ALNN-based LOS/NLOS identification in 3D millimetre wave channel

Line-of-sight (LOS)/non-line-of-sight (NLOS) identification is crucial for millimetre-wave (mmW) since it is vulnerable to blocking in the NLOS environment. In this paper, an LOS/NLOS environment identification method is proposed based on angle information learning. Specifically, a neural network named ALNN (angle information learning neural network) is employed, which can learn the difference between the various angle paraments to identify the LOS and NLOS environments for mmW wireless communication systems. First, the 3D mmW channel model is applied to extract the desired angle information. Moreover, an mmW wireless transmission system is constructed to introduce actual measurement data to test the proposed method. Furthermore, various hyperparameters of the ALNN are adjusted to improve identification accuracy. Simulation results show that the proposed method has outstanding identification performance, and the identification accuracy is up to 99.41%.