低复杂度的TBCC自适应循环VA译码算法

咬尾是一种将卷积码转换为块码的技术,它消除了归零状态所造成的码率损失,同时避免了截尾带来的性能降低,在短块编码中具有明显优势。针对咬尾卷积码(TBCC)现有译码算法复杂度过大和收敛性问题,提出一种低复杂度的TBCC自适应循环维特比(VA)译码算法。该算法根据信道变化自适应调整译码迭代次数,使咬尾路径收敛到最佳。通过仿真对比不同译码算法的块错误率和译码迭代次数,结果表明TBCC性能明显好于传统卷积码;相比于同类循环VA算法,在不降低性能的前提下,改进算法简化了停止规则,减少译码迭代次数和复杂度,在低信噪比时,改进算法比传统绕维特比译码算法(WAVA)平均迭代次数减少约4次。 

Low-complexity TBCC Adaptive Cyclic VA Decoding Algorithm

Tail-biting is a technique to convert convolutional codes into block codes. It eliminates the bit rate loss caused by the zero return state and avoids the performance degradation caused by tail-cutting. It has obvious advantages in short code transmission. Aiming at the complexity of the existing decoding algorithms of tail-biting convolutional code (TBCC) over large and convergent, a low complexity TBCC adaptive cyclic Viterbi (VA) decoding algorithm is proposed. The algorithm adjusts the number of iterations adaptively according to the change of the channel so that the tail-biting path converges to the best. By comparing the block error rate and decoding iteration times of different decoding algorithms, the simulation results show that the performance of TBCC is obviously better than traditional convolutional codes. Compared with the similar cyclic VA algorithm, the improved algorithm simplifies the stop rule and reduces the number and complexity of decoding iteration without reducing the performance. At low SNR, the average number of iterations of the improved algorithm is reduced by about 4 times compared with the traditional wrap-around Viterbi decoding algorithm (WAVA).