Demonstration of energy detector performance and spectrum sensing in Cognitive Radio using AWGN, Rayleigh and Nakagami channels

Ashraf, Maria; Khan, Junaid Ali; Rasheed, Haroon; Ashraf, Farhana; Faizan, Muhammad; Anis, M.

doi:10.1109/icieect.2017.7916538

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…SS is effected by multi path and shadowing which shows degradation of system performance. The system gives different results over different fading channels like AWGN, Rayleigh, Nakagami [2]. By using SVM, ANN, k means techniques, system performance can be improved but there are different challenges, advantages and limitations in different techniques [3].…”

Section: Related Workmentioning

confidence: 99%

“…The Fig.1 shows Energy detection method, in which the received signal is transmitted through band pass filter (BPF) then energy of signal obtained by squaring the amplitude of signal and it is integrated over time and by comparing with threshold, the decision will take weather Primary User (PU) is present or not [2].…”

Section: Energy Detectionmentioning

confidence: 99%

“…Whereas some spectrums remain unused [1]. The national body, Federal Communications Commission (FCC) is coordinate, consumption of radio spectrum resources and the regulation of radio emissions [2]. To utilize spectrum efficiently, the Cognitive Radio (CR) concept was introduced.…”

Section: Introductionmentioning

confidence: 99%

“…A user who doesn't has legacy rights on the usage of a specific part of the spectrum is called SUs. SUs has lowest priority to use spectrum [2]. Spectrum Sensing (SS) is basic requirement in CRN.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Energy Detection and Artificial Neural Network for Spectrum Sensing in Cognitive Radio

Shah¹,

Yelalwar²

2019

IJCT

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In today's wireless communication technology, spectrum occupancy is one of the major challenge. To perform all the task in wireless communication intelligently, Cognitive Radio (CR) is used. With the help of machine learning techniques, performance of CR will increase. In this paper, implementation of spectrum sensing (SS) in Cognitive Radio Network (CRN) is presented. To check the availability of spectrum, the supervised Machine Learning (ML) and conventional spectrum sensing method is used. To classify signal and noise, the Artificial Neural Network (ANN) classifier is used. The classifier's result shows better result than conventional method's result.

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Section: Related Workmentioning

confidence: 99%

Section: Energy Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Energy Detection and Artificial Neural Network for Spectrum Sensing in Cognitive Radio

Shah¹,

Yelalwar²

2019

IJCT

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Artificial Neural Network Based Approach for Spectrum Sensing in Cognitive Radio

Yelalwar

Ravinder

2018

2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

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Performance Analysis of Cooperative Spectrum Sensing using Empirical Mode Decomposition and Artificial Neural Network in Wireless Regional Area Network

Jain,

Yadav,

Kumar

et al. 2024

RACSC

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Background: Radio spectrum is natural and the most precious means in wireless communication systems. Optimal spectrum utilization is a key concern for today's cutting-edge wireless communication networks. The impending problem of the lack of available spectrum has prompted the development of a new idea called “Cognitive Radio” (CR). Cooperative spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several fusion algorithms of decision-making are proposed for sensing the licensed user, but they do not work well under low signal-to-noise ratio (SNR). Objectives: To address the issue of poor detection performance under low SNR, Empirical mode decomposition (EMD) and artificial neural network (ANN) based CSS under Rayleigh multipath fading channel in IEEE 802.22 wireless regional area network (WRAN) is proposed in this paper. Method: In this work, we propose the use of ANN as a fusion center. First, the received signal's energy is calculated using EMD. The computed energy, SNR, and false alarm probability are combined to form a data set of 2048 samples. They are utilized to train Levenberg- Marquardt back propagation training algorithm-based feed-forward neural network (FFNN). Using this trained network, CSS in WRAN is simulated under Rayleigh multipath fading. Results: Simulation results show that the proposed CSS method based on EMD-ANN outperforms the standard fast Fourier transform (FFT) and EMD detection-based cooperative spectrum sensing with a hard "OR" fusion at low SNR. With Pf =0.01, 100% detection accuracy with proposed techniques is obtained at SNR= -22dB. Conclusion:: The findings show that the suggested approach outperforms EMD and FFT based energy detection scheme-based traditional CSS in low SNR environments.

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Demonstration of energy detector performance and spectrum sensing in Cognitive Radio using AWGN, Rayleigh and Nakagami channels

Cited by 4 publications

References 10 publications

Comparative Analysis of Energy Detection and Artificial Neural Network for Spectrum Sensing in Cognitive Radio

Comparative Analysis of Energy Detection and Artificial Neural Network for Spectrum Sensing in Cognitive Radio

Artificial Neural Network Based Approach for Spectrum Sensing in Cognitive Radio

Performance Analysis of Cooperative Spectrum Sensing using Empirical Mode Decomposition and Artificial Neural Network in Wireless Regional Area Network

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