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To improve the accuracy of direction-of-arrival (DOA) estimation, a deeplearning (DL)-based method called CDAE-DNN is proposed for hybrid analog anddigital (HAD) massive MIMO receive array with overlapped subarray (OSA)architecture in this paper. In the proposed method, the sample covariancematrix (SCM) is first input to a convolution denoise autoencoder (CDAE) toremove the approximation error, then the output of CDAE is imported to afully-connected (FC) network to get the estimation result. Based on thesimulation results, the proposed CDAE-DNN has great performance advantages overtraditional MUSIC algorithm and CNN-based method, especially in the situationswith low signal to noise ratio (SNR) and low snapshot numbers. And the OSAarchitecture has also been shown to significantly improve the estimationaccuracy compared to non-overlapped subarray (NOSA) architecture. In addition,the Cramer-Rao lower bound (CRLB) for the HAD-OSA architecture is presented.

Deep Learning Based DOA Estimation for Hybrid Massive MIMO Receive Array with Overlapped Subarrays