Cognitive radio communications depend on methods for sensing the spectrum as well as adapting transmission parameters\nto available resources. In this context, this work proposes a novel system that makes use of prediction to dynamically allocate\nsubcarriers to different transmissions in an orthogonal frequency division multiplexing (OFDM) system. To this end, the proposal\nis comprised of a predictive componentwhichmakes use of a neural network andmultiresolution analysis and a second component,\nwhich uses wavelet analysis and cognitive radio functions to carry out a dynamic allocation of subcarriers in an OFDM system.\nThe use of wavelets allows the system to split the data stream in blocks of information to be transmitted over multiple orthogonal\nsubcarriers. This proposed system makes use of the decision-making functions of a cognitive radio device to select the number\nand position of the subcarriers used for communications without interference. Although there exist other OFDM systems using\nwavelets, they are not used in combination with the decision-making functions implemented in cognitive radio devices. In contrast,\nthe proposed OFDM system operates using some of these functions, thus being able to better adapt its operational parameters. The\nuse of wavelets combined with a neural network model improves the prediction of the bandwidth utilization as shown in this\nwork. It is concluded that the proposed system improves spectral efficiency and data rate by using the decision-making functions\nof cognitive radios to select the appropriate OFDM subcarriers to be used during the data transmissions.
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