Machine-Learning-Based Fast Angle-of-Arrival Recognition for Vehicular Communications

Obtaining angle-of-arrival (AOA) information is of great significance to improve the performance of communication systems. Real-time AOA recognition can reduce the complexity of beamforming design for massive multiple-input multiple-output (MIMO) systems, and can be used to construct an outer precoder to optimize the system and rate. However, for vehicular communications, AOA will change with environment and positions of vehicles, and it is difficult to obtain accurate AOA in real-time. Therefore, a fast AOA recognition method is needed to adapt to the rapid changes of channels. The traditional spectral- or parametric-based AOA estimation methods are difficult to obtain real-time AOA information because of the relatively high computational complexity. In order to solve this problem, this paper proposes a machine-learning-based fast AOA recognition approach. The proposed method includes off-line training and on-line estimation processes. In the off-line training process, an estimation model is obtained by using the support vector machine (SVM) based on a large number of actual measurement data in vehicular scenarios. Then, in the on-line estimation process, the obtained model is used to realize fast AOA recognition according to the channel snapshots collected by antenna array. Furthermore, the performance is verified under the different conditions of SVM parameters, training features, antenna numbers, and training data sizes. The experimental results show that the proposed method has satisfactory accuracy in real-time AOA recognition, and the optimal configuration and implementation scheme are also discussed.