04-08-2012, 12:51 PM
Neural Networks in Wireless Communications
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Abstract
Neural processing presents a different way to store
and manipulate knowledge. It uses a connectionist approach,
where connections emphasize the learning capability and
discovery of representations. This work presents a study of the
application areas for neural networks in wireless communication.
Despite its capability to act as a black box and model systems
using learning, domain knowledge is required to apply neural
networks successfully in wireless communications. Considered in
this work are neural network based adaptive equalization, field
strength prediction in indoor networks and microstrip antenna
design using neural networks.
INTRODUCTION
N artificial neural network (ANN) is a network of
artificial neurons. Artificial neurons model the simple
characteristics of neurons in the brain. Each neuron receives
signals from other neurons through special connections called
synapses. Some inputs excite the neuron, others inhibit it.
When the cumulative effect of the inputs to a neuron exceeds a
threshold, the neuron fires a signal to other neurons. Similarly
an artificial neuron receives a set of inputs X=[x1,x2,...,xn],
each coming through an in-bound connection. Connections
have weights, and each input is multiplied by the weight of the
connection it comes along. At the neuron, the weighted inputs
are summed. If the weights are arranged in a vector
W=[w1,w2,...,wn], then the computation of the neuron is
precisely the dot-product p=X.W The neuron applies an
activation function f on the dot-product to get its output y,
given by y=f(p).
CHANNEL EQUALIZATION
Overview
Wireless data transmission channels distort data signals in
both the amplitude and phase, causing Inter-symbol
Interference (ISI). This distortion causes the transmitted
symbols to spread and overlap over successive time intervals,
causing the information content also to spread among these
symbols. Other factors like co-channel interference (CCI),
multipath fading, cause further distortions to the received
symbols. Signal processing techniques used at the receiver to
overcome these interferences, so as to restore the transmitted
symbols and recover their information are referred to as
equalization methods.
ANTENNA DESIGN
Most wireless phenomena like the design or analysis of
antennas, estimation of direction of arrival, adaptive
beamforming techniques, have quite a nonlinear relationship
with their corresponding input variables. Neural networks can
be used in these areas - microstrip antenna analysis and
design, wideband mobile antenna design.
Low Profile Antennas
When antenna occupies appreciable volume a compact
wireless device, and as transceivers are integrated into other
devices, accurate characterization of antenna becomes
necessary for the device's high performance.
Analysis of parameters such as input resistance, bandwidth,
resonant frequency of different regular shaped microstrip
antennas have been modeled using neural networks [7].
Accuracy and simplicity were the key features of these
networks, making ANN candidate for use in CAD algorithms.
CONCLUSION
Neural networks are mathematical models for a selforganizing
and learning black box, which is a universal
function approximator and time series predictor. Neural
networks are built based on domain knowledge and can be
used for causal modeling of systems in uncertainty and noise.
Wireless communication systems present ample scope for
neural networks to be applied, but performance of the neural
network depends on fine tuning the parameters to the network,
and proper quantization of inputs.