Cognitive radio: Cyclostationarity-based classification approach for analog TV and wireless microphone signals

In cognitive radio system, different primary signals have different protection requirements and operating parameters. So, classification of primary signals at secondary user receiver is needed to achieve these requirements of protection and support scalability. Scalability means changing operating parameters according to the current conditions which one of them is the primary signal that occupies the spectrum band. In this paper, we propose an approach to classify the primary signal either TV-PAL signal or wireless microphone signal using cyclostationary features in the context of IEEE 802.22 Wireless Regional Area Network (WRAN). The performance of the proposed approach is evaluated by probability of correct classification. This knowledge of identifying the primary signals can be applied for bandwidth scalability to use fractions of TV channel when it is occupied by wireless microphone signal. The results show that the proposed approach performs well in low signal-to-noise ratio (LSNR) and it is expected to increase the overall spectrum utilization of WRAN cognitive radio as well.