Analysis of Cognitive Radio for LTE and 5G Waveforms

Ramamoorthy, Ramesh; Sharma, Himanshu; Akilandeswari, A.; Gour, Nidhi; Кумар, Арун; Masud, Mehedi

doi:10.32604/csse.2022.024749

“…5 However, each has limitations that researchers continue to address through advancements in cognitive radio technology, signal processing techniques, and machine learning to improve spectrum-sensing accuracy, adaptability, and efficiency in dynamic and complex wireless environments. 6 The suggested article is organized as follows: the article's background, motivation, and contributions are covered in Section 1, related work is covered in Section 2, a system model is shown in Section 3, and the simulated outcomes and the proposed article's conclusion are covered in Sections 4 and 5.…”

Section: Revised Amendments From Versionmentioning

confidence: 99%

“…This feature makes it easier for telemedicine, IoT devices for healthcare, and remote patient monitoring to proliferate, promoting prompt and accurate healthcare delivery. Additionally, spectrum sensing improves spectrum reliability and efficiency, which is essential for enabling the ultra-low latency and large data rates needed by developing healthcare applications in 6G networks [6]. Further motivations are also given which address the above question: Author Ans.…”

Section: Reviewer Comments: Table 2 (Validation Table)mentioning

confidence: 99%

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Kumar,

Kaur,

Gaur

et al. 2024

F1000Res

2

0

View full text Add to dashboard Cite

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.

show abstract

“… 5 However, each has limitations that researchers continue to address through advancements in cognitive radio technology, signal processing techniques, and machine learning to improve spectrum-sensing accuracy, adaptability, and efficiency in dynamic and complex wireless environments. 6 The suggested article is organized as follows: the article’s background, motivation, and contributions are covered in Section 1 , related work is covered in Section 2 , a system model is shown in Section 3 , and the simulated outcomes and the proposed article’s conclusion are covered in Sections 4 and 5 .…”

Section: Introductionmentioning

confidence: 99%

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Kumar,

Kaur,

Gaur

et al. 2024

F1000Res

Self Cite

0

View full text Add to dashboard Cite

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.

show abstract

An efficient hybrid spectrum sensing algorithm to enhance the performance of optical NOMA waveforms using 256-QAM

Sharma,

Yadav,

Kumar

2024

Journal of Optical Communications

0

View full text Add to dashboard Cite

The ever-increasing demand for bandwidth in optical networks necessitates efficient spectrum utilization. To address this challenge, this paper proposes a novel hybrid spectrum sensing algorithm tailored explicitly for 256-QAM (Quadrature Amplitude Modulation) optical communication waveforms. The proposed algorithm combines the strengths of energy detection and cyclostationary feature detection to overcome the limitations of individual methods. Energy detection (ED) provides fast and low-complexity sensing, while cyclostationary feature detection offers higher accuracy and sensitivity. First, ED is employed for rapid initial spectrum assessment. Subsequently, matched filter (MF) detection is selectively applied only to frequency bands identified as potentially occupied by primary users based on the energy detection results. This selective approach significantly reduces computational complexity while maintaining high detection accuracy. The results demonstrate significant improvements in detection accuracy, sensitivity, and computational efficiency compared to existing methods. In particular, the hybrid algorithm performs better in scenarios where weak 256-QAM signals coexist with strong primary users, showcasing its effectiveness in dynamic spectrum-sharing applications. This work contributes significantly to optical spectrum sensing by offering an efficient and accurate solution for advanced radio systems. The proposed hybrid algorithm paves the way for improved spectrum utilization and facilitates the development of high-performance, next-generation optical networks. The projected method obtained a gain of −200 as compared with the existing methods.

show abstract

Analysis of Cognitive Radio for LTE and 5G Waveforms

Cited by 3 publications

References 27 publications

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

An efficient hybrid spectrum sensing algorithm to enhance the performance of optical NOMA waveforms using 256-QAM

Contact Info

Product

Resources

About