Ali Ozen

A novel variable step size adjustment method based on channel output autocorrelation for the LMS training algorithm

A novel variable step size least mean squares (VSS-LMS) algorithm based on channel output autocorrelation has been proposed to improve the weakness of previous VSS-LMS for application to unknown channel estimation or system identification in low-SNR in this paper. Computer simulations have been performed to show the performance of the proposed method in frequency selective Rayleigh fading channels. The obtained simulation results using HIPERLAN/1 standard have demonstrated that the proposed VSS-LMS algorithm has considerably better performance than conventional LMS, RLS, normalized LMS (N-LMS) and the other VSS-LMS algorithms.

A Supervised Constant Modulus Algorithm for Blind Equalization

Blind equalization is a technique for adaptive equalization of a communication channel without the aid of the usual training sequence. Although the Constant Modulus Algorithm (CMA) is one of the most popular adaptive blind equalization algorithms, it suffers from slow convergence rate. A novel enhanced blind equalization technique based on a supervised CMA (S-CMA) is proposed in this paper. The technique is employed to initialize the coefficients of a linear transversal equalizer (LTE) filter in order to provide a fast startup for blind training. It also presents a computational study and simulation results of this newly proposed algorithm compared to other CMA techniques such as conventional CMA, Normalized CMA (N-CMA) and Modified CMA (M-CMA). The simulation results have demonstrated that the proposed algorithm has considerably better performance than others.

Design of a Fuzzy Based Outer Loop Controller for Improving the Training Performance of LMS Algorithm

Because of the fact that mobile communication channel changes by time, it is necessary to employ adaptive channel equalizers in order to combat the distorting effects of the channel. Least Mean Squares (LMS) algorithm is one of the most popular channel equalization algorithms and is preferred over other algorithms such as the Recursive Least Squares (RLS) and Maximum Likelihood Sequence Estimation (MLSE) when simplicity is a dominant decision factor. However, LMS algorithm suffers from poor performance and convergence speed within the training period specified by most of the standards. The aim of this study is to improve the convergence speed and performance of the LMS algorithm by adjusting the step size using fuzzy logic. The proposed method is compared with the Channel Matched Filter-Decision Feedback Equalizer (CMF-DFE) [1] which provides multi path propagation diversity by collecting the energy in the channel, Minimum Mean Square Error-Decision Feedback Equalizer (MMSE-DFE) [2] which is one of the most successful equalizers for the data packet transmission, normalized LMS-DFE (N-LMS-DFE) [3], variable step size (VSS) LMS-DFE [4], fuzzy LMS-DFE [5,6] and RLS-DFE [7]. The obtained simulation results using HIPERLAN/1 standards have demonstrated that the proposed LMS-DFE algorithm based on fuzzy logic has considerably better performance than others.

A novel modulation recognition technique based on artificial bee colony algorithm in the presence of multipath fading channels

In this paper, a novel automatic modulation recognition (AMR) method has been proposed for classifying of the transmitted signals by observing the received data samples in the presence of additive white Gaussian noise (AWGN) and multipath fading channel. The proposed method (ABC-ANN) is based on artificial neural network (ANN) which is trained by artificial bee colony (ABC) algorithm. Because high order statistics are very interesting features to solve the problem of AMR, the high order cumulants have been employed in the proposed ABC-ANN classifier. ABC algorithm is used in finding the optimal weight set of artificial neural networks for classification and the performance of the proposed ABC-ANN algorithm is compared with the performance of ANN classifier (SCG-ANN) using scaled conjugate gradient learning algorithm. Computer simulation results have demonstrated that the proposed recognizer can reach much better classification accuracy than the SCG-ANN in even 0 dB of signal to noise ratio (SNR) value.In this paper, a novel automatic modulation recognition (AMR) method has been proposed for classifying of the transmitted signals by observing the received data samples in the presence of additive white Gaussian noise (AWGN) and multipath fading channel. The proposed method (ABC-ANN) is based on artificial neural network (ANN) which is trained by artificial bee colony (ABC) algorithm. Because high order statistics are very interesting features to solve the problem of AMR, the high order cumulants have been employed in the proposed ABC-ANN classifier. ABC algorithm is used in finding the optimal weight set of artificial neural networks for classification and the performance of the proposed ABC-ANN algorithm is compared with the performance of ANN classifier (SCG-ANN) using scaled conjugate gradient learning algorithm. Computer simulation results have demonstrated that the proposed recognizer can reach much better classification accuracy than the SCG-ANN in even 0 dB of signal to noise ratio (SNR) value.

