摘要
针对中短码长下串行抵消(SC)算法性能较差,且串行抵消列表(SCL)算法复杂度较高等问题,根据译码纠错空间理论,该文提出了一种基于卷积神经网络(CNN)扰动的极化码译码算法。对SC译码失败的接收序列,通过CNN产生相应的扰动噪声,并将该扰动噪声添加到接收信号中,然后根据重新计算的似然信息进行译码。仿真结果表明:与SC译码算法相比,所提出的算法约有0.6 dB的增益,与SCL(L=16)译码算法相比,该算法约有0.1 dB的提升,且平均复杂度更低。
According to the space theory for error correction,a Polar decoding algorithm for medium and short code lengths,based on the perturbation with a Convolution Neural Network(CNN),is presented to overcome the poor performance of the Successive Cancellation(SC)decoding algorithm and the high complexity of the Successive Cancellation List(SCL)decoding algorithm.For any receiving signals that failing to decode,a perturbation noise,generated through the CNN,is added to the receiving signal,and the likelihood information is then recalculated for further decoding.The simulation results show that the proposed algorithm has a gain of about 0.6 dB compared with the SC decoding algorithm,and an improvement of about 0.1 dB and a lower average complexity than that of SCL decoding algorithm when L=16.
作者
赵生妹
徐鹏
张南
孔令军
ZHAO Shengmei;XU Peng;ZHANG Nan;KONG Lingjun(Institute of Signal Processing&Transmission,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;China Aerospace Academy of Systems Science and Engineering,Beijing 100048,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2021年第7期1900-1906,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61871234,11847062)
中国博士后科学基金(2020M671595)
江苏省博士后科研资助计划(2020Z198)
南京邮电大学国自孵化基金(NY219075)。
关键词
极化码
串行抵消译码
扰动噪声
卷积神经网络
Polar code
Successive Cancellation(SC)decoding
Perturbation noise
Convolutional Neural Network(CNN)
