一种改进的动态自适应多用户检测算法

An Improved Dynamic Adaptive Multi-user Detection Algorithm

在大规模机器类通信(mMTC)上行免调度非正交多址接入(NOMA)系统中,活跃用户通常以高概率在几个连续时隙传输数据。为了充分利用相邻时隙之间的时间相关性,文章提出了一种改进的动态自适应(IDA)多用户检测算法,将压缩感知的重构思想结合时间相关性实现活跃用户和数据的联合检测。在估计支撑集过程中加入自适应阈值辅助策略选择原子,利用幂函数变步长方法提高稀疏度估计的准确性,并优化迭代终止条件。仿真结果表明,与贪婪重构算法和基于动态压缩感知(DCS)的多用户检测算法相比,在缺乏先验稀疏度条件下,IDA多用户检测算法表现出更优的信号检测性能。

In massive Machine Type Communications(mMTC)uplink grand-free Non Orthogonal Multiple Access(NOMA)systems,active users usually transmit data in consecutive time slots with high probability.In order to make full use of the time correlation between adjacent time slots,this paper proposes an Improved Dynamic Adaptive(IDA)multi-user detection algorithm,which combines the reconstruction idea of the existing compressed sensing algorithm with time correlation to achieve joint detection of active users and data.In the process of estimating the support set,an adaptive threshold-assisted strategy is added to select atoms,and the power function variable step size method is used to improve the accuracy of the sparsity estimation and optimize the iteration termination conditions.The simulation results show that compared with the greedy reconstruction algorithm and the Dynamic Compressed Sensing(DCS)algorithm,the IDA algorithm has better signal detection performance in the absence of a priori sparseness.