大规模MIMO系统中低复杂度信号检测算法

Low Complexity Signal Detection Algorithms for Massive MIMO Systems

第五代(5G)移动通信系统大规模多输入多输出(MIMO)技术利用信道硬化理论,采用消息传递检测(MPD)算法实现了良好的检测性能。但是由于MPD算法的计算复杂度随调制阶数和用户天线数的增加而增加,在实际的大规模MIMO系统中难以有效实现。概率近似-消息传递检测(PA-MPD)算法可以减少原始MPD算法的计算复杂度。文章将提前终止迭代过程和更新部分符号概率的策略应用于MPD算法迭代过程中,提出了一种选择性更新-消息传递检测(SU-MPD)算法,从而降低了MPD算法的计算复杂度。仿真结果表明,在各种天线配置下SU-MPD算法的计算复杂度最大限度可降低为MPD算法的19%和PA-MPD算法的50%,且不降低算法的检测性能。

Massive Multiple-Input Multiple-Output(MIMO) has been identified as a key technology for the upcoming Fifth Generation(5 G) wireless communication systems. The original Message Passing Detection(MPD) algorithm utilizes channel hardening theory to achieve good detection performance in large-scale MIMO systems. However, the computational complexity of the MPD algorithm increases with the increase of the modulation order and the number of user antennas, which is difficult to implement effectively in an actual large-scale MIMO system. The Probabilistic Approximation-MPD(PA-MPD) algorithm can reduce the computational complexity of the original MPD algorithm. In this paper, the iterative process of terminating the iterative process and updating the partial symbol probability is applied in the iterative process of the MPD algorithm, a Selective Update-MPD(SU-MPD) algorithm is proposed, which reduces the computational complexity of the MPD algorithm. The simulation results show that the computational complexity of SU-MPD algorithm can be reduced to 19% of MPD algorithm and 50% of PA-MPD algorithm under various antenna configurations, without reducing the detection performance of the algorithm.