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.

Monitoring blood pressure is a critical aspect of safeguarding an individual’s health, as early detection of abnormal blood pressure levels facilitates timely medical intervention, ultimately leading to a reduction in mortality rates associated with cardiovascular diseases. Consequently, the development of a robust and continuous blood pressure monitoring system holds paramount significance. In the context of this research paper, we introduce an innovative deep learning regression model that harnesses phonocardiogram (PCG) data to achieve precise blood pressure estimation. Our novel approach incorporates a convolutional neural network (CNN)-based regression model, which not only enhances its adaptability to spatial variations but also empowers it to capture intricate patterns within the PCG signals. These advancements contribute significantly to the overall accuracy of blood pressure estimation. To substantiate the effectiveness of our proposed method, we meticulously gathered PCG signal data from 78 volunteers, adhering to the ethical guidelines of Suranaree University of Technology (Human Research Ethics number EC-65-78). Subsequently, we rigorously preprocessed the dataset to ensure its integrity. We further employed a K-fold cross-validation procedure for data division and alignment, combining the resulting datasets with a CNN for blood pressure estimation. The experimental results are highly promising, yielding a Mean Absolute Error (MAE) and standard deviation (STD) of approximately 10.69 ± 7.23 mmHg for systolic pressure and 6.89 ± 5.22 mmHg for diastolic pressure. Our study underscores the potential for precise blood pressure estimation, particularly using PCG signals, paving the way for a practical, non-invasive method with broad applicability in the healthcare domain. Early detection of abnormal blood pressure levels can facilitate timely medical interventions, ultimately reducing cardiovascular disease-related mortality rates.