NOMA系统基于自适应匹配追踪算法的联合信道估计与多用户检测新方法

A New Method of Joint Channel Estimation and Multiuser Detection Based on Adaptive Matching Pursuit Algorithm in NOMA System

在免调度非正交多址接入(Non-Orthogonal Multiple Access,NOMA)系统中,针对基于帧的多用户传输场景的信道估计(Channel Estimation,CE)与用户的活跃和数据检测(Multiuser Detection,MUD)问题,本文在多重测量矢量压缩感知(Multiple Measurement Vector-Compressive Sensing,MMV-CS)框架下,提出了一种门限辅助的分布式弱选择分段自适应匹配追踪(Thresholod Aided-Distributed Weak Selection Stagewise Adaptive Matching Pursuit,TA-DWSStAMP)算法来联合解决CE和MUD问题。该算法在精确的迭代终止准则下,引入阶段标识,在大步长阶段设计了一种幂函数型的变步长方法。仿真结果表明,本文提出的算法能够在复杂度仅为现有算法10%的条件下,获得与现有算法相近的信道估计性能、用户成功活跃检测率和用户数据的误符号率。

For the channel estimation(CE)and the multiuser detection(MUD)with active user detection and data detection problems in the grant-free non-orthogonal multiple access(NOMA)system,this paper proposes a threshold aided-distributed weak selection stagewise adaptive matching pursuit(TA-DWSStAMP)algorithm to jointly solve the CE and MUD problems in the multiple measurement vector-compressive sensing(MMV-CS)model.The algorithm terminates at precise iterations under the criterion and introduces a new identification parameter.When the identification parameter represents a large step,a variable step size method based on power function is designed.Simulation results show that,as compared to the existing algorithm,the proposed TA-DWSStAMP algorithm can obtain similar successful activity detection rate of the users,symbol error rate of the user data and the normalized mean squared error performance of the channel estimation.However,its computational complexity only accounts for about 10%of the existing algorithm.