Fast Convergence Algorithm for Blind Channel Estimation and Equalization Using CMF-DFE

A novel fast blind equalizer is obtained by using the direct calculations from a channel matched filter decision feedback equalizer (CMF-DFE). The proposed technique converts the inverse convolution operations of an equalizer into a linear finite impulse response estimation filter, which is more suitable for blind training. A novel error function is introduced for blind training which enables the use of fast algorithms such as LMS or RLS. The required auto-regression values for the CMF-DFE equalizer are calculated from the incoming data. The resulting performance with LMS training is close to that of non-blind techniques.

A novel Variable Step Size LMS algorithm employing cross correlation between channel output and error signal

A novel variable step size least mean squares (VSS-LMS) algorithm employing cross correlation between channel output and error signal has been proposed as a solution to disadvantage of slow convergence of LMS algorithm. The new algorithm resolves the conflict between the convergence rate and precise of the fixed step-size conventional LMS algorithm. Computer simulations have been performed to verify the performance of the proposed method in frequency selective Rayleigh fading channels. The obtained simulation results using HIPERLAN/1 standard have demonstrated that the proposed VSS-LMS algorithm has considerably better performance than conventional LMS, RLS, normalized LMS (N-LMS) and the other VSS-LMS algorithms.

A novel variable step size constant modulus algorithm employing cross correlation between channel output and error signal

A novel variable step size constant modulus algorithm (VSS-CMA) employing cross correlation between channel output and error signal has been proposed as a solution to the problem of slow convergence of CMA algorithm. The new algorithm resolves the conflict between the convergence rate and low steady state error of the fixed step-size conventional CMA algorithm. Computer simulations have been performed to verify the performance of the proposed method in frequency selective Rayleigh fading channels. The proposed technique has been compared with the popular non-blind equalizer, Least Mean Squares (LMS) algorithm, and blind equalizer, the conventional CMA and Zhaos VSS-CMA as benchmarks. The obtained simulation results have demonstrated that the proposed VSS-CMA algorithm has considerably better performance than the conventional CMA and Zhaos VSS-CMA blind equalization algorithms, and the conventional LMS non-blind equalization algorithm.

A novel walsh hadamard based approach for improving performance of zero tail OFDM systems

A novel zero tail OFDM (ZT OFDM) method based on Walsh Hadamard transform (ZT WHT OFDM) has been proposed to improve the performance of ZT OFDM systems, from 5G candidate waveforms in this paper. Computer simulations have been performed to show the performance of the proposed method in stationary and non-stationary frequency selective Rayleigh fading channels. The obtained simulation results using HIPERLAN/2 physical layer specifications have demonstrated that the proposed ZT WHT OFDM method has considerably better performance and Doppler shift tracking than conventional OFDM and ZT OFDM.

A frequency domain channel equalizer for discrete Wavelet Transform based OFDM systems

A novel frequency domain channel equalizer, inspired by [1], based on discrete wavelet transform (DWT) based OFDM has been proposed to improve the frequency domain channel equalizer employed OFDM systems in this paper. In this study, the performance of DWT based OFDM frequency domain channel equalizer (DWT-OFDM-FDE) has been compared with the conventional FFT based OFDM (FFT-OFDM-FDE) systems. Computer simulations have been performed to verify the performance of the proposed method in frequency selective Rayleigh fading channels. The obtained simulation results using HIPERLAN/2 standard have demonstrated that the proposed DWT-OFDM-FDE system has considerably better performance than the conventional FFT-OFDM-FDE system in all modulation types and also provides high SNR improvement of approximately 8 dB for a BER value of 1E-3.

A novel variable step size constant modulus algorithm based on autocorrelation of error signal for blind equalization

A novel variable step size constant modulus algorithm (VSS-CMA) based on autocorrelation of error signal has been proposed to improve the weakness of previous VSS-CMAs for application to blind equalization in this paper. Computer simulations have been performed to illustrate the performance of the proposed method in frequency selective Rayleigh fading channels. The obtained simulation results using WiMAX (IEEE 802.16–2004) radio standard have demonstrated that the proposed VSS-CMA algorithm has considerably better performance than conventional CMA, normalized CMA (N-CMA) and the other VSS-CMA algorithms.