Abstract This paper applies neural networks to the adaptive channel equalization of a bipolar signal passed through a dispersive channel in the presence of additive noise. The paper describes two neural networks which might be considered as adaptive equalizers. The simulation results confirm that the neural network equalizers offer a performance which exceeds that of linear structures. More specifically, this paper highlights the effects of delay order on BER performance for nonlinear structures. I
A novel feed forward multiplicative neural network architecture with optimum number of nodes is used...
This paper presents a new neural architecture suitable for digital signal processing application. Th...
This paper presents a new neural architecture suitable for digital signal processing application. Th...
Adaptive equalization of channels with non-linear intersymbol interference is considered. It is show...
When digital signals are transmitted through frequency selective communication channels, one of the ...
One of the main obstacles to reliable communications is the inter symbol interference (ISI). An equa...
For severe inter-symbol-interference (ISI) channels, linear post-equalizer at the receiver causes no...
In wireless communications, transmitted signals suffer from distortion caused by the channel. Equali...
Back-propagation is a popular method for training feed-forward neural networks. This thesis extends ...
The paper considers the problem of constructing adaptive minimum bit error rate (MBER) neural networ...
Abstract: Problem statement: Digital transmission over band-limited communication channel largely su...
A computational efficient artificial neural network for adaptive channel equalization in a digital c...
In digital communication systems, multipath propagation induces Inter Symbol Interference (ISI). To ...
Channel equalisation is a process of compensating the disruptive effects caused mainly by Inter Symb...
The field of digital data communications has experienced an explosive growth in the last three decad...
A novel feed forward multiplicative neural network architecture with optimum number of nodes is used...
This paper presents a new neural architecture suitable for digital signal processing application. Th...
This paper presents a new neural architecture suitable for digital signal processing application. Th...
Adaptive equalization of channels with non-linear intersymbol interference is considered. It is show...
When digital signals are transmitted through frequency selective communication channels, one of the ...
One of the main obstacles to reliable communications is the inter symbol interference (ISI). An equa...
For severe inter-symbol-interference (ISI) channels, linear post-equalizer at the receiver causes no...
In wireless communications, transmitted signals suffer from distortion caused by the channel. Equali...
Back-propagation is a popular method for training feed-forward neural networks. This thesis extends ...
The paper considers the problem of constructing adaptive minimum bit error rate (MBER) neural networ...
Abstract: Problem statement: Digital transmission over band-limited communication channel largely su...
A computational efficient artificial neural network for adaptive channel equalization in a digital c...
In digital communication systems, multipath propagation induces Inter Symbol Interference (ISI). To ...
Channel equalisation is a process of compensating the disruptive effects caused mainly by Inter Symb...
The field of digital data communications has experienced an explosive growth in the last three decad...
A novel feed forward multiplicative neural network architecture with optimum number of nodes is used...
This paper presents a new neural architecture suitable for digital signal processing application. Th...
This paper presents a new neural architecture suitable for digital signal processing application. Th...