Abstract:
Wireless communication systems are evolving with the passage of time and
resulted in the advanced communication systems such as mobile and Adhoc
communication systems, sensor networks etc. Two fundamental parameters of
their performance are higher data rates and better spectral efficiency. In order to
achieve high data rate and robust communication over wireless the most important
task to be performed at the receiver side is the channel equalization. The
transmitted data symbols when pass through the wireless channel suffer various
types of impairments, such as fading, Doppler shifts and Inter Symbol Interference
(ISI), degrade the overall performance of the communication system. In order to
mitigate the channel related impairments many channel equalization algorithms
have been proposed in the communication systems domain. These algorithms are
based on either the least squares methods or minimum mean square estimation
methods. Frequency domain equalization methods are also used for this purpose.
The channel equalization problem can also be solved as a classification problem
using Machine Learning (ML) methods. Many researchers have addressed this
problem however there is a need to compare the performance of the ML based
equalizers by using the already established communication systems criterion. In
this thesis the channel equalization has been performed using ML techniques and
their Bit Error Rate (BER) performance has been compared. Radial Basis
Functions (RBF), Multi Layer Perceptrons (MLP), Support Vector Machines
(SVM), and Polynomial based NN’s have been used for channel equalization. The
work is also extended to the multi-carrier systems where the channel equalization
of OFDM system is carried out using Long Short Term Memory (LSTM) method.
The simulation results show improved BER when compared with the LMS based
traditional channel equalization method. Further the computational complexity of
all the used ML algorithms has also been formulated theoretically and has been
verified with the help of simulations.
