Hamming code performance evaluation using artificial neural network decoder

Aldrin Claytus Vaz, C. Gurudas Nayak, Dayananda Nayak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

With the increase in the connectivity among various electronic devices day-by-day, the technology has stepped up into a new era of Internet-of-Things. To ensure the accuracy, integrity and fault-tolerance in the transmitted data, Error Correcting Codes are used. Various techniques are available to decode the received data and correct the errors. In this paper, an approach based on Artificial Neural Networks (ANN) is been used to decode the received data because of their real-time operation, self-organization and adaptive learning. Back propagation Algorithm for feed forward ANN has been simulated using MATLAB for (7, 4) Hamming Code. The synaptic weights are updated during each training cycle. The designed ANN is trained for all possible combination of code words such that it can detect and correct 1-bit error. The Bit Error rate performance of the proposed ANN based method is compared with the syndrome decoding.

Original languageEnglish
Title of host publication2019 15th International Conference on Engineering of Modern Electric Systems, EMES 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-40
Number of pages4
ISBN (Electronic)9781728107738
DOIs
Publication statusPublished - 06-2019
Event15th International Conference on Engineering of Modern Electric Systems, EMES 2019 - Oradea, Romania
Duration: 13-06-201914-06-2019

Publication series

Name2019 15th International Conference on Engineering of Modern Electric Systems, EMES 2019

Conference

Conference15th International Conference on Engineering of Modern Electric Systems, EMES 2019
Country/TerritoryRomania
CityOradea
Period13-06-1914-06-19

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Hardware and Architecture
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Waste Management and Disposal
  • Instrumentation

Fingerprint

Dive into the research topics of 'Hamming code performance evaluation using artificial neural network decoder'. Together they form a unique fingerprint.

Cite this