Errors Impacts of Multi-5G Waveforms in Cognitive Radio Systems

The demand for new wireless services within frequency bands has increased due to the exponential growth of wireless communication, leading to issues related to spectrum scarcity. This paper proposes a hybrid technique-based Spectrum Sensing (SS) algorithm. The traffic signal filtering component of the method utilizes the cosine law in conjunction with the Welch algorithm for segmentation. It incorporates various 5G waveforms, including Generalized Frequency Division Multiplexing (GFDM), Filtered Orthogonal Frequency Division Multiplexing (F-OFDM), Filter Bank Multi-Carrier (FBMC), Non-Orthogonal Multiple Access (NOMA), and Universal Filtered Multi-Carrier (UFMC). The (SS) system achieves a false alarm rate of less than 0.05, a detection probability (Pd) greater than 0.95, and a system error probability of less than 0.09%. Key metrics are used to evaluate the algorithm's performance, and simulation results demonstrate its capability to distinguish between different types of 5G signals.