A Novel Tree Model-based DNN to Achieve a High-Resolution DOA Estimation via Massive MIMO receive array

To satisfy the high-resolution requirements of direction-of-arrival (DOA) estimation, conventional deep neural network (DNN)-based methods using grid idea need to significantly increase the number of output classifications and also produce a huge high model complexity. To address this problem, a multi-level tree-based DNN model (TDNN) is proposed as an alternative, where each level takes small-scale multi-layer neural networks (MLNNs) as nodes to divide the target angular interval into multiple sub-intervals, and each output class is associated to a MLNN at the next level. Then the number of MLNNs is gradually increasing from the first level to the last level, and so increasing the depth of tree will dramatically raise the number of output classes to improve the estimation accuracy. More importantly, this network is extended to make a multi-emitter DOA estimation. Simulation results show that the proposed TDNN performs much better than conventional DNN and root-MUSIC at extremely low signal-to-noise ratio (SNR), and can achieve Cramer-Rao lower bound (CRLB). Additionally, in the multi-emitter scenario, the proposed Q-TDNN has also made a substantial performance enhancement over DNN and Root-MUSIC, and this gain grows as the number of emitters increases.

High-Resolution DOA Estimation via a Novel Tree Model-based Deep Neural Network

Traditional deep neural networks (DNNs) have bad performance on estimating off-grid angles, and the most direct solution is to increase the number of output classes for improving angular resolution. But more output classes can weaken the model accuracy of DNNs and thus decreasing the direction-of-arrival (DOA) estimation accuracy. In this work, a tree-model based deep neural networks (TDNN) is proposed, which contains H layers and each layer is consist of multiple small-scale DNNs. From the first layer to the last layer of TDNN, the angular region is gradually divided into smaller subregions by these DNNs, and the estimated DOA is finally obtained by cumulative calculating the classification results of all the layers. TDNN can improve the angular resolution by increasing the number of layers or the number of DNNs in any layer instead of changing the structure of single DNN, so the model accuracy of TDNN will not decrease with the improvement of angular resolution and its estimation performance is also stable. In addition, the Q-TDNN method is also proposed for multi-sources DOA estimation, which can obtain Q different DOAs from the same signals by combining Q independent and parallel TDNNs. The simulation results validate TDNN has much better estimation performance than traditional methods in both single-source and multi-sources cases, especially at low signal-to-noise ratio (SNR).

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

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.

Machine Learning Methods for Inferring the Number of Passive Emitters via Massive MIMO Receive Array

To improve the efficiency and accuracy of direction finding with massive MIMO receive array, it is necessary to determine the specific number of signal emitters in advance. In this paper, we present a complete DOA preprocessing system for inferring the number of passive emitters. Firstly, in order to improve the accuracy of detecting the number of signals, two high-precision signal detectors, square root of maximum eigenvalue times minimum eigenvalue (SR-MME) and geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number, that is a problem of pattern classification. Therefore, we perform feature extraction on the the eigenvalue sequence of sample covariance matrix to construct feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM), and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70\% with massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and Minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive receive arrays, and they also have much worse performance than machine learning-based classifiers.