摘要
讨论了线性分组码互补码的特性及分解，提出了基于神经网络的互补码分解译码方案，此方案利用神经网络的吸引子和吸引域进行纠错译码，并把互补码分解为子码及其若干陪集，更进一步减小神经网络译码的复杂性及规模，从而实现高效实时硬判决译码．给出了实现原理及步骤，并对其译码性能进行了分析比较．
After characteristics and decomposition of complementary codes are discussed, A decomposition decoding strategy for complementary codes based on neural network is presented. The attractors of neural network are introduced to implement error correction decoding. The complementary codes are decomposed into a subcode and several cosets of the subcode, which can further decrease complexity of decoding using neural networks, carrying out high efficiency decoding in real time. The paper demonstrates the principle and the procedure of the decoding structure, and analysis the performance of the decoding.
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
1998年第2期46-50,共5页
Journal of Beijing University of Posts and Telecommunications
基金
国家自然科学基金
关键词
信道译码
纠错编码
分组码
神经网络
channel decoding
error correcting coding
block codes
neural networks
