【摘要】 为降低低密度奇偶校验(LDPC:Low-Density Parity-Cheek)码译码算法的复杂度,同时提高通信系统性能,在研究对数似然比置信传播(LLR-BP:Log-Likelihood Ratio Belief Propagation)算法及其简化的译码算法基础上,提出了一种基于归一化最小和(NMS:Normalized Min Sum)算法的改进算法。深入研究了Gamma-Gamma分布模型,并将Gamma-Gamma分布作为仿真的信道模型,应用Matlab仿真比较了LLR-BP及其简化算法在不同湍流强度信道下的性能。仿真结果表明,无论在AWGN(Additive White Gaussian Noise)信道还是不同湍流强度的Gamma-Gamma信道下,改进算法在几种算法中都有较好的译码性能,尤其是在中湍流和强湍流信道下改进算法的性能优势更为明显。通过码长对BER(Bit Error Rate)影响的仿真结果表明,适当增加码长可有效改善算法的性能。更多还原

【Abstract】 In order to reduce the LDPC( Low-Density Parity-Cheek) complexity of decoding algorithm,studied the LLR BP( Log-Likelihood Ratio Belief Propagation algorithm) and its simplified decoding algorithm,and an improved algorithm based on NMS( Normalized Min Sum) algorithm is proposed; studied the Gamma-Gamma distribution model and Gamma-Gamma distribution as the channel model are studied. The performance of LLRBP and its simplified algorithm under different turbulence intensities are compared with Matlab,the simulation results show that both in the AWGN( Additive White Gaussian Noise) or different turbulence intensity GammaGamma channel,the improved algorithm has better performance in decoding algorithms in particular,the performance advantages of the improved algorithm in strong turbulence is more obvious. The simulation results show that the increase of the code length can improve the performance of the BER( Bit Error Rate).更多还原