Using Neural Networks for Optimum band selection in Cognitive-Radio Systems

The growing development of Internet of Things (IoT) devices is producing an increasing use of the electromagnetic spectrum for wireless communications. Cognitive Radio (CR) technology provides communication terminals with the capability to select arbitrary frequency bands dynamically in order to make a more efficient use of the frequency spectrum and bands occupied by different standards and communication protocols. In this work, we propose a system which uses Long Short-Term Memory (LSTM) networks to predict the future occupation of frequency bands and modifies the specifications of the analog and radio-frequency front-end, adapting dynamically to the best communication channel. System-level simulations of a band-pass filter are shown as a case study to validate the presented approach.