Neural-Network-Based NLOS Identification of Angular Clusters at 60 GHz

The work in this article identifies the nature of individual angular clusters as line-of-sight (LOS) or non-line-of-sight (NLOS) in indoor millimeter-wave (mm-wave) channels. The proposed technique utilizes the channel knowledge that is readily available from a beam training process in directional antenna-based communications. In particular, the behavior of five different channel metrics, namely the angular covariance, the time-domain, frequency-domain channel kurtosis, the mean excess delay, and the rms delay spread, is analyzed using maximum likelihood ratio (MLR) and artificial neural network (ANN). A noticeable difference between LOS and NLOS clusters is observed and assessed for identification. Hypothesis testing shows errors as low as 0.02–0.003 in simulations and 0.04–0.07 in measurements at 60 GHz in indoor short-range environments.