Deep learning aided beam vector assignment for massive MIMO maritime communication considering location information and handover impact

Abstract

Location aided beamforming (LAB) has been proposed as a potential solution to fast beam vector (BV) selection for massive multi-input multi-output (M-MIMO) maritime communication systems. However, the existing LAB schemes hardly consider the impact from handover. In this paper, we propose to apply the Gaussian anomaly detection (GAD) to improve the system performance, particularly in the handover zone, by exploiting the information of sea lane and locations of the user equipment (UE). Furthermore, inspired by the rationale of deep learning (DL) aided image processing methods, we first transform the BV assignment problem to a location-based artificial gray value classification problem, and then utilize a convolutional deep belief networks (CDBN) to predict the BV indices from a predefined codebook constituted by location-related feature matrices. In addition, we exploit GAD to rectify the training data of CDBN, such that the prediction on BV assignments can be more accurate to achieve a higher capacity than known methods. Finally, an extensive comparison with selected existing schemes through simulations demonstrates the effectiveness of the proposed scheme.