American Journal of Electrical and Computer Engineering
Volume 4, Issue 2, December 2020, Pages: 49-54
Received: Sep. 5, 2020;
Accepted: Sep. 19, 2020;
Published: Sep. 25, 2020
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Abdullah Al Zubaer, Department of Computer Science and Engineering, Rabindra Maitree University, Kushtia, Bangladesh
Sabrina Ferdous, Department of Computer Science and Engineering, Rabindra Maitree University, Kushtia, Bangladesh
Rohani Amrin, Department of Information and Communication Technology, Rabindra Maitree University, Kushtia, Bangladesh
Md. Romzan Ali, Department of Electrical and Electronic Engineering, Rabindra Maitree University, Kushtia, Bangladesh
Md. Alamgir Hossain, Department of Computer Science and Engineering, Islamic University, Kushtia, Bangladesh
Cognitive radio which is a low-cost communication system can choose the available frequencies and waveform so that it can restrict the interference on the unlicensed users on the premise automatically. In cognitive radio networks the spectrum sensing is considered as the key technology. On the contrary it is not only able fill voids in the wireless spectrum but also it can increase the spectral efficiency dramatically. The another issue is that sometimes users can experience deep shadowing or fading effect that time accurate detection factor will be compromised. However, we also allow the CK (cognitive Radio) users to co-operate by sharing their information so that it can detect the primary users (PU) to more accurately. Indeed, the main motive of this project is to investigate performance of Co-operative spectrum sensing scheme by upgrade using energy detection and to promote/n the sensing performance in channels such as AWGN and Rayleigh fading channels. At fusion centre (FC) hard decision is performed which is the combination of (OR rule and AND rule). That is why for this extraordinary performance CR (Cognitive Radio) can be able to make final decision about primary user present or not. Additionally, comparisons among data fusion rules have been investigated also for a vast range of average in SNR (Signal to noise ratio) values. As a result, the performance of this CR is evaluated in terms of the probability of miss detection (Pmd) and the probability of false alarm (Pfa). Moreover, the report is compared between the theoretical value and the simulated result and then it describes the relationship between the signal to noise ratio (SNR) and the detections. At last, the method, energy detection and simulation and result are discussed.
Abdullah Al Zubaer,
Md. Romzan Ali,
Md. Alamgir Hossain,
Detection and False Alarm Probabilities over Non-fading and Fading Environment, American Journal of Electrical and Computer Engineering.
Vol. 4, No. 2,
2020, pp. 49-54.
Copyright © 2020 Authors retain the copyright of this article.
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Federal Communications Commission, November 2002, "Spectrum policy task force report. ET Docket 02- 135”.
FCC, ET. "Docket No 03-222 “Notice of proposed rulemaking and order." (2003): 1-21.
Cabric, Danijela, Shridhar Mubaraq Mishra, and Robert W. Brodersen, 2004, "Implementation issues in spectrum sensing for cognitive radios." Signals, systems and computers. Conference record of the thirty-eighth Asilomar conference on. Vol. 1. IEEE.
Lu, et al.,"Ten years of research in spectrum sensing and sharing in cognitive radio." EURASIP Journal on Wireless Communications and Networking 2012.1 (2012): 28.
Urkowitz, Harry. "Energy detection of unknown_deterministic_signals." Proceedings of the IEEE 55.4 (1967): 523-531.
Hossain, Mohammad Alamgir, Md Shamim Hossain, and Md Ibrahim Abdullah, 2012 "Cooperative spectrum sensing over fading channel in cognitive radio." International Journal of Innovation and Applied Studies 1.1: 84-93.
Digham, Fadel F., Mohamed-Slim Alouini, and Marvin K. Simon. 2007, "On the energy detection of unknown signals over fading channels." IEEE transactions on communications 55.1: 21-24.
P. Verma, B. Singh (24–27 September, 2014), “Throughput analysis in cognitive radio_networks”, International Conferen_On Advances in Computing, Communications and Informatics (ICACCI), New Delhi, India.
Z. Shi, K. The (2013), “Energy-efficient joint design of sensing and transmission durations for protection of primary user in cognitive radio systems”, IEEE Communications Letters, Volume 17, Issue 3, pp. 565–568, DOI: 10.1109/LCOMM.2013.012313.122442.
C. Sun, W. Zhang, K. B. Letaief (11–15 March, 2007), “Cooperative spectrum sensing for cognitive radios under bandwidth constraints”, Wireless Communications and Networking Conference, Kowloon, China.
Ma, J., Zhao, G., & Li, Y. (2008). Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 7 (11), 4502-4507.
Quan, Z., Cui, S., & Sayed, A. H. (2008). Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE Journal of selected topics in signal processing, 2 (1), 28-40.
Taricco, G. (2011). Optimization of linear cooperative spectrum sensing for cognitive radio networks. IEEE Journal of Selected Topics in Signal Processing, 5 (1), 77-86.
S. Haykin,, Feb 2005, “Cognitive radio: brain- empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220.
D. Cabric, S. M. Mishra, and R. W. Brodersen, “Implementation issues in spectrum sensing for cognitive radios,” in Proc. Asilomar Conf. on Signals, Systems, and Computers, Nov. 7-10, 2004, vol. 1, pp. 772–776.