Open AccessError Detection in Turbo Decoding using Neural Network
Author(s) -
Ms.S. Bhavanisankari,
Ms.G.T. Bharathy,
M. Tamilselvi
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1039.1091s19
Subject(s) - computer science , turbo code , turbo equalizer , decoding methods , bit error rate , turbo , serial concatenated convolutional codes , algorithm , encoder , soft decision decoder , encoding (memory) , word error rate , error detection and correction , decodes , artificial neural network , speech recognition , concatenated error correction code , artificial intelligence , block code , engineering , automotive engineering , operating system
In this paper reduction of errors in turbo decoding is done using neural network. Turbo codes was one of the first thriving attempt for obtaining error correcting performance in the vicinity of the theoretical Shannon bound of –1.6 db. Parallel concatenated encoding and iterative decoding are the two techniques available for constructing turbo codes. Decrease in Eb/No necessary to get a desired bit-error rate (BER) is achieved for every iteration in turbo decoding. But the improvement in Eb/No decreases for each iteration. From the turbo encoder, the output is taken and this is added with noise, when transmitting through the channel. The noisy data is fed as an input to the neural network. The neural network is trained for getting the desired target. The desired target is the encoded data. The turbo decoder decodes the output of neural network. The neural network help to reduce the number of errors. Bit error rate of turbo decoder trained using neural network is less than the bit error rate of turbo decoder without training.