A CR-5G network based on multi-user for various waveforms detection

The 5G network is promised to provide all the requirements surfacing from the speedily rising number of mobile phone users rather than huge amount of data transmission. Many 5G waveforms can be used by 5G network such as Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM), Universal Filtered Multi-Carrier (UFMC), or Filter Bank Multi-Carrier (FBMC) waveform. To effectively capitalize on the spectrum resources, Internet of Thing (IoT), and multimedia transmission, the Cooperative Spectrum Sensing (CSS) system is being extensively utilized by Cognitive Radio (CR). It is able to meet the requisite communication services, however, the conventional CSS systems have been designed to detect only single kind of 5G waveform and poor detection performance for low signal-to-noise ratio (SNR). In this paper, a hybrid filter based CSS system is proposed for detection of various 5G waveforms by multi-user. The proposed filter includes three cascaded stages: cosine filtering, Welch segmentation, and Hamming windowing. The proposed detection system is applied on different data such as various of waveforms and SNRs. The simulation results exhibit a significant detection performance for the parameters of less than zero dB of SNR, greater than 95% global detection probability, less than 1% global system error probability, and less than 1% global false alarm probability. In addition, the detection performance reveals that the proposed system is outperformed related works as shown in the comparison in terms of the previous parameters.