Massive multiple-input multiple-output (MIMO) systems under time division duplexing (TDD), assume reciprocity between the uplink and downlink channels making it essential for the base station (BS) to estimate two-dimensional direction-of-arrival (2D-DOA) for downlink operations. Owning to the large dimension, computational complexity becomes a major concern for these systems in addition to the estimation accuracy. Matrix and tensor-based subspace schemes provide high accuracy estimates but offer tremendous computational load to perform singular value decomposition (SVD), root computations and/or peak search. Keeping in view, we propose a novel higher-order propagator method (HOPM) for DOA estimation which benefits from the inherent multidimensional structure and enhances the estimation accuracy with an added advantage of inexpensive computability over SVD. Moreover, we also developed a higher-order unitary PM (HOUPM) which provided further performance improvement at a much lower computational requirement. Numerical simulations are included to verify the effectiveness of proposed methods.

中文翻译：

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用于大规模 MIMO 系统中 2D-DOA 估计的高阶传播器方法

时分双工 (TDD) 下的大规模多输入多输出 (MIMO) 系统假设上行链路和下行链路信道之间存在互易性，这使得基站 (BS) 估计二维到达方向 (2D- DOA) 用于下行链路操作。由于维度较大，除了估计精度之外，计算复杂度成为这些系统的主要关注点。基于矩阵和张量的子空间方案提供高精度估计，但提供巨大的计算负载来执行奇异值分解 (SVD)、根计算和/或峰值搜索。保持观望，我们提出了一种用于 DOA 估计的新型高阶传播器方法（HOPM），该方法受益于固有的多维结构，并通过与 SVD 相比具有廉价可计算性的附加优势来提高估计精度。此外，我们还开发了一种高阶酉 PM（HOUPM），它以低得多的计算要求提供了进一步的性能改进。包括数值模拟以验证所提出方法的有效性。