基于时间相关性的梯度追踪多用户检测算法

为降低大规模机器类型通信基站端多用户检测的复杂度, 结合活跃设备在相邻时隙间的相关性和梯度追踪算法提出了相关性辅助的梯度追踪多用户检测(correlation-assisted gradient pursuit multi-user detection, CAGP-MUD)算法, 不仅避免了矩阵求逆的过程, 还减少了除第一时隙的其他时隙的迭代次数。为了进一步降低多用户检测算法的复杂度, 在CAGP-MUD算法框架内引入决策衰弱的思想, 对梯度最大值进行衰弱, 并以此作为阈值, 每次迭代可挑选出多个活跃设备, 以减少迭代次数, 称为相关性辅助的组梯度追踪多用户检测算法。对提出的两种算法进行了复杂度计算。理论分析和仿真实验表明, 和同类算法相比, 这两种算法的计算消耗降低了60%以上。

Gradient pursuit multi-user detection algorithm based on time correlation

To reduce the complexity of multi-user detection at the base station in massive machine type communication, the correlation of active devices in adjacent slots and the gradient pursuit algorithm are used to propose a correlation-assisted gradient pursuit multi-user detection (CAGP-MUD) algorithm. The CAGP-MUD algorithm not only avoids the process of matrix inversion, but also reduces the number of iterations of other slots except the first slot. To reduce the multi-user detection complexity even further, the idea of stage wise weak is introduced into the framework of the CAGP-MUD algorithm, and a correlation-assisted group gradient pursuit multi-user detection algorithm is proposed. This algorithm uses the weakening of the maximum gradient as a threshold to select multiple active devices in each iteration, so that the iteration number can be reduced. Theoretical analysis and experimental results show that, compared with similar algorithms, the computational cost of these two algorithms is reduced by more than 60%.