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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.
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.
Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array
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.
Enhanced RSS-based UAV Localization via Trajectory and Multi-base Stations
First, fixing the nth BS, by exploiting multiple measurements along trajectory, the position of UAV is computed by ML rule.
