresolution theory.…”