Learning methods for long-term channel gain prediction in wireless networks
Federico Chiariotti, Davide Del Testa, Michele Polese, Andréa Zanella, Giorgio Maria Di Nunzio, Michele Zorzi · 2017 International Conference on Computing, Networking and Communications (ICNC) · 2017
Efficiently allocating resources and predicting cell handovers is essential in modern wireless networks; however, this is only possible if there is an efficient way to estimate the future state of the network. In order to accomplish this, we investigate two learning techniques to predict the long-term channel gains in a wireless network. Previous works in the literature found efficient methods to perform this prediction with the aid of a GPS signal: in this work, we predict the future channel gains using only past channel samples, without any geographical information.