Blind parameter estimation based matched filter detection for cognitive radio networks

Abstract

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 in this paper we proposed a new MF technique by which requirement of priori knowledge about PU signal can be eliminated as well as performance at low SNR is improved. 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. Performance analysis and comparison of ED, conventional MF detector and proposed MF detector also have been done in this paper which show that the proposed MF detector perform better than ED and almost same as the conventional MF detector.