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

TL;DR

This paper introduces a novel hybrid MIMO receiver that intrinsically removes phase ambiguity in DOA estimation, enabling rapid, high-precision direction finding with a single time-slot, reducing complexity and improving efficiency.

Contribution

The paper proposes a new heterogeneous HAD MIMO structure with an innovative framework and clustering methods for phase ambiguity removal and fast DOA estimation in one time-slot.

Findings

Approaches CRLB at most SNR levels in simulations.

Proposed clustering methods outperform traditional ones in accuracy.

Framework reduces DOA estimation time from multiple to single time-slot.

Abstract

Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional Hybrid analog and digital MIMO receive structure emerges a natural choice. 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 DOA measurement with the first time-slot generating the set of candidate solutions and the remaining ones to find a true direction by received beamforming over this set. This will lead to a low time-efficiency. To address this problem, a new heterogeneous sub-connected hybrid analog and digital (HAD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity and a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. This framework consists of two steps: 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 the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). And, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer-Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB at almost all SNR regions except for extremely low SNR. Four clustering methods have an accuracy decreasing order as follows: WGMD, improved DBSCAN, ALW-K-means and WLMD.