PAPR reduction based on entropy wavelet transform for Sniffer Mobile Robot

In this paper, the performance of wireless systems has been improved based on a proposition of a new peak detection algorithm based on the entropy of the wavelet transforms [1] and to be imposed to the Sniffer Mobile Robot (SNFRbot). This enhancement has been compared with a previously published work that uses a predistortion neural network (PDNN) architecture has been imposed to the SNFRbot. Moreover, the proposed method in this work has been applied to combat the Orthogonal Frequency Division Multiplexing (OFDM) system's drawback; namely peak-to-average power ratio (PAPR). Generally, the proposed algorithm consists of three stages; firstly, reconstruction based on wavelet transform, secondly, predetermined thresholding, and thirdly, imposing moving filter. This algorithm is called Multiple-Input Multiple-Output (MIMO)-OFDM system based entropy wavelet transform and to be imposed to SNFRbot work; SNFRbot-EWT.The performance of SNFRbot-EWT has been evaluated using a numerical analysis and a conducted simulation. The results have been compared with the our previously published work, SNFRbot-EWT gives an improvement reach to around 30% for the same bandwidth occupancy, which will reduce the cost and the complexity of the transceiver's structure.