大规模MIMO系统中基于模等式约束的降维去相干DOA估计

Modulus Equation Constraints Based Decoherent Dimension Reduction 2-D DOA Estimation for Massive MIMO Systems

大规模MIMO系统中存在大量相干信号,简单的去相干处理导致二维波达方向(DOA, Direction of Arrival)估计的精度较低。为此,本文提出一种能够大幅度提高相干信号二维DOA估计性能的模等式约束降维MUSIC算法。该算法将二维DOA估计问题转化为优化问题,并采用模等式约束法定义附加条件,对方向矢量施加较强的约束,使优化方程求解更接近最优解。理论分析和仿真实验结果表明,本文提出的去相干DOA算法的可靠性与精度高,满足大规模MIMO系统的DOA估计性能需求,具有较强的可行性和实用性。

There are large numbers of coherent signals in massive MIMO system. Simple decoherence processing leads to the lower accuracy of the 2-D DOA (Two-Dimensional Direction of Arrival). This paper will propose a modulus equation constraints based dimension reduction MUSIC algorithm which can greatly improve the performance of two-dimensional DOA estimation of coherent signals. The algorithm transforms the two-dimensional DOA estimation problem into an optimization problem, and uses the modulus equation constraints to define the additional conditions and impose strong constraints on the direction vector so that the optimization equation is solved more close to the optimal solution. The results of theoretical analysis and simulation experiments show that the proposed DOA algorithm has high reliability and precision. Such algorithm is able to meet the requirements of DOA estimation performance in massive MIMO system, and can also provide high feasibility and practicability.