Implementación de un modelo predictor para la toma de decisiones en redes inalámbricas de radio cognitiva

Abstract

The stage of spectral decision making in cognitive radio networks (CRNs) with centralized topologies depends, among other variables, on the reliability of the characterization model of primary users (PUs), the base station request processing method (BS ) And the channel selection algorithm; according to (Masonta, Mzyece, & Ntlatlapa, 2013), (López, Trujillo, & Gualdron, 2015), among other authors, it is necessary to propose and/or apply methodologies that better estimate the presence/ absence of the PUs in the licensed channels, perfect the way in which requests are processed in the BS and improve the sub-stage of selection and allocation of channels in the CRN. In this regard, the doctoral thesis proposes: 1) the use of LSTM, SVM and ANFIS-GRID-FCM to predict the behavior of PUs, 2) poses the possibility of managing requests from secondary users in the BS (using MLPNN) aiming at reducing the time needed for spectrum allocation, 3) generate efficient spectrum selection schemes based on spectrum classification from SVM and ANFIS learning techniques. The methodology used to evaluate/validate the algorithms that make up the decision making system includes as a source of information, the use of a database containing the spectral behavior of PUs in different channels in the licensed GSM band and free WiFi, and generating traffic simulation using QoS criteria for SUs; the programming languages used for building the algorithms are based on the use of C #, Java Script and Matlab. Results show: 1) a higher percentage of success in the LSTM and ANFIS-GRID-FCM characterization, 2) a decrease in the time needed to select and allocate channels, using a proactive strategy to manage SUs requests In relation to those existing in the state of the art, 3) furthermore it proves that SVM and ANFIS are valid for use in channel selection techniques. In conclusion, the proposed decision-making system should be considered as an additional contribution to improve the spectral decision stage in infrastructure-based CRNs, which should be improved/complemented, including such important factors as the characterization of secondary users, the generation of schemas that allow auto configuration of cognitive nodes, and integration of other CR stages, such as spectral mobility in order to determine the feasibility of its implementation on a real scale.