Wavelet transformation method to allocate the OFDM signals peaks

Abstract

This work makes use of the entropy in order to propose a wavelet transformation algorithm to detect the odd peaks. Furthermore, this algorithm has been used to enhance the Orthogonal Frequency Division Multiplexing (OFDM) system performance based on combatting the peak-to-average power ratio (PAPR) problem.Three main stages are used to fulfill the process requirements; OFDM signal transformation based on the wavelet structure, thresholding process based on a predetermined criterion, and the filtration stage based on the moving filter.The proposed algorithm performance has been checked and validated not just numerically but also by a MATLAB conducted simulation. Furthermore, to check the simulation results, a comparison has been made to the literature; and shows promising results under the same bandwidth occupancy and systems limitations.

Wavelet transformation method to allocate the DVB-T signals peaks

“…It is known that the CCDF curves can check the system performance by comparing the threshold value against the probability that the PAPR will exceed this certain threshold. From these curves, it is clearly shown that result from proposed work falls in a middle way between the other previous proposed work in Daoud et al (2016) and Daoud (2015). The published work in Daoud (2015) gives the best performance enhancement over the three proposed, where it reduces the CCDF curves at the threshold of 16 dB from 81.48 × 10 -2 to 59.76 × 10 -4 .…”

Reducing PAPR from the Transmitted Signal by the Application of Clipping and Filtering Mechanism

“…Three main stages are associated with the proposed wavelet system. First of all OFDM transmission system is considered, secondly thresholding mechanism is considered for detecting misinterpreted signal and last filtering is applied to reduce PAPR levels within the transmitted signals [7].…”

Wavelet transformation method to allocate the DVB-T signals peaks

“…Previously in our published work the work based on the entropy of the wavelet transformation has been discussed in depth, Daoud et al (2016), Daoud (2015).Wavelet transformation is a process of reconstruction of any signal based on a special treatment of small wavy functions that have limited duration in time with no DC. Therefore, the summation of the multiplication between the signals and the shifted wavy functions will form the continuous wavelet transform (CWT).…”

“…In this table, it is clearly seen, the entropy of the original signal is about 100.4268. After dividing the used signal into eight levels using the wavelet packets, the entropy has been divided into the range of 0.68262 to 74.2432 (Daoud et al, 2016). From the entropy table values (Daoud et al, 2016), OFDM signal decomposition will vary based on the decomposition acceptance.…”

“…The aforementioned problem has been taken into consideration and tackled in some of our previously published work (Daoud et al, 2016(Daoud et al, , 2012a(Daoud et al, , 2012bDaoud, 2015); starting from using the idea of making use of different linear coding techniques in order to spread the affected OFDM symbols; then imposing the artificial intelligence (AI) such as the neural networks, wavelets; after that using special signal processing such as adaptive clipping and the pulse width modulation.…”