Performance Analysis of Robust GRCR Based Spectrum Detector Using Compressed Sensing with Non-reconstruction Model

Vivekanand, Chettiyar Vani; Bagan, K. Bhoopathy

doi:10.1007/s11277-021-08324-3

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…However, it is suggested that in order to sense the spectrum availability, the compressed measurements are sufficient [9]. Hence, the reconstruction step can be eliminated, thereby saving energy and time and reducing complexity [11,12].…”

Section: Related Workmentioning

confidence: 99%

“…Cognitive radio is a paradigm for improving quality of service (QoS) standards in telemedicine services [20][21][22]. The underutilised spectrum can be effectively utilised by the cognitive radio networks (CRNs) through compressed sensing with a nonreconstruction model [11,12]. It helps telemedicine services with low latency and high bandwidth.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Efficient Compressed Sensing in Cognitive Radio Network for Telemedicine Services

Vivekanand¹,

Inbamalar²,

Nadar

et al. 2023

Wireless Communications and Mobile Computing

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Wireless body area networks (WBAN) are becoming a promising solution for health care applications. WBAN allows monitoring of patients continuously in their own comfort zone. These devices use the industrial, scientific, and medical band (ISM) for communication. This band is overcrowded due to the increasing number of wireless medical devices and other wireless devices occupying this band. This causes interference, which can be damaging and could result in a change in received power. However, WBAN also needs minimum and reliable energy communication for a longer lifetime and improved quality of service. This work addresses both problems and proposes solutions for the same. A cognitive radio controller is employed as a centralized controller with dynamic spectrum allocation properties to mitigate the interference. The sensing of the spectrum is based on compressed sensing with a nonreconstruction model to save energy. To quantify interference measurement, the interference mitigation factor is introduced. Further, to increase energy efficiency, the K-means algorithm is used to cluster WBANs. However, critical emergency data and normal data are categorized as priority data and normal data, respectively, by the proposed priority scheduling algorithm. The performance of this cognitive radio-based system for telemedicine applications is analyzed through simulations. The simulations are performed using MATLAB 2019.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Compressed Sensing in Cognitive Radio Network for Telemedicine Services

Vivekanand¹,

Inbamalar²,

Nadar

et al. 2023

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

T M

et al. 2024

Int J Communication

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The spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

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Performance Analysis of Robust GRCR Based Spectrum Detector Using Compressed Sensing with Non-reconstruction Model

Cited by 2 publications

References 14 publications

Energy-Efficient Compressed Sensing in Cognitive Radio Network for Telemedicine Services

Energy-Efficient Compressed Sensing in Cognitive Radio Network for Telemedicine Services

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

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