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

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