Beamforming in cognitive radio networks using partial update adaptive learning algorithm

Abstract

Cognitive radio technology is a promising way to improve bandwidth efficiency. Frequency which is not used in any aspect will be utilized by using some of the most powerful resources in this cognitive radio. One of the main advantages of cognitive radio signal is to detect the different channels which are there in the spectrum and it can modify the frequencies which is utilized frequently. It allows the licensed users to gain the licensed bandwidth under the condition to protect the licensed users from harmful interference i.e., from secondary users. In this paper, we would like to implement cognitive radio using the beamforming technique, by using power allocation as a strategy for the unlicensed transmitter which is purely form on the result of sensing. It is on the state of the primary user in a various cognitive radio network whereas the unlicensed transmitter gives a single antenna and it modify its power transmission. For the cognitive radio setup, we have used normalized adaptive learning algorithms. This application would be very useful in medical telemetry applications. Nowadays wireless communication plays a vital role in healthcare applications for that we have to build a separate base. It reduces the effort of the building of separate infrastructure for medical telemetry applications.