Time series forecasting with deep learning for cognitive-radio applications

Abstract

We live in a world where the number of devices that are constantly communicating with each other are growing exponentially, and to keep up with that trend, new communication technologies are being developed at a higher rate than in previous decades. The consequence of all these is the increase in the shared usage of the same electromagnetic spectrum by all these devices. Cognitive Radios (CR) [1] are being proposed as a solution that allows communication systems to efficiently use the frequency spectrum, by dynamically modifying their transceiver specifications according to the information sensed from the electromagnetic environment, where they should be able to develop sensing, decision, sharing and allocation functions. A Software-defined Radio (SDR) acts as the base upon which CR technology can be implemented. Artificial Intelligence (AI) layers, embedded in CR systems can be used to optimize the management of the electromagnetic spectrum and assist the signal processing and performance of IoT nodes equipped with CR technology [2]. In the past few years, improvements on Artificial Neural Networks (ANNs) have led to their usage in trying to solve the spectrum management problem, where, for example, Long Short-term Memory networks (LSTMs), a type of Recurrent Neural Networks (RNNs) have been used in the past to predict temporal evolution of data [3] [4]. This project contributes to this topic by examining the use of several ANNs to predict spectrum occupancy in CR systems. Their performance is compared in terms of system complexity, execution time and accuracy. Five NN architectures are studied and implemented to predict channel occupancy which was envisioned as a time series forecasting/prediction problem and will be used to predict the future evolution of the radioelectric spectrum for Cognitive-Radio applications.