On the performance of various 5G signals sensing based on hybrid filter

The 5G wireless communication system is promised to exploit many kinds of waveform for satisfying various requirements to transmit huge size of data. The Cognitive Radio Networks based 5G can coexist among various kinds of signal to enrich every 5G wireless system with necessary frequency bands regardless the kind of waveform. However, most of Spectrum Sensing (SS) techniques are proposed to detect only one kind of waveform that is used in the 5G wireless communication system. To address this issue, designing a SS technique to sense various kinds of waveforms for the 5G wireless communication system is necessary to help this system. In this paper, a SS technique have been proposed for accurately sensing different waveform kinds (F-OFDM, UFMC, and FBMC) through a 5G network where these waveforms have different rates of some data like cyclic prefix, signal length, energy, mapper, and shape. The proposed sensing technique includes three stages; cosine filtering, Bartlett segmenting, and hamming windowing. The cosine filtering function is differentiating between the traffic signals based 5G and noise. Then, the filtered signals are segmented with the help of the Bartlett Segmenting for decreasing the rest noise then every segment is windowed using the Hamming windowing for maintaining the signal resolution. The simulation results revealed a significant detection performance regarding to the following numerical results; detection probability is ≥ 0.95 and false alarm probability is < 0.05, for less than zero dB of signal-to-noise ratio and a lower complexity level. Furthermore, the detection performance of the proposed SS technique is better than that of the related works as shown in a comparison table and a graphical manner.

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