Abstract:

The article presents a concept of spectrum occupancy prediction with Recurrent Neural Network (RNN). RNN input feature vector, corresponding to the single radio channel is reduced and normalized to time differences in channel activity. This makes RNN implementation far less computationally demanding than raw data processing, applicable for analyzed scenario of channel activity. Aim of this approach is to find “white holes” that are long enough to enable a secondary user transmission.