  A new paradigm for large-scale spectrum occupancy learning based on long
short-term memory (LSTM) recurrent neural networks is proposed. Studies have
shown that spectrum usage is a highly correlated time series. Moreover, there
is a correlation for occupancy of spectrum between different frequency
channels. Therefore, revealing all these correlations using learning and
prediction of one-dimensional time series is not a trivial task. In this paper,
we introduce a new framework for representing the spectrum measurements in a
tensor format. Next, a time-series prediction method based on CANDECOMP/PARFAC
(CP) tensor decomposition and LSTM recurrent neural networks is proposed. The
proposed method is computationally efficient and is able to capture different
types of correlation within the measured spectrum. Moreover, it is robust
against noise and missing entries of sensed spectrum. The superiority of the
proposed method is evaluated over a large-scale synthetic dataset in terms of
prediction accuracy and computational efficiency.
