Investigation of Adaptive Beamforming Algorithms for Cognitive Radio Technology

Frequency spectrum is one of the biggest natural resource which has a significant impact on the development of wireless communication technologies. Therefore, utilizing this natural resource in an efficient way accelerates the technological advancement. The spectrum allotment strategy that has been serving well the wireless communication family is the fixed spectrum allocation strategy. However, the increasing demand to use wireless technologies increased the competition for spectrum. As a result, there is no usable frequency spectrum left unoccupied. In spite of this spectrum scarcity, different research shows that most of the times most of the spectrum bands are not in use. The proposed solution to overcome this problem is to use the cognitive radio technology. Cognitive radio is a wireless communication technology which adds intelligence to the existing wireless communication scenario. As every wireless communication requires antenna, in this paper the feasibility of smart antenna to this intelligence system is studied and the performance (based on computational complexity, convergence rate and radiation pattern characteristics) of different adaptive beamforming algorithms are investigated. The investigation result shows that the Sample Matrix Inversion (SMI) algorithm besides its best convergence rate, also produces radiation pattern that best suits the behavior of cognitive radio technology.