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Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications version 2; peer review: 2 approved with reservations
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Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications version 2; peer review: 2 approved with reservations
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Journal Article
Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications version 2; peer review: 2 approved with reservations
2024
Overview
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.
