一种基于互信息最大化的Raptor码优化设计方法

Raptor码通过级联预编码可以很好地改善错误平层问题,但编译码的复杂度相对较高。为了降低级联Raptor码的复杂度,提出一种基于互信息最大化的Raptor码优化设计方法。该方法基于外部信息转移图的渐进收敛分析,从最大化互信息的角度出发,得到有限迭代次数约束下的校验节点度分布的数学模型。仿真结果表明,当译码迭代次数一定时,基于互信息最大化设计的Raptor码与码率最大化设计的Raptor码相比,具有更好的误码性能。

An optimal design method of Raptor code based on mutual information maximization

Raptor code can improve the problem of error leveling by cascading precoding, but the complexity of coding is relatively high. In order to reduce the complexity of concatenated Raptor codes, this paper proposes an optimal design method based on mutual information maximization. This method is based on the gradual convergence analysis of the external information transfer graph. From the perspective of maximizing mutual information, the mathematical model of the degree distribution of check nodes under the constraint of the limited number of iterations is obtained. The simulation results show that when the number of decoding iterations is fixed, the designed Raptor code based on mutual information maximization has better BER performance than the designed Raptor code based on rate maximization.