29-10-2012, 02:22 PM
Nonlinear Fading Channel Equalization of BPSK Signals Using Multiplicative Neuron Model
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Abstract
A high order feed forward neural network
architecture with optimum number of nodes is used for
adaptive channel equalization in this paper.The replacement of
summation at each node by multiplication results in more
powerful mapping because of its capability of processing
higher-order information from training data. The equalizer is
tested on Rayleigh fading channel with BPSK signals.
Performance comparison with recurrent radial basis function
(RRBF) neural network show that the proposed equalizer
provides compact architecture and satisfactory results in terms
of bit error rate performance at various levels of signal to noise
ratios for a Rayleigh fading channel.
INTRODUCTION
As higher-level modulation becomes more desirable to
cope with the need for high-speed data transmission,
nonlinear distortion becomes a major factor, which limits the
data carrying capacity of digital communication sytems.
Thermal noise, impulse noise, cross talk and the nature of the
channel itself distort the transmitted data in amplitude and
phase due to which temporal spreading and consequent
overlap of individual pulses occurs. The presence of inter
symbol interference (ISI) in the system introduces errors in
the decision device at the receiver output. Therefore, in the
design of the transmitting and receiving filters, the objective
is to minimize the effects of ISI, and thereby deliver the
digital data to its destination with the smallest error possible.
Equalizers modelled as adaptive digital filters which shape
the receiver’s transfer function are ubiquitous in todays
signal processing applications to combat ISI in dispersive
channels. Adaptive filters achieve desired spectral
characteristics of a signal by altering the filter coefficients
and thereby the filter response according to a recursive
optimization algorithm. Adaptive coefficients are required
since some parameters of the desired processing operation
(for instance, the properties of some noise signal) are not
known in advance [1].
SIMULATION AND RESULTS
To study the BER performances the equalizer structure
was trained with 3000 iterations and tested over 10000
samples. A fading channel is a communication channel that
experiences fading due to multipath propagation. In wireless
communications, the presence of reflectors in the
environment surrounding the transmitter and receiver create
multiple paths that the transmitted signal can traverse. At the
receiver there is a superposition of these multipath signals
which experience different attenuation, delay and phase shift.
This can result in either constructive or destructive
interference, amplifying or attenuating the signal power seen
at the receiver. Strong destructive interference is known as
deep fade. The fading process is characterised by a Rayleigh
distribution for a non-line-of-sight path. The coherence time
of the channel is related to a quantity known as Doppler
spread of the channel. When the user or the reflectors in the
environment are mobile, the user’s velocity causes a shift in
the frequency of the signal transmitted along each signal path.
The difference in Doppler shifts between different signal
components contributing to a single fading channel tap is
known as Doppler spread.
CONCLUSIONS
A high order feed forward neural network equalizer with
multiplicative neuron is proposed in this paper. Use of
multiplication allows direct computing of polynomial inputs
and approximation with fewer nodes. Performance
comparison in terms of network architecture and BER
performance suggest the better classification capability of the
proposed MNN equalizer over RRBF.