Cyclostationarity-based spectrum sensing using beamforming algorithm in cognitive radio networks

摘要

This Cognitive radio (CR) technology is a promising way to improve the bandwidth efficiency of radio spectrum. For practical CR systems have limited a priori knowledge of the primary users signal characteristics , spectrum sensing have three type energy detection, match filter and cyclostationary feature base detection. One of the main benefits of the cyclostationary beam forming algorithms is their ability to extract signals from co-channel interference with only knowledge of the cycle frequency. In this paper, the popular cyclostationary beam formers which has two algorithms namely the adaptive cross self-coherent restoral (ACS) and cyclic adaptive beam forming (CAB), algorithms that provide good performance in the case of medium or weak interference. The CAB algorithm is a special case of the least-square self-coherent restoral (LS-SCORE) algorithm. The proposed adaptive algorithm is to implement which is very promising for applications in wireless and mobile communications.