GE Yuming, SUN Yi, LU Shan and Eryk Dutkiewicz, “A Distributed Decision Making Method inCognitive Radio Networks for SpectrumManagement,” Chinese Journal of Electronics, vol. 19, no. 2, pp. 195-200, 2010,
Citation:
GE Yuming, SUN Yi, LU Shan and Eryk Dutkiewicz, “A Distributed Decision Making Method inCognitive Radio Networks for SpectrumManagement,” Chinese Journal of Electronics, vol. 19, no. 2, pp. 195-200, 2010,
GE Yuming, SUN Yi, LU Shan and Eryk Dutkiewicz, “A Distributed Decision Making Method inCognitive Radio Networks for SpectrumManagement,” Chinese Journal of Electronics, vol. 19, no. 2, pp. 195-200, 2010,
Citation:
GE Yuming, SUN Yi, LU Shan and Eryk Dutkiewicz, “A Distributed Decision Making Method inCognitive Radio Networks for SpectrumManagement,” Chinese Journal of Electronics, vol. 19, no. 2, pp. 195-200, 2010,
In order to make full utilization of the scarce spectrum resources for Cognitive radio networks, secondary users are expected to exploit the available spec- trum of primary users. However, when there are several spectrum bands available, how to select an appropriate one for the secondary user according to the spectrum quality and the QoS requirements of di®erent kinds of applications is a new challenge. In this paper, we propose a new Au- tomatic distributed spectrum decision (ADSD) method to solve this problem. ADSD considers multiple spectrum characterization parameters, in particular, the primary users' arrival probability, to estimate the quality of the available spectrum bands. A weight auto-generation mech- anism is included to automatically determine the weights of di®erent parameters, thus avoiding the di±culty and ir- rationality when relying on the users to specify the weights directly. In addition, in conjunction with the recon¯g- uration mechanism, ADSD can reduce the rate of spec- trum hando®s by recon¯guring the transmission parame- ters rather than making a new decision for the existing transmission. Simulation results show that without any users' interference, ADSD can automatically select the ap- propriate spectrum for transmission and signi¯cantly im- prove the Cognitive Radio network performance in terms of throughput and the spectrum hando® rate.
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