自由空间光通信下的LSTM-SCFlips译码方法研究

LSTM-SCFlips Decoding Method in Free-Space Optical Communication

针对自由空间光通信下大气湍流引起极化码译码中错误比特难以定位的问题,提出一种自由空间光通信下的LSTM-SCFlips译码方法。首先将极化码串行抵消(SC)译码的对数似然比(LLR)信息序列进行one-hot预编码处理,在不同训练步长下,分析与学习极化码对数似然比信息序列的特征,综合考虑神经预测模型的均方根误差和计算复杂度,选取合适的训练步长,在提升预测结果精确度的基础上,进一步消除预测结果过拟合的现象。通过长短时记忆(LSTM)神经网络模型定位SC译码的第一个错误位时,按错误概率大小排序,依次进行SC译码算法的单比特或多比特翻转。仿真结果表明,在不同的大气弱湍流强度下,自由空间光通信下的LSTM-SCFlips译码方法在以牺牲少量计算资源为前提的情况下能更好地识别最优翻转位,降低计算复杂度,同时获得更好的误码率性能。当误码率为10-4时,LSTM-SCFlips译码方法最优翻转位的正确识别率被提高7个百分点,且产生了0.3 dB~1.2 dB的编码增益。

Because of the difficulty in locating error bits in polarization code decoding caused by atmospheric turbulence in free-space optical communication, this paper proposes an LSTM-SCFlips decoding method in free-space optical communication. First, one-hot precoding is performed on the log-likelihood ratio(LLR) information sequence of polarization codes in serial cancellation(SC) decoding, and the characteristics of the information sequence are analyzed and learned in different training step sizes. The root mean square error and computational complexity of the neural prediction model are comprehensively considered, and an appropriate training step size is selected. After the accuracy of the prediction results is improved, the phenomenon of overfitting the prediction results is further eliminated. The LSTM neural network model is used to locate the first error bit of SC decoding and single-bit or multi-bit flips of the SC decoding algorithm according to the error probability are performed. The simulation results show that in different weak atmospheric turbulence intensities, the proposed decoding method can better identify the optimal flip position and reduce the computational complexity on the premise of sacrificing few computing resources. In addition, better bit-error-rate performance is achieved. When the bit error rate is 10-4, the correct recognition rate of the optimal flipped bit of the LSTM-SCFlips decoding method is increased by 7 percentage points, and the coding gain of 0.3 dB--1.2 dB is generated.