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Hamming Code Performance Evaluation using Artificial Neural Network Decoder

机译:使用人工神经网络解码器的汉明码评估

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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.
机译:随着各种电子设备之间的连通性的增加,该技术已经进入了一个新的互联网的时代。为了确保传输数据中的准确性,完整性和容错,使用纠错码。可以使用各种技术来解码接收的数据并纠正错误。在本文中,基于人工神经网络(ANN)的方法被用来解码所接收的数据,因为它们的实时操作,自组织和自适应学习。使用MATLAB(7,4)汉明代码模拟了对馈送转发ANN的后传播算法。在每个训练周期期间更新突触权重。设计的ANN培训,以获取所有可能的代码词组合,使得它可以检测和校正1位误差。将所提出的ANN基方法的误码率性能与综合征解码进行比较。

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