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

TL;DR

This paper introduces a deep learning approach called CDAE-DNN for accurate DOA estimation in hybrid massive MIMO systems with overlapped subarrays, outperforming traditional methods especially in noisy conditions.

Contribution

It proposes a novel deep learning framework combining CDAE and DNN for DOA estimation in HAD massive MIMO with OSA architecture, demonstrating superior performance over existing algorithms.

Findings

CDAE-DNN outperforms MUSIC and CNN methods at low SNR and snapshot counts.

Overlapped subarray architecture significantly improves estimation accuracy.

CRLB analysis provided for the HAD-OSA system.

Abstract

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