Neural network error correcting decoders for block and convolutional codes
Neural network error correcting decoders for block and convolutional codes
复制标题
用于块码和卷积码的神经网络纠错解码器
DOI：
10.1109/glocom.1990.116658
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发表时间：
1990
期刊：
影响因子：
--
通讯作者：
R. W. Means
中科院分区：
文献类型：
--
作者：
W. R. Caid;R. W. Means
使用神经网络作为纠错解码器进行说明。结果表明，神经网络可以提供的优势，在电子对抗（ECM）的环境中，其中的卷积设计假设的加性白色高斯噪声（AWGN）和二进制对称信道（BSC）被违反。初步研究的一些结果和基于神经的解码器的方法的好处进行了讨论。<<ETX>>
The use of neural networks as error correcting decoders is described. It is shown that the neural networks may offer advantages in electronic countermeasure (ECM) environments in which the convolutional design assumptions of additive white Gaussian noise (AWGN) and a binary symmetric channel (BSC) are violated. Some results of preliminary studies and benefits of the neural-based decoder approach are discussed.<<ETX>>