Abstract: This paper presents two of the most knowing kernel
adaptive filtering (KAF) approaches, the kernel least mean squares
and the kernel recursive least squares, in order to predict a new output
of nonlinear signal processing. Both of these methods implement a
nonlinear transfer function using kernel methods in a particular space
named reproducing kernel Hilbert space (RKHS) where the model is
a linear combination of kernel functions applied to transform the
observed data from the input space to a high dimensional feature
space of vectors, this idea known as the kernel trick. Then KAF is the
developing filters in RKHS. We use two nonlinear signal processing
problems, Mackey Glass chaotic time series prediction and nonlinear
channel equalization to figure the performance of the approaches
presented and finally to result which of them is the adapted one.
Abstract: In this paper, novel statistical sampling based equalization techniques and CNN based detection are proposed to increase the spectral efficiency of multiuser communication systems over fading channels. Multiuser communication combined with selective fading can result in interferences which severely deteriorate the quality of service in wireless data transmission (e.g. CDMA in mobile communication). The paper introduces new equalization methods to combat interferences by minimizing the Bit Error Rate (BER) as a function of the equalizer coefficients. This provides higher performance than the traditional Minimum Mean Square Error equalization. Since the calculation of BER as a function of the equalizer coefficients is of exponential complexity, statistical sampling methods are proposed to approximate the gradient which yields fast equalization and superior performance to the traditional algorithms. Efficient estimation of the gradient is achieved by using stratified sampling and the Li-Silvester bounds. A simple mechanism is derived to identify the dominant samples in real-time, for the sake of efficient estimation. The equalizer weights are adapted recursively by minimizing the estimated BER. The near-optimal performance of the new algorithms is also demonstrated by extensive simulations. The paper has also developed a (Cellular Neural Network) CNN based approach to detection. In this case fast quadratic optimization has been carried out by t, whereas the task of equalizer is to ensure the required template structure (sparseness) for the CNN. The performance of the method has also been analyzed by simulations.
Abstract: In this paper a new cost function for blind equalization
is proposed. The proposed cost function, referred to as the modified
maximum normalized cumulant criterion (MMNC), is an extension
of the previously proposed maximum normalized cumulant criterion
(MNC). While the MNC requires a separate phase recovery system
after blind equalization, the MMNC performs joint blind equalization
and phase recovery. To achieve this, the proposed algorithm
maximizes a cost function that considers both amplitude and phase of
the equalizer output. The simulation results show that the proposed
algorithm has an improved channel equalization effect than the MNC
algorithm and simultaneously can correct the phase error that the
MNC algorithm is unable to do. The simulation results also show that
the MMNC algorithm has lower complexity than the MNC algorithm.
Moreover, the MMNC algorithm outperforms the MNC algorithm
particularly when the symbols block size is small.
Abstract: A new blind symbol by symbol equalizer is proposed.
The operation of the proposed equalizer is based on the geometric
properties of the two dimensional data constellation. An unsupervised
clustering technique is used to locate the clusters formed by the
received data. The symmetric properties of the clusters labels are
subsequently utilized in order to label the clusters. Following this
step, the received data are compared to clusters and decisions are
made on a symbol by symbol basis, by assigning to each data
the label of the nearest cluster. The operation of the equalizer is
investigated both in linear and nonlinear channels. The performance
of the proposed equalizer is compared to the performance of a CMAbased
blind equalizer.
Abstract: In this paper a technique for increasing the
convergence rate of fractionally spaced channel equalizer is
proposed. Instead of symbol-spaced updating of the equalizer filter, a
mechanism has been devised to update the filter at a higher rate. This
ensures convergence of the equalizer filter at a higher rate and
therefore less time-consuming. The proposed technique has been
simulated and tested for two-ray modeled channels with various
delay spreads. These channels include minimum-phase and nonminimum-
phase channels. Simulation results suggest that that
proposed technique outperforms the conventional technique of
symbol-spaced updating of equalizer filter.
Abstract: In this paper the application of neuro-fuzzy system for equalization of channel distortion is considered. The structure and operation algorithm of neuro-fuzzy equalizer are described. The use of neuro-fuzzy equalizer in digital signal transmission allows to decrease training time of parameters and decrease the complexity of the network. The simulation of neuro-fuzzy equalizer is performed. The obtained result satisfies the efficiency of application of neurofuzzy technology in channel equalization.
Abstract: We consider optimal channel equalization for MIMO
(multi-input/multi-output) time-varying channels in the sense of
MMSE (minimum mean-squared-error), where the observation noise
can be non-stationary. We show that all ZF (zero-forcing) receivers
can be parameterized in an affine form which eliminates completely
the ISI (inter-symbol-interference), and optimal channel equalizers
can be designed through minimization of the MSE (mean-squarederror)
between the detected signals and the transmitted signals,
among all ZF receivers. We demonstrate that the optimal channel
equalizer is a modified Kalman filter, and show that under the AWGN
(additive white Gaussian noise) assumption, the proposed optimal
channel equalizer minimizes the BER (bit error rate) among all
possible ZF receivers. Our results are applicable to optimal channel
equalization for DWMT (discrete wavelet multitone), multirate transmultiplexers,
OFDM (orthogonal frequency division multiplexing),
and DS (direct sequence) CDMA (code division multiple access)
wireless data communication systems. A design algorithm for optimal
channel equalization is developed, and several simulation examples
are worked out to illustrate the proposed design algorithm.
Abstract: In this paper, we consider the design of pulse shaping
filter using orthogonal Hermite-Rodriguez basis functions. The pulse
shaping filter design problem has been formulated and solved as a
quadratic programming problem with linear inequality constraints.
Compared with the existing approaches reported in the literature, the
use of Hermite-Rodriguez functions offers an effective alternative to
solve the constrained filter synthesis problem. This is demonstrated
through a numerical example which is concerned with the design of
an equalization filter for a digital transmission channel.