How to use Soft Computing in Wireless Communication area?
Abstract
The
proliferation of number of users in a limited wireless spectrum have raised the
levels of inter symbol interference (ISI) and have also contributed towards
probable degradation of quality of service (QoS). The key challenges faced by
upcoming wireless communication systems is to provide high-data-rate wireless
access with better QoS. Also, the fast shrinking spectrum for such communication
have necessitated the development of methods to increase spectral efficiency.
Multiple input multiple output (MIMO) wireless technology is a viable option in
such a situation and is likely to be able to meet the demands of these
ever-expanding mobile networks. Many researchers have explored this field over
a considerable period of time. A sizable portion of the research have been on
the application of traditional statistical methods in such areas. Over the
years, soft computational tools like artificial neural network (ANN), fuzzy
systems and their combinations have received attention in the diverse segments
of wireless communication. This is because of the fact that these are learning
based systems. These learn from the environment, retain the knowledge and use
it subsequently. This paper highlights some of the important application areas
in wireless communication which have reported the use of soft computing tools
in wireless communication that are in circulation in open literature.
Keywords
Multiple input multiple output (MIMO) technology· Soft computation·
Wireless communication· Multi layer perceptron (MLP)· Recurrent neural network
(RNN)· Fuzzy·Fuzzy-neural· Neuro-fuzzy
Introduction
The proliferation of mobile communication networks over
the last decade has increased the use of the wireless spectrum in exponential
terms. Increase in number of users in a limited spectrum have raised the levels
of inter symbol interference (ISI) and also have increased the possibility of
degraded quality of service (QoS).
Importance
of Soft Computing Tools in Wireless Communication
Soft computing tools like ANN, fuzzy systems and their
combinations have become important segments of systems related to wireless
communication. This is because of the fact that these being learning based
systems, are better placed to use channel side information (CSI) for improved
performance. ANNs have already received considerable attention as an optional technique
for equalization and other such applications in wireless communication. The most preferable aspects of the ANN in these
applications have been parallelism, adaptive processing, self-organization,
universal approximation and ability of tackling highly nonlinear problems.
Also, as the ANN learn complex patterns, it acts as a reliable estimator and
hence is used for the modeling a host of phenomena observed in wireless systems
and MIMO channels.
Application
of Feedforward ANN and MLP in Wireless Communication
1.
A
three layer ANN along with feedback is used for MIMO channel estimation and
equalization and is reported in. The work uses a Kalman filter and a feedforward
ANN to perform MIMO channel estimation.
2.
Another
work cited in reports the application of ANN for location estimation and CCI
suppression in cellular networks.
3.
A
work related to blind equalization of a noisy channel by linear ANN is reported
in.
4.
Another
work of similar nature is available as cited in where blind channel equalization and
estimation is performed using ANN.
5.
This
work discusses application of ANN mainly with a time in variant SISO channel. Along
with CCI cancelation and equalization, estimation of MIMO channels have also
received attention with regards to application of ANN and related tools.
Conclusion
Here, we have discussed about the application of soft
computing tools for wireless communication applications. We focussed on the use
of ANN in both feedforward and recurrent forms for dealing with a range of
issues like channel equalization and estimation, interference cancelation, user
identification etc. related to wireless communication. Fuzzy systems are able to
deal with uncertainty, hence are useful for dealing with the stochastic nature
observed in wireless channels. Fuzzy in combination of ANN form constitute a reliable
framework for application in wireless channel.
References
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