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.

The orthogonal time–frequency space (OTFS) waveform is a modulation scheme for wireless communication that achieves high spectral efficiency by exploiting multipath propagation, offering robustness to Doppler shifts and delay spread. A high peak-to-average power ratio (PAPR) is considered to be a significant issue in the OTFS waveform, drastically reducing the performance of the framework. PAPR affects system performance by causing power amplifiers to operate inefficiently at high power levels, leading to increased power consumption, distortion, and reduced spectral efficiency. In this study, we combined the partial transmission sequence (PTS) and selective mapping (SLM) with the bacterial foraging algorithm (BFA), which is also known as the SLM-BFA and PTS-BFA. An extensive search for the optimal phase factors in the PTS and SLM can be computationally expensive. BFO mimics the foraging behavior of bacteria to efficiently navigate the search space. BFO intelligently finds the optimal phase factors, reducing the computation and PAPR values of the OTFS. The performance of the proposed hybrid schemes was simulated for Rayleigh and Rician channels for 64 and 256 subcarriers, respectively. Parameters such as PAPR, bit error rate (BER), and power spectral density (PSD) were analyzed and compared with those of conventional SLM and PTS methods. The projected hybrid methods obtained a significant PAPR gain of 7.9 and 11.9 dB for 64 subcarriers and 7.5 dB and 8.9 dB for 256 subcarriers with Rayleigh and Rician channels, respectively. Further, it is seen that the proposed methods effectively retained the BER performance of the framework with trivial intricacy. In conclusion, using PTS and SLM techniques, which have been improved by the BFA, makes OTFS waveform communication systems work better. These methods effectively mitigate the PAPR issue and improve the system efficiency and spectral utilization. The synergy between the PTS, SLM, and BFA offers a promising approach for overcoming the challenges in OTFS modulation, contributing to the advancement of robust and efficient wireless communication technologies.

Background Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world’s rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients’ health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. Methods In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. Results In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. Conclusions The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.