Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks

TL;DR

This paper introduces a neural network-based detection method and a novel two-layer HAD-MIMO structure to improve DOA estimation accuracy and resolve phase ambiguity in massive hybrid analog-digital MIMO systems, approaching fully digital performance.

Contribution

It proposes a MLNN detector for emitter detection and a TLHAD structure to eliminate phase ambiguity using only one snapshot, advancing high-precision DOA estimation in HAD-MIMO systems.

Findings

MLNN detector outperforms existing methods in detection probability.

TLHAD structure achieves CRLB with a single snapshot.

Proposed methods enhance DOA estimation accuracy in massive HAD-MIMO.

Abstract

Due to a high spatial angle resolution and low circuit cost of massive hybrid analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as a valuable green communication technology for future wireless networks. Combining a massive HAD-MIMO with direction of arrival (DOA) will provide a high-precision even ultra-high-precision DOA measurement performance approaching the fully-digital (FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO DOA estimation. In this paper, we review three aspects: detection, estimation, and Cramer-Rao lower bound (CRLB) with low-resolution ADCs at receiver. First, a multi-layer-neural-network (MLNN) detector is proposed to infer the existence of passive emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed to eliminate phase ambiguity using only one-snapshot. Simulation results show that the proposed MLNN detector is much better than both the existing generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection probability. Additionally, the proposed TLHAD structure can achieve the corresponding CRLB using single snapshot.