Link Blockage Modelling for Channel State Prediction in Higher Frequencies Using Deep Learning

Abstract

Wireless communications using higher frequencies is now possible due to the advancements in the field of high gain antennas. Using such technologies has enabled accessing wireless media within a short range supplying frequency bands with capacity worth multi-gigabits. Higher frequencies are however exposed to blockage events that can hinder the wireless system performance by reducing the throughput and losing user connectivity. The blockage effect becomes more severe with the addition of mobile blockers like vehicles. In order to understand the blockage events induced by a mobile vehicle, an efficient blockage model is required that can assist in the maintenance of the user connection. This paper proposes using a four state channel model based on the user’s signal strength for describing the occurrence of blockage events at high frequencies. Signal strength prediction and the channel state classification are then conducted and evaluated using two deep learning neural network disciplines. The high accuracy of the simulation results observed suggest the possibility and implementation of the model in real systems.