• Channel
• CNN
• Colab
• Convolutional
• Deep
• Indoor
• Learning
• MIMO
• Neural
• Positioning
• Tensorflow
• Wireless

In this work a deep convolutional neural network was trained, with the purpose of indoor positioning. Starting from a MIMO propagation model, expressing wireless channel response as function of 3-dimensional position coordinates, a specific CNN have been trained. This was then tested with data coming from a real scenario. Furthermore, a dedicated CNN was trained considering this dataset, in order to compare perfomances with the previous one. Finally, an hybrid approach was provided: the amount of data coming from real scenario was expanded with gradually increasing portions of data generated from propagation model. A dedicated CNN was trained also in this case to evaluate overall performances.