摘要
针对现有基于校验关系的Turbo码交织器识别算法在低信噪比(Signal-to-Noise Ratio,SNR)时适应性较弱以及识别过程中容易出现差错传播的问题,为了进一步提升交织识别的容错性与实时性,在基于最大序列相关识别算法的基础上加入迭代译码和小范围遍历两种纠错算法,更加充分的利用了接收数据的同时增强了抗误码能力.仿真结果表明,加入纠错算法使得原有算法的交织识别性能明显改善,相同条件下完成全部交织参数识别所需数据量仅需原有的1/3;在相同数据量的条件下实现全部交织参数识别时的SNR增益大于2dB.此外,基于译码纠错的方法具有通用性,同样适用于其他基于校验关系的识别算法.
The existing algorithms based on parity-check relationship in the recognition of interleaver for Turbo codes have weak adaptability at low signal-to-noise ratio(SNR)and are prone to"error propagation"in the recognition process,In order to further improve the fault tolerance and real time of interleaving recognition,we propose two error correction algorithms based on the maximum sequence correlation recognition algorithm,including iterative decoding and small traversal,which make full use of the received data and enhance the error resilience.Simulation results show that the performance of the improved algorithm is obviously better than the original algorithm by adding error correction algorithm,and it only needs 1/3 of the data to complete the identification of all the interleaved parameters under the same conditions and has more than 2dB gain to achieve the identification of all the interleaved parameters under the condition of the same amount of data.In addition,the method based on decoding error correction is universal,which is also applied to other recognition algorithms of parity-check relationship.
作者
李卓伦
韩卓茜
LI Zhuo-lun;HAN Zhuo-xi(PLA Strategic Support Force Information Engineering University,Zhengzhou,Henan 450001,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2021年第2期239-247,共9页
Acta Electronica Sinica
关键词
TURBO码
随机交织
最大互相关
译码
纠错
Turbo-code
random interweaving
maximum sequence correlation
decoding
error correction
