A DNN-Based Channel State and Scenario Identifications for Millimeter-Wave Communications

Abstract

In this work, a novel deep neural network (DNN) based approach is proposed to simultaneously identify mmWave channel states and scenarios. In addition to use channel statistical features in traditional identification approaches, the cluster-based channel features carrying scatterers information are also considered when training our proposed DNN-based identification model, which is validated by the channel measurements conducted in a substation, a waiting hall and an open office scenarios with the line-of-sight and non-line-of-sight channel states. The results show that our proposed DNN-based approach can enhance the identification accuracies greatly, which is very useful to be applied in millimeter wave channel state and scenario identifications.