自由空间光通信中基于Q学习算法的Raptor10码译码策略

Q-Learning-Based Raptor10 Code Decoding Strategy in Free-Space Optical Communication

为在保障自由空间光(FSO)通信质量的同时,缩短传统的自动重传请求技术所需等待时间、减小缓存资源消耗,并随信道的变化适配数字喷泉码冗余符号的数量,提出一种基于Q学习算法的Raptor10码译码策略。该策略采用以减少冗余符号和降低误码率为联合回报目标的Q学习算法,可对接收端所接收的Raptor10码冗余符号进行动态调整,并且随着译码经验的不断累积可自动提升通信性能。仿真结果表明,在弱、中、强3种湍流条件下,与传统的采用固定冗余度Raptor10码译码策略和线性滤波调整算法相比,所提方法可有效减少3%的平均冗余符号数量。

In order to reduce the waiting time and buffer resource consumption of conventional automatic repeat request technology, make the redundant symbol number of digital fountain codes adapt to the varying of channels, and guarantee the quality of the free-space optical communication at the same time, a Q-learning-based Raptor10 code decoding strategy is proposed. Reducing redundant symbols and decoding bit error rate are as the joint return target in the proposed strategy, and Raptor10 code redundant symbols received by receiver are dynamically adapted. As the decoding experience is accumulated continually, the communication performance is improved automatically. Simulation results show that, under the weak, moderate and strong turbulence conditions, the proposed strategy reduces 3 % average redundant symbols compared with conventional Raptor10 codes with fixed redundancy rate and linear filtering adaption algorithm.