The world is moving towards faster data transformation with more efficient localization of a user being the preliminary requirement. This work investigates the use of a deep learning technique for wireless localization, considering both millimeter-wave (mmWave) and sub-6 GHz frequencies. The capability of learning a new neural network model makes the localization process easier and faster. In this study, a Deep Neural Network (DNN) was used to localize User Equipment (UE) in two static scenarios. We propose two different methods to train a neural network, one using channel parameters (features) and another using a channel response vector and compare their performances using preliminary computer simulations. We observe that the former approach produces high localization accuracy considering that all of the users have a fixed number of multipath components (MPCs), this method is reliant on the number of MPCs. On the other hand, the latter approach is independent of the MPCs, but it performs relatively poorly compared to the first approach.
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