Enhancing the Odd Peaks Detection in OFDM Systems Using Wavelet Transforms

Abstract

This work aims to study the effect of unwanted peaks and enhance the performance of wireless systems on the basis of tackling such peaks. A new proposition has been made based on wavelet transform method and its entropy. Signals with large peak-to-average power ratio (PAPR) will be examined such as the ones that are considered as the major Orthogonal Frequency Division Multiplexing (OFDM) systems drawbacks. Furthermore, aspatial diversity Multiple-Input Multiple-Out-put (MIMO) technology is used to overcome the complexity addition that could arise in our proposition. To draw the best performance of this work, a MATLAB simulation has been used; it is divided into three main stages, namely, MIMO-OFDM symbols' reconstruction based on wavelet transform, a predetermined thresholding formula, and finally, moving filter. This algorithm is called Peaks' detection based Entropy Wavelet Transform; PD-EWT. Based on the simulation, and under some constrains such as the bandwidth occupancy and the complexity structure of the transceivers, a peak detection ratio has been achieved and reaches around 0.85. Comparing with our previously published works, the PD-EWT enhances detection ratio for 0.25 more peaks.

Seismic trace noise reduction by wavelets and double threshold estimation

“…In this context of non-stationary denoising signal also can be cited the methods SureShrink, see [4,5] for details, and the Universal Threshold applied by hard-thresholding function [6]. The application of these methods constitutes a vast literature, which can be highlighted by their use in the improvement of the performance of wireless systems [7], ECG denoising [8] and works focused on seismic denoising [2]. Besides these, another method that due to its great efficiency has become popular in recent years is the total variation denoising [9].…”