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Based on the theory of the Federal Communications Commission, the spectrumavailable on cognitive radio networks is limit and the non-optimal use of thespectrum necessitates the need for a telecommunications model, so that thispattern can exploit the existing spectral positions. In this spectrumsubscription scenario, when the primary users are not present, it is alsopossible to assign this telecommunication to tenants who are unauthorized orsecondary. The challenge of using this scenario is to allocate time-frequencyresources to them and how to access nodes in one channel without anyinteractions between primary and secondary users and the throughput willincrease. The main idea of this research is using chaotic recurrent neuralnetwork for improving access to spectrum in congestion times and the mainpurposes are reduce interference and increase throughput in cognitive radionetworks. In this method, in addition to the throughput, the amount of unwantedblockage of packets, the reduction of the cost of operations for secondaryusers, the hardware requirements for secondary users and the coefficient ofjustice are considered which in fact, it is a new channel assignment processwith respect to the environment response, the updates the probability that thechannels are empty in subsequent periods, and increases the permeability byreducing interference with chaotic recurrent neural network.

Spectrum accessing optimization in congestion times in radio cognitive networks based on chaotic neural networks