上行免调度NOMA系统中基于遗传算法的扩频矩阵优化方法

A Genetic Algorithm Based Optimization Method for Spread Spectrum Matrix in Uplink Grant-Free NOMA System

为了提升基于压缩感知(Compressive Sensing,CS)框架下的免调度非正交多址接入(Non-Orthogonal Multiple Access,NOMA)系统的信道估计和多用户检测性能,本文提出了一种基于遗传算法(Genetic Algorithm,GA)的扩频矩阵优化方法。该方法以最小化扩频矩阵的互相关值为目标,提出一种遗传算法来解决从傅里叶变换方阵中抽取若干行作为NOMA系统扩频矩阵的组合优化问题。与解决同类问题的现有遗传算法相比,本文提出的遗传算法在个体构造上更加新颖,并且能够收敛于更小的扩频矩阵互相关值。仿真结果表明,在基于多重测量矢量CS框架的免调度NOMA系统中,与使用高斯随机矩阵作为扩频矩阵相比,使用本文优化方法获得的扩频矩阵能够使系统的误符号率平均降低52.14%;成功活跃检测率平均增加12.14%;信道估计均方误差降低约10 dB左右。

In order to improve the performances of channel estimation and multi-user detection in the grant-free nonorthogonal multiple access(NOMA)system based on the compressive sensing(CS)theory,a genetic algorithm(GA)based optimization method for spread spectrum matrix was proposed in this paper. In this method,a genetic algorithm was proposed to solve the combinatorial optimization problem to extract several rows from the Fourier transform matrix as the spreading spectrum matrix in NOMA system with the aim of minimizing the mutual coherence of the spreading spectrum matrix. As compared with the existing genetic algorithm that solved the similar problem,the genetic algorithm proposed in this paper was more novel in individual structure and could converge to a smaller mutual coherence of the spreading spectrum matrix. Simulation results show that in the grant-free NOMA system based on the multiple measurement vectorcompressive Sensing model,as compared with using a Gaussian random matrix as the spreading spectrum matrix,using the optimized matrix obtained by our proposed method can reduce the symbol error rate of the system by 52. 14% and increase the successful activity detection rate by 12. 14% on average. Apart from that,the reduction of the channel estimation mean square error is about 10 dB.