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Massive multiple input multiple output (MIMO) antenna arrays eventuate a hugeamount of circuit costs and computational complexity. To satisfy the needs ofhigh precision and low cost in future green wireless communication, theconventional Hybrid analog and digital MIMO receive structure emerges a naturalchoice. But it exists an issue of the phase ambiguity in direction of arrival(DOA) estimation and requires at least two time-slots to complete one-time DOAmeasurement with the first time-slot generating the set of candidate solutionsand the remaining ones to find a true direction by received beamforming overthis set. This will lead to a low time-efficiency. To address this problem, anew heterogeneous sub-connected hybrid analog and digital (HAD) MIMO structureis proposed with an intrinsic ability of removing phase ambiguity and acorresponding new framework is developed to implement a rapid high-precisionDOA estimation using only single time-slot. This framework consists of twosteps: 1) form a set of candidate solutions using existing methods like MUSIC)find the class of the true solutions and compute the class mean. To infer theset of true solutions, we propose two new clustering methods: weight globalminimum distance (WGMD) and weight local minimum distance (WLMD). And, we alsoenhance two classic clustering methods: accelerating local weighted k-means(ALW-K-means) and improved density. Additionally, the corresponding closed-formexpression of Cramer-Rao lower bound (CRLB) is derived. Simulation results showthat the proposed frameworks using the above four clustering can approach theCRLB at almost all SNR regions except for extremely low SNR. Four clusteringmethods have an accuracy decreasing order as follows: WGMD, improved DBSCAN,ALW-K-means and WLMD.

A New Heterogeneous Hybrid Massive MIMO Receiver with An Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning