Spectrum Occupancy Prediction for Internet of Things via Long Short-Term Memory

With the development of Internet of things (IoT), the demand on spectrum is increasing rapidly. Moreover, due to lack of power and the feature of short burst, the signals of IoT may be transmitted relying on accessing the idle spectrum, leading to a higher successful transmitting probability. Thus, the spectrum should be allocated in advance for the ongoing terminals of IoT. In this paper, a long short-term memory aided spectrum-prediction (LSTMSP) scheme has been conceived by analyzing the relationships between time and frequency of historical spectrum data. Performance evaluations on real-world spectrum data show that the accuracy of the spectrum occupancy prediction is above 0.7, demonstrating the benefits of the conceived LSTMSP method.