针对大规模多输入多输出的动态调整排序串行干扰消除算法

Dynamically Adjusted Order Successive Interference Cancellation Algorithm for Massive Multiple Input Multiple Output

针对多输入多输出(MIMO)系统非线性检测中排序串行干扰消除(OSIC)算法信号检测性能弱的问题,提出了一种可动态调整的OSIC(D-OSIC)检测算法。为解决早期的误差传播问题,通过最大似然(ML)算法选择最佳符号,提高ML-D-OSIC算法的检测性能。根据遍历容量动态调节消除层的数目,并结合混合迭代算法降低算法的复杂度。仿真结果表明,ML-D-OSIC算法的信号检测性能明显优于OSIC算法,检测性能可通过调整预定义阈值、偏移量和权重而提升,且复杂度远低于ML算法。

Aiming at the low signal detection performance of the order successive interference cancellation(OSIC)algorithm in the nonlinear detection of multiple input multiple output(MIMO)systems,the paper proposes a dynamic adjusted OSIC(DOSIC)detection algorithm.In order to solve the problem of early error propagation,the maximum likelihood(ML)is used to select the best symbol which could improve the detection performance of the MLDOSIC algorithm.The number of elimination layers is dynamically adjusted due to the traversal capacity.Compared with the hybrid iterative algorithm,the complexity of the algorithm is reduced.The simulation results show that MLDOSIC algorithm is superior to the OSIC algorithm in signal detection performance.The detection performance improves with the difference of predefined threshold,offset and weight.The complexity is much lower than that of ML algorithm.