Primary user characterization for cognitive radio wireless networks using long short-term memory

Abstract

Cognitive radio is a paradigm that proposes managing the radio electric spectrum dynamically by integrating the spectrum sensing, decision-making, sharing, and mobility stages. In the decision-making stage, the best available channel is selected for transmitting secondary user data in an opportunistic fashion, and the success of that stage depends on the efficiency of the primary user characterization model. Use of the long short-term memory technique based on the deep learning concept is proposed in order to reduce the forecasting error present in the future estimation of primary users in the GSM and WiFi frequency bands. The results show that long short-term memory has the capacity needed to improve channel use forecasting significantly more than other methods such as multilayer perceptron neural networks, Bayesian networks, and adaptive neuro-fuzzy inference systems (ANFIS-Grid). It is concluded that although long short-term memory exhibits better performance generating forecasts for time series, computing complexity is higher due to the existence of input, forget, and output gates within the neural structure; therefore, implementation is feasible in cognitive radio networks based on centralized network topologies.