Browsing by Author "BOUGUEZEL Saad"

Now showing 1 - 5 of 5
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    A Low Complexity Parametric DFT and DHT -Based OFDM Transceivers
    (Fourth International Conference on Technological Advances in Electrical Engineering (ICTAEE’23.), 2023-05-24) CHERGUI Laid; BOUGUEZEL Saad
    In this paper, we design new efficient and fast orthogonal frequency division multiplexing (OFDM) transceivers by introducing parametric discrete Fourier and Hartley transforms. We experimentally show that, for a specific value of the parameter and for various signal-to-noise ratios and modulation schemes such as binary phase shift keying (BPSK), quadratic PSK (QPSK), 16 quadratic amplitude modulation (QAM) and 64QAM, the resulting OFDM transceivers based on these transforms achieve performance in terms of bit error rate similar to that of the existing OFDM transceivers. The compatibility of the proposed OFDM modulators with the conventional DFT-based OFDM demodulator is also experimentally verified. In addition, compared to the existing OFDM transceivers, we show that the computational complexity of the proposed OFDM transceivers is significantly reduced.
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    A New Post-whitening Transform Domain LMS Algorithm
    (IIETA Traitement du Signal, 2019-09-01) CHERGUI Laid; BOUGUEZEL Saad
    This paper proposes a new post-whitening transform domain LMS (POW-TDLMS) algorithm for system identification purposes, where the post whitened and original transformed signals are used during the adaptation and filtering phases, respectively. The main idea behind the proposed algorithm is to introduce a first order adaptive post-whitening filter in the TDLMS algorithm after applying the transform to completely decorrelate the transformed signal. Linear prediction is adopted for the post-whitening and the prediction coefficients are adapted in the time domain. Furthermore, the mean convergence performance analysis of the proposed POW-TDLMS algorithm is presented. The simulation results show the superiority of the proposed POW-TDLMS algorithm compared to the conventional TDLMS algorithm in terms of the MSE convergence speed and reached steady state.
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    A new pre-whitening transform domain LMS algorithm and its application to speech denoising
    (Science Direct, Elsevier Signal Processing, 2016-06-23) CHERGUI Laid; BOUGUEZEL Saad
    In this paper, we propose a new pre-whitening transform domain LMS algorithm. The main idea is to introduce a pre-whitening using a simple finite impulse response decorrelation filter of order one before applying the transform to reinforce its decorrelation. The resulting algorithm has the advantage of using any transform even with low decorrelation. This advantage can be exploited to consider transforms having lower computational and structural complexities than those of the classical transforms. For this purpose, we also investigate the use of other transforms, namely the parametric Fourier and Hartley transforms. This investigation is accomplished by studying the eigenvalue spreads obtained by a given parametric transform and then finding the value of the parameter corresponding to the minimum eigenvalue spread, which is equivalent to the best mean square error (MSE) convergence behavior. This approach provides new attractive transforms for the proposed algorithm. Moreover, we consider the adaptive speech denoising as an application to evaluate the performance of the proposed algorithm. The comparisons between the proposed and conventional algorithms for different transforms are performed in terms of the computational complexity, MSE convergence speed, reached steady state level, residual noise in the denoised signal, steady state excess MSE, misadjustment and output SNR.
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    Enhanced Pre-Whitening TDLMS Adaptive Noise Canceller
    (International Conference on Technological Advances in Electrical Engineering (ICTAEE 2018), 2018-12-12) CHERGUI Laid; BOUGUEZEL Saad
    In this paper, we propose a new algorithm for adaptive noise cancellation. The main idea behind the proposed algorithm is to introduce a first-order adaptive decorrelation filter to decorrelate the error signal in the existing pre-whitening transform domain least mean square (PW-TDLMS) algorithm. The obtained decorrelated error signal is then used in the adaptation equation of the PW-TDLMS algorithm. The proposed adaptive noise canceller is applied to speech denoising and the obtained results are compared to those of the PW-TDLMS and discrete cosine transform-based algorithms. The comparison is performed in terms of the computational complexity, mean square error (MSE) convergence speed, reached steady state level, steady state excess MSE, misadjustment and output signal to noise ratio
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    Variable Step Size Pre-Whitening Transform Domain LMS Adaptive Noise Canceller
    (IEEE Xplore, 2019-03-22) CHERGUI Laid; BOUGUEZEL Saad
    In this paper, we propose a novel variable step size pre-whitening transform domain LMS algorithm for adaptive noise cancellation by introducing a new expression for updating the step size using a smoother gradient vector estimated by weighted averaging. The resulting algorithm presents a good tradeoff between performance and computational complexity and significantly outperforms the existing transform domain LMS algorithms in terms of the convergence speed, level of the steady state reached by the excess mean square error (EMSE), the steady state of the EMSE, misadjustment and output SNR.