| Author: | Chan, Chong-fun |
| Title: | Soft decision decoding using neural networks |
| Degree: | M.Sc. |
| Year: | 2000 |
| Subject: | Coding theory Decoders (Electronics) Neural networks (Computer science) Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies Department of Electronic and Information Engineering |
| Pages: | v, 92 leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | The advantage of soft decision decoding over the hard decision decoding is well known. Soft decision decoding of convolutional codes using Viterbi algorithm has low complexity, but it is not true for block codes. The neural network is used to aid the implementation of soft decision decoder of block codes. The concept and algorithm for training a network is presented. In this dissertation, a decoder which first implements the hard decision decoding of block codes, second, a procedure (called Decision Maker) tests to see if that result matches the result of soft decision decoding and third the neural network is used to implement the soft decision decoding if a match is not found. The advantage of the test procedure is that if the hard decision decoding result is good enough then the computation effort is greatly reduced. The generalization capability and storage capability of neural network are exploited to aid the soft decision decoder of block codes. Backpropagation networks are successfully trained for the binary Hamming (7,4) code and (9,5) code. Simulation results show that the performance of the decoder with Decision Maker and neural network approaches that of the maximum likelihood decoder over a wide range of signal to noise rations. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15383568.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.88 MB | Adobe PDF | View/Open |
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