More spectrum bands are needed as the number of wireless applications rises. The spectrum band, though, is now very difficult to adapt to new applications. Because of this, the spectrum is getting more crowded, which also affects quality of service (QoS). One of the most promising technologies to address the issue of spectrum scarcity is cognitive radio (CR). Spectrum sensing (SS) is thought to be essential to CR. It determines that when primary users (PUs) are not using the spectrum, the spectrum can be allocated to secondary users (SUs). In this paper, a novel 5G spectrum sensing technique was implemented using a hybrid matched filter (HMF) algorithm based on the fusion of two matched filters (MF). In addition, we compared the performance of the HMF and traditional MF in Rayleigh and Rician channels. It has been observed that the HMF performs more effectively than the conventional MF in both channels.

This work presents the research on spectrum sensing in optical OFDM (orthogonal frequency division multiplexing) systems. Spectrum sensing is crucial for cognitive radio networks to efficiently utilize available spectral resources. Various spectrum sensing techniques are explored, including energy detection (ED), cyclostationary feature detection (CD), and matched filter detection (MF). These techniques enable the detection of occupied and unoccupied subcarriers in optical OFDM systems, facilitating dynamic spectrum access and spectrum sharing. The research highlights the importance of accurate spectrum sensing in maximizing spectral efficiency, optimizing network performance, and enabling coexistence of multiple users in optical OFDM-based cognitive radio networks. The findings contribute to the development of future wireless communication systems. The parameters such as probability of detection (pd), probability of false alarm (pfa), bit error rate (BER), and power spectral density (PSD) is analysed using computer simulation. The simulation results reveal that the MF achieved a gain of 2.8 dB and 3.6 dB as compared with conventional spectrum sensing algorithms.

<abstract> <p>The cyclostationary spectrum (CS) method is one of the best at what it does because it effectively detects idle spectrum with low signal-to-noise ratios (SNR). In order to distinguish the signal in a noisy environment, gather more data that aids in a better analysis of signals, and use spectral correlation for dependable framework modelling, CS achieves optimal performance characteristics. High intricacy is seen as one of the CS's shortcomings. In this article, we suggest a novel CS algorithm for 5G waveforms. By restricting the computation of cyclostationary characteristics and the signal autocorrelation, the complexity of CS is reduced. To evaluate the performance of 5G waveforms, the Energy Detection (ED) and CS spectrum sensing algorithms based on cognitive radio (CR) are presented. The results of the study show that the suggested CS algorithm did a good job of detection and gained 2 dB compared to the conventional standards.</p> </abstract>

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