基于压缩感知理论的大规模MIMO系统下行信道估计中的导频优化理论分析与算法设计

针对大规模多输入多输出(multiple input multiple output, MIMO)系统信道估计中的导频设计问题,在压缩感知理论框架下,提出了一种基于信道重构错误率最小化的自适应自相关矩阵缩减参数导频优化算法.首先以信道重构错误率最小化为目标,推导了正交匹配追踪(orthogonal matching pursuit, OMP)算法下信道重构错误率与导频矩阵列相关性之间的关系,并得出优化导频矩阵的两点准则,即导频矩阵列相关性期望和方差最小化;然后研究了优化导频矩阵的方法,并提出相应的自适应自相关矩阵缩减参数导频矩阵优化算法,即在每次迭代过程中,以待优化矩阵平均列相关程度是否减小作为判断条件,调整自相关矩阵缩减参数值,使参数不断趋近于理论最优.仿真结果表明,与采用Gaussian矩阵、Elad方法、低幂平均列相关方法得到的导频矩阵相比,本文所提方法具有更好的列相关性,且具有更低的信道重构错误率.

Theoretical analysis and algorithm design of optimized pilot for downlink channel estimation in massive MIMO systems based on compressed sensing

Aiming at the pilot design problem in channel estimation of large-scale multiple input multiple output(MIMO)systems,an adaptive autocorrelation matrix reduction parameter pilot optimization algorithm based on channel reconstruction error rate minimization is proposed under the framework of compression perception theory.Firstly,the system model and orthogonal matching pursuit(OMP)algorithm are introduced.Secondly,for minimizing the channel reconstruction error rate,the relation between the expected value of the correlation decision in each iteration of the OMP algorithm and the reconstruction error rate is analyzed.For the optimal expected value of the correlation decision,the relation between the channel reconstruction error rate and the correlation of the pilot matrix column under the OMP algorithm is derived,and the two criteria of optimizing the pilot matrix are obtained:the pilot matrix column correlation expectation and the variance minimization.Then the method of optimizing the pilot matrix is studied,and the corresponding adaptive autocorrelation matrix reduction parameter pilot matrix optimization algorithm is proposed.In each iteration,whether the average column correlation degree of the matrix to be optimized is reduced is used as a judgment condition.The autocorrelation matrix reduction parameter value is adjusted to make the parameters close to the theoretical optimization.The simulation results show that the proposed method has a better column correlation property and lower channel reconstruction error rate than the pilot matrix obtained,separately,by Gaussian matrix,Elad method and low power average column correlation method.