ADSD: An Automatic Distributed Spectrum Decision method in Cognitive Radio networks

With the development of Cognitive Radio networks, in order to fully utilize the scarce spectrum resources, secondary users are capable of exploiting the available spectrum of primary users. However, when there are several spectrum options available, how to select an appropriate one for the secondary user according to the spectrum quality and the QoS requirements of different kinds of applications is a new challenge. In this paper, we propose a new Automatic Distributed Spectrum Decision (ADSD) method to solve this problem. ADSD considers multiple characterization parameters, in particular, the primary users' arrival probability, to estimate the quality of the available spectrum options. A weight self-generation mechanism is included to automatically determine the weights of different parameters, thus avoiding the difficulty and irrationality when relying on the users to specify the weights directly. In addition, in conjunction with the reconfiguration mechanism, ADSD can reduce the rate of spectrum handoffs by reconfiguring the transmission parameters rather than making a new decision for the existing transmission. Simulation results show that without any users' interference, ADSD can automatically select the appropriate spectrum for transmission and significantly improve the Cognitive Radio network performance in terms of throughput and the spectrum handoff rate.