Blind Parameter Estimation Based Matched Filter Detection for Cognitive Radio Networks

Abstract

Recent advancements in wireless communication applications have generated the need for very large spectrum. The assignment of a frequency spectrum permanently to a particular user has led to underutilization of available spectrum.The solution to more spectrum demand lies in offering licensed idle spectrum to other non-licensed requesting user temporarily. Cognitive Radio is an environment aware, intelligent system that exploits the spectrum holes and enhances the usage of limited frequency spectrum resource so that more user can be accommodated in limited band. In order to detect spectrum holes in wideband spectrum many techniques have been proposed for spectrum sensing out of which Energy detection (ED), Cyclostationary (CS) detection and Matched Filter (MF) detection are most discussed and most practiced .Energy detection (ED) technique is a vastly used sensing technique in CRNs because of its operational simplicity. However at low SNR, the performance of ED is badly degraded. Matched Filter (MF) detection is an alternate sensing technique at low SNR, as it increases SNR of the received signal. MF detector gives far better performance when compared to ED at low SNR. But the problem with MF detector is that it must have priori knowledge about Primary User (PU) signal, therefore we need dedicated MF detector for each PU. Motivated by above drawback of ED and MF a new MF technique is proposed by which requirement of priori information of PU signal can be eliminated. At the MF detector front end, we perform blind estimation of PU signal parameters and accordingly update the coefficient of MF transfer function. Blind Estimation of signal parameters solves the problem of having priori information about PU signal for MF detector.