Using weighted multilevel wavelet decomposition for wideband spectrum sensing in cognitive radios

Cognitive radio is defined as a paradigm for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with the other users. Cognitive Radio (CR) introduces the idea of system awareness and intelligence adaptability in order to provide more efficient spectrum utilization especially in spectrum scarcity environment. One of the key CR functionalities is the spectrum sensing, which allows CRs to monitor radio environment and detect unused spectrum. Accuracy and speed of estimation are keys to select appropriate spectrum sensing technique. Conventional spectrum estimation techniques based on Fourier transform have a lack of performance in case of low frequency resolution, large variance of estimated power and large side lobes. Some other methods that improve these deficiencies would increase the complexity of the system, which is not desired. In this paper, we utilized an energy detection based spectrum sensing method which reduced the complexity of the system while improving the performance of the CR. Multi level wavelet decomposition is used to divide the signal into different frequency resolution and each scale level is taken into account according to its importance in the edge detection procedure. Simulation results revealed that in comparison to state of the art similar methods, the proposed method reduces the complexity while improving the performance.