基于查找表的ADMM译码算法中量化算法优化研究

Study on Optimization of Quantization Algorithm in ADMM Decoding Algorithm Based on Lookup Table

在基于ADMM的线性规划译码中,待投影向量向校验多胞体进行欧几里得投影计算是最复杂和耗时的部分。基于查找表的ADMM-LDPC译码算法通过简单的查表操作来替代复杂的投影运算,简化了投影过程,提升了算法的效率,但消耗了大量的内存资源。之后研究者提出了非均匀量化方法,该方法虽然极大地减少了内存消耗,但是所采用的量化方案的计算复杂度较高,从而使得该方法在量化段数较多的条件下难以实现。针对该问题,文中提出了一种新的非均匀量化方法。首先,针对不同的码字,在不同信噪比条件下,通过实验统计待投影向量中元素的分布特性,探究其分布规律,并设计相应的函数作为量化映射关系;然后,采用差分进化算法对函数的参数进行优化,从而得出在该函数下的最优量化方案,最终确定量化函数。仿真实验表明:与已有的量化方法相比,文中设计的非均匀量化方法具有不受量化段数、精度等因素影响的优点;且针对不同的码字,所提方法在高信噪比下均能达到0.05 dB左右的性能增益。

In the linear programming decoding based on ADMM,the Euclidean projection calculation of the projection vector to the check multi-cellular body is the most complex and time-consuming part.The ADMM-LDPC decoding algorithm based on look-up tables replace the time complexity of algorithm with simple table look-up operations to simplify the projection process and improve the efficiency of the algorithm,however it expends much memory consumption.La-ter,the researchers proposed the nonuniform quantization method,which can decrease the memory consumption effectively,but the computation complexity of the quantitative method is too high,so that the way is difficult to achieve when the number of segments is too much.For this problem,this paper proposed a new nonuniform quantization method.Firstly,for different code-words,the distribution characteristics of elements in the vector are calculated to be projected under different SNRs conditions by experiment,its distribution rules are explored and the corresponding function is designed as the quantitative mapping relation.Then,differential evolution algorithm is used to optimize the parameters of the function,the optimal quantization schema under the function is obtained,and the quantization function is finally determined.The simulation results show that,compared with the existing quantitative methods,the nonuniform method proposed in this paper has the advantage of not being affected by the number of quantization segment,precision and other factors.What’s more,it achieves about 0.05 dB performance improvement for different code words in high SNRs.