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http://hdl.handle.net/2080/690
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| DC Field | Value | Language |
|---|
| contributor.author | Das, Susmita | - |
| contributor.author | Satapathy, J K (Guide) | - |
| date.accessioned | 2008-05-23T10:05:38Z | - |
| date.available | 2008-05-23T10:05:38Z | - |
| date.issued | 2004 | - |
| identifier.citation | Adaptive Equalisation of Communication Channels Using ANN Techniques, Thesis submitted in partial fulfillment of the requirements for the award of the Doctor of Philosophy in Electrical Engineering, Submitted to National Institute of Technology, Rourkela | en |
| identifier.uri | http://hdl.handle.net/2080/690 | - |
| description | Copyright for the thesis belongs to National Institute of Technology Rourkela | en |
| description.abstract | Channel equalisation is a process of compensating the disruptive effects caused
mainly by Inter Symbol Interference in a band-limited channel and plays a vital role for
enabling higher data rate in digital communication. The development of new training
algorithms, structures and the selection of the design parameters for equalisers are active
fields of research which are exploiting the benefits of different signal processing
techniques. Designing efficient equalisers based on low structural complexity, is also an
area of much interest keeping in view of real-time implementation issue. However, it has
been widely reported that optimal performance can only be realised using nonlinear
equalisers. As Artificial Neural Networks are inherently nonlinear processing elements
and possess capabilities of universal approximation and pattern classification, these are well suited for developing high performance adaptive equalisers.
This proposed work has significantly contributed to the development of novel
equaliser structures with reduced structural complexity in the neural network paradigm
based on both the feedforward neural network (FNN) and the recurrent neural network
(RNN) topologies. Various innovative techniques like hierarchical knowledge
reinforcement, genetic evolutionary concept, transform domain based approach and
sigmoid slope tuning using fuzzy logic approach have been incorporated into an FNN
framework to design highly efficient equaliser structures. Subsequently, novel hybrid
configurations using cascaded modules of RNN and FNN have also been proposed in this
thesis work. Further, suitable modifications in the Back-Propagation and Real-Time-
Recurrent-Learning algorithms have been incorporated to update the connection weights
of the proposed structures. Significant performance improvement over the conventional
FNN and RNN based equalisers, faster adaptation and ease of implementation in realtime
applications are the major advantages of the proposed neural equalisers. Exhaustive
simulation studies carried out on various linear and nonlinear channels verify the efficacy
of the proposed neural equalisers.
Further, all the proposed FNN based equalisers are of decision feedback type as
inclusion of this technique significantly improves the performance alongwith a
considerable reduction in the structural complexity. A proper selection of feedforward
order, decision delay and feedback order is a challenging task in such equalisers as these
key design parameters play a crucial role for an impressive performance. A detailed study
of various factors influencing the bit error rate performance of optimal Bayesian equaliser
has been undertaken in the present work. This study has given an insight for proposing of
a novel approach in the parameter selection issue, which can eliminate the use of
cumbersome procedure of determining these design parameters from graphical analysis.
Thus a major breakthrough has been achieved in successfully evaluating these parameters
of the equaliser structure. The new methodology and its logical interpretation that led to
the development of some empirical relationships have emerged as a powerful tool for
selecting the key design parameters directly from the channel characteristics. | en |
| format.extent | 5463678 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | National Institute of Technology, Rourkela | en |
| subject | Adaptive Equalisation | en |
| subject | Inter Symbol Interference | en |
| subject | Communication Channel | en |
| subject | Decision Feedback Equaliser | en |
| subject | Nonlinear Equalisers | en |
| subject | Feedforward Neural Network | en |
| title | Adaptive Equalisation of Communication Channels Using ANN Techniques | en |
| type | Thesis | en |
| Appears in Collections: | Thesis (Doctor of Philosophy)
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| Final-thesis-susmita dada.pdf | | 5335Kb | Adobe PDF | View/Open |
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