Adaptive processing of image using DWT and FFT OFDM in AWGN and Rayleigh channel

Mannan, Abdul; Habib, Aamir

doi:10.1109/c-code.2017.7918955

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…DWT decomposes the signal into wavelet coefficients from which the original signal can be reconstructed again. The wavelet coefficients represent the signal in various frequency bands [27,28]. Figure 1 shows the structure of discrete wavelet transform adaptive filter (DWTAF).…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…In this paper, we introduce a solution for the aforementioned problems by proposing a new DWTbased RI (DWT-RI) algorithm. Applying DWT on the input signal [22,23] will reduce the eigenvalue spread of the autocorrelation matrix [24,25] and gives the user the freedom to initiate the RI algorithm with relatively high initial values for the step-size. This process guarantees a very high performance, of the algorithm, in terms of both MSE and convergence rate.…”

Section: Introductionmentioning

confidence: 99%

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Discrete wavelet transform-based RI adaptive algorithm for system identification

Salman

Eleyan

Al-Sheikh

2020

IJECE

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In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

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Section: Discrete Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discrete wavelet transform-based RI adaptive algorithm for system identification

Salman

Eleyan

Al-Sheikh

2020

IJECE

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“…Terdapat beberapa penelitian yang sudah membahas tentang DWT-OFDM [25] - [30]. Pada [25] Dengan demikian, simbo l luaran dari konverter serial-keparalel adalah ko mponen frekuensi diskrit dari luaran OFDM modulator. Luaran OFDM modulator d ihasilkan dengan mengubah komponen frekuensinya menjadi sampel waktu dengan melakukan inverse DFT (IDFT) pada subchannel.…”

Section: Pendahuluanunclassified

Transformasi Wavelet dengan Teknik Clipping Filtering untuk Mereduksi PAPR pada OFDM

Wirastuti¹,

Dhyaksa²

2020

Jurnal Teknik Elektro

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Orthogonal Frequency Division Multiplexing (OFDM) is chosen as multiplexing techniques and broadly used in today’s radiocommunication environments to overcome spectrum insufficiency. With several superior advantages, however, OFDM is terribly affected by high peak to average power ratio (PAPR) due to offset frequency errors and local oscillator (LO) frequency synchronization errors. The high PAPR can cause nonlinear distortion, which outcomes in intermodulation and spectral leakage. This study aims to model the use of wavelet transform (discrete wavelet transform (DWT)) to replace Fourier transform (discrete Fourier transform (DFT)) that used in conventional OFDM, later in this paper is termed as DFT-OFDM. Clipping filtering techniques then applied to DWT-OFDM. The model was proposed to reduce PAPR in DFT-OFDM. The model was compared to DFT-OFDM using Matlab simulation method. The performance was evaluated using the Complementary Cumulative Distributive Function (CCDF) vs. PAPR. The results show that at PAPR 10-3for DFT-OFDM, it was produced PAPR of 10.6 dB whereas in DWT-OFDM, using Daubechies orde 7 (Daubechies7), Symlet orde 7 (Symlet7), Coiflet orde 2 (Coiflet2), were reached PAPR 4.8 dB, PAPR 3.3 dB, PAPR 3 dB, respectively. It means Coiflet2 providing the best PAPR reduction among other orthogonal wavelets. By applied clipping filtering to wavelet Coiflet2, it was produced PAPR of 2.9 dB for classical clipping and 2.8 dB for deep clipping. It show that wavelet Coiflet2 with deep clipping provided the best PAPR.

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“…Adaptive filtering techniques can promote accurate solutions and high convergence rates in many signal processing problems [1][2][3]. Some of these well-known problems; such as, noise cancellation [4,5], channel equalization [6], and system identification [7,8], have been addressed by many researchers for many decades.…”

Section: Introductionmentioning

confidence: 99%

Discrete-wavelet-transform recursive inverse algorithm using second-order estimation of the autocorrelation matrix

2020

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The recursive least squares (RLS) algorithm was introduced as an alternative to least mean square (LMS) algorithm with enhanced performance. Computational complexity and instability in updating the autocolleltion matrix are some of the drawbacks of the RLS algorithm that were among the reasons for the introduction of the second-order recursive inverse (RI) adaptive algorithm. The 2 nd order RI adaptive algorithm suffered from low convergence rate in certain scenarios that required a relatively small initial step-size. In this paper, we propose a new second-order RI algorithm that projects the input signal to a new domain namely discrete wavelet transform (DWT) as pre step before performing the agorthim. This transformation overcomes the low convergence rate of the second-order RI algorithm by reducing the selfcorrelation of the input signal in the mentioned scenatios. Expeirments are conducted using the noise cancellation setting. The performance of the proposed algorithm is compared to those of the RI, original second-order RI and RLS algorithms in different Gaussian and impulsive noise environments. Simulations demonstrate the superiority of the proposed algorithm in terms of convergence rate compared to those algorithms.

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Adaptive processing of image using DWT and FFT OFDM in AWGN and Rayleigh channel

Cited by 8 publications

References 11 publications

Discrete wavelet transform-based RI adaptive algorithm for system identification

Discrete wavelet transform-based RI adaptive algorithm for system identification

Transformasi Wavelet dengan Teknik Clipping Filtering untuk Mereduksi PAPR pada OFDM

Discrete-wavelet-transform recursive inverse algorithm using second-order estimation of the autocorrelation matrix

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