PAPR reduction based on entropy wavelet transform for Sniffer Mobile Robot

Daoud, Omar; Hamarsheh, Qadri; Saraireh, Saleh

doi:10.1016/j.aeue.2014.06.005

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Moreover, the proposed algorithm enhances the probability that the PAPR exceeds 20 dB from 2.5×10 -2 to 4.3×10 -4 . This is in addition to that the CCDF curves also improved; as an example at 20 dB threshold it is around 80% while in [18] we have got reduction around 65%. In Figure 6, the curves show not only the performance has been improved but also the noise robustness as well.…”

Section: Wavelet Transformation Propositionmentioning

confidence: 53%

“…Based on the simulation results, it is clearly shown that the proposed work has better performance than either some techniques found in the literature such as clipping technique and partial transmit sequence (PTS) or our previously published work found in [18]. Moreover, the proposed algorithm enhances the probability that the PAPR exceeds 20 dB from 2.5×10 -2 to 4.3×10 -4 .…”

Section: Wavelet Transformation Propositionmentioning

confidence: 69%

“…Furthermore and for simplicity if the used modulation technique is BPSK, this assumption concludes that the resultant average power will equal to the total number of input signals; N, and then the maximum power of the OFDM symbol is N 2 [18]. In this section a new algorithm has been proposed to allocate the high peaks that found in an OFDM signal and to combat the effect of the PAPR.…”

Section: Wavelet Transformation Propositionmentioning

confidence: 99%

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Wavelet transformation method to allocate the OFDM signals peaks

Daoud

Hamarsheh

Damati

2016

2016 13th International Multi-Conference on Systems, Signals &Amp; Devices (SSD)

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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.

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Section: Wavelet Transformation Propositionmentioning

confidence: 53%

Section: Wavelet Transformation Propositionmentioning

confidence: 69%

Section: Wavelet Transformation Propositionmentioning

confidence: 99%

See 1 more Smart Citation

Wavelet transformation method to allocate the OFDM signals peaks

Daoud

Hamarsheh

Damati

2016

2016 13th International Multi-Conference on Systems, Signals &Amp; Devices (SSD)

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“…Furthermore and for simplicity if the used modulation technique is BPSK, this assumption concludes that the resultant average power will equal to the total number of input signals; N, and then the maximum power of the OFDM symbol is N 2 (Daoud et al, 2014). In this section a new algorithm has been proposed to allocate the high peaks that found in an OFDM signal and to combat the effect of the PAPR.…”

Section: Papr Reduction Techniquesmentioning

confidence: 99%

Wavelet transformation method to allocate the DVB-T signals peaks

Daoud¹,

Hamarsheh²,

Damati³

2017

IJDSSS

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Abstract:Terrestrial digital video broadcasting (DVB-T) is considered as one of the main two popular broadcasting standards, which facilitates the transmission of digital television in handheld receivers. In this work, a comparison will be held on the physical layer level in order to enhance the wireless systems' performance. It is attained by exploiting a recently published work that is based on the wavelets entropy and a new proposition that is based on a special averaging technique. Those two propositions have been used to tackle the peak-to-average power ratio (PAPR) problem that is found in the used orthogonal frequency division multiplexing (OFDM) technique. This comparison asset from BER curves and CCDF curves in order to distinguish between them. A MATLAB simulation has been run in order to validate the numerical achieved results of the proposed work. At the same bandwidth occupancy of the speaker verification-based systems, the proposed technique shows additional enhancement in both of CCDF and BER curves. Moreover, it reduces the previously added complexity to the structure in the receiver side. An extra 25% reduction is achieved in the essence of PAPR and around 35% extra noise immunity.Keywords: orthogonal frequency division multiplexing; OFDM; wavelet transform; peak-to-average power ratio; PAPR; entropy; clipping; selective mapping; SLM; signal processing.Reference to this paper should be made as follows: Daoud, O.R., Hamarsheh, Q.J. and Damati, A. (2017) 'Wavelet transformation method to allocate the DVB-T signals peaks', Int.

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“…Signals are decomposed into a feature matrix and different frequency ranges can be obtained by using the WT. And the features usually appear in different frequency ranges [15]. SVD can be used to extract the prominent feature from all the frequency ranges.…”

Section: Feature Extraction In Time and Frequency Domainmentioning

confidence: 99%

A General Purpose Adaptive Fault Detection and Diagnosis Scheme for Information Systems with Superheterodyne Receivers

Song

et al. 2018

Complexity

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A superheterodyne receiver is a type of device universally used in a variety of electronics and information systems. Fault detection and diagnosis for superheterodyne receivers are therefore of critical importance, especially in noise environments. A general purpose fault detection and diagnosis scheme based on observers and residual error analysis was proposed in this study. In the scheme, two generalized regression neural networks (GRNNs) are utilized for fault detection, with one as an observer and the other as an adaptive threshold generator; faults are detected by comparing the residual error and the threshold. Then, time and frequency domain features are extracted from the residual error for diagnosis. A probabilistic neural network (PNN) acts as a classifier to realize the fault diagnosis. Finally, to mimic electromagnetic environments with noise interference, simulation model under different fault conditions with noise interferences is established to test the effectiveness and robustness of the proposed fault detection and diagnosis scheme. Results of the simulation experiments proved that the presented method is effective and robust in simulated electromagnetic environments.

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PAPR reduction based on entropy wavelet transform for Sniffer Mobile Robot

Cited by 5 publications

References 26 publications

Wavelet transformation method to allocate the OFDM signals peaks

Wavelet transformation method to allocate the OFDM signals peaks

Wavelet transformation method to allocate the DVB-T signals peaks

A General Purpose Adaptive Fault Detection and Diagnosis Scheme for Information Systems with Superheterodyne Receivers

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