低复杂度的大规模MIMO上行链路软输出信号检测

针对信道矩阵维度高以及接收信号复杂的情况,提出了一种适用于大规模MIMO系统上行链路信号检测的混合迭代算法,即结合自适应阻尼雅克比(damped Jacobi,DJ)算法和共轭梯度(conjugate gradient,CG)算法。首先利用CG算法为自适应阻尼雅克比迭代算法提供有效的搜索方向;随后提出切比雪夫方法消除松弛参数对信号检测的影响,在降低算法复杂度的同时加快收敛速度;最后,利用信道编译码中的比特似然比近似求解软信息,以提升检测性能。通过理论分析算法的复杂度,仿真在不同判决方式下对不同检测算法进行误码率对比,并对混合迭代算法的收敛进行了分析。仿真结果表明,混合迭代算法在少量迭代次数下快速收敛并近似达到最佳MMSE检测性能,且算法复杂度远低于MMSE算法。

Low-complexity soft-output signal detection on uplink for large-scale MIMO

Based on the high dimension of the channel matrix and the complexity of the received signals,this paper proposed a hybrid iterative algorithm used to signal detection on uplink for large-scale MIMO systems,which combined adaptive damped Jacobi(DJ)algorithm and conjugate gradient(CG)algorithm.Firstly,this paper used CG algorithm to provide effective search direction for adaptive damped Jacobian iterative algorithm.Then,it proposed Chebyshev method to eliminate the influence of relaxation parameters on signal detection to reduce the complexity of the algorithm and accelerate the convergence speed.Finally,it used the bit likelihood ratio in channel coding and decoding to solve the soft information approximately,which could improve detection performance.This paper analyzed the complexity of algorithm quantitatively in the theory,and studied the bit error rate performances with different decision methods and analyzed convergence of hybrid iterative algorithm by simulation experiments.The simulation results show that the hybrid iterative algorithm converges quickly and approximately achieves the best MMSE detection performance under a small number of iterations,and the algorithm’s complexity is far lower than that of MMSE algorithm.