Adaptive Learning Based Beamforming for Spectrum Sensing in Wireless Communications.

In wireless communications, cognitive radio systems are widely used. But in real time applications several interferences occur due to multiple input multiple output systems. The main reason for interference is imperfect spectrum sensing for channel allocation to secondary users. Secondary users try to access primary channels and switch to another frequency when primary user needs access. During this phenomenon interference come in to the picture between the two users. In such a scenario, beamforming provides information security and avoids interference. In this work, a new variable step size based adaptive learning is considered for beamforming. Variable step size scheme is used for distributed estimation in adaptive networks using diffusion concept. This learning scheme iteratively update the step size and reduces the power of error. The performance of proposed diffusion variable step size LMS (DVSLMS) algorithm is studied in multipath environment. The convergence analysis is carried to prove the ability of adaptive learning methods. The experimental results are presented in terms of convergence rate and number of active taps at threshold points 0.1, 0.5 and 1.0. [ABSTRACT FROM AUTHOR]