Blind Cyclostationary Spectrum Sensing Technique Algorithm for Cognitive Radio Networks

Cyclostationary Spectrum Sensing (CSS) techniques are commonly used to detect primary users in Cognitive Radio (CR) networks by exploiting the cyclostationary nature of many wireless signals. CSS techniques have demonstrated impressive results in detecting primary users in CR networks, particularly in situations with low signal-to-noise ratios and multi-user interference. Nonetheless, CSS still faces challenges in handling signal variations and implementing high-complexity systems, which are active areas of research. The premise of this paper is that noise is generally a signal that lacks correlation. The paper suggests that if the input signal has a periodicity, the autocorrelation function can enhance this periodicity, while if the input signal is non-periodic, it can create a structure that lacks periodicity. The suggested approach involves utilizing the outcome of the autocorrelation module as an input for a new blind cyclostationary spectrum detection technique. This paper involves introducing the optimized detector parameters and evaluating its performance in simulation environments. Moreover, the simulation results presented in the paper demonstrate a 2.5 dB improvement in detection performance compared to the conventional cyclostationary technique. This indicates the effectiveness of the proposed approach in enhancing the accuracy of detecting primary users in CR networks.