Spectrum Occupancy Predictions Using Deep Learning Algorithms

The fixed spectrum allocation (FSA) policy causes a waste of valuable and limited natural resources because a significant portion of the spectrum allocated to users is unused. With the exponential growth of wireless devices and the continuous development of new technologies demanding more bandwidth, there is a significant spectrum shortage under current policies. Dynamic spectrum access (DSA) implemented in a cognitive radio network (CRN) is an emerging solution to meet the growing demand for spectrum that promises to improve spectrum utilization, enabling secondary users (SUs) to utilize unused spectrum allocated to primary users (PUs). This study has addressed all the limitations of the previous studies by implementing a comprehensive approach that encompasses reliable spectrum sensing, potential candidate spectrum band identification, long-term adaptive prediction modeling, and quantification of improvements achieved in the prediction model. The Long-Short Term Memory (LSTM) Deep Learning (DL) model was proposed as a solution for this study to address the challenge of capturing temporal dynamics in sequential inputs. The LSTM model leverages a gating mechanism to regulate information flow within the network, allowing it to learn and model long-term temporal dependencies effectively. The dataset used for this study was obtained from a real-world spectrum measurement by employing the Cyclostationary Feature Detection (CFD) approaches in the GSM900 mobile network uplink band, spanning a frequency range of 902.5 to 915 MHz over five consecutive days. The dataset comprises a total of 225,000 data points. The five-day spectrum measurement data analysis yielded an average spectrum utilization of 20.47 %. The proposed model predicted the spectrum occupancy state for 5 hours ahead in the future with an accuracy of 99.45 %, improved the spectrum utilization from 20.47 % to 98.28 % and reduced the sensing energy to 29.39 % compared to real-time sensing.