Abstract

The paper investigates principles of work and BER characteristics of a new soft decision algorithm for decoding convolutional codes that is based on neural network applications. The novelty of the algorithm is in its capability to generate soft output estimates of the message bits encoded. For this purpose the noise energy function, which is defined and used for the neural network decoding of convolutional codes, has been related to the well known log-likelihood function and the soft decision decoding rule has been defined and derived. The BER curves are obtained for this novel algorithm and then compared to the curve obtained by the Viterbi algorithm and the gradient descent algorithm. Based on the theoretical model a simulator of coding communication system has been developed and used to confirm theoretically expected results. It was found that the performances of the proposed soft decision decoder are comparable or better than the performances of the recurrent neural network decoder and decoders based on the Viterbi algorithm.

Keywords: Neural networks convolutional decoder; Soft output decoding; Noise energy function

Article Outline

1. Introduction

2. Theoretical background

2.1. Encoder operation

2.2. Transmission channel characterization

2.3. Decoding procedure

3. Development of soft decision neural network algorithm (SONNA)

4. Practical investigations and results

4.1. Demonstration of the basic algorithm on a numerical example

4.2. Simulation of the basic algorithm

5. Conclusions

Appendix A. Viterbi algorithm

Appendix B. Complexity of Computations

References