With this technique, it is determined which frequency band is suitable and there will be done appropriately the assignment of the second user.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:Because a large number of licensed users have occasionally used their allocated bandwidth in recent years, efficient use of frequency band is a vital issue.
📊
Key Findings & Data:While licensed users continue to use this bandwidth for publishing, unused bands can be allocated for reuse to provide broadband network services.
💡
Conclusion & Impact:With this technique, it is determined which frequency band is suitable and there will be done appropriately the assignment of the second user.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:“Cognitive radio · energy detection method neyman-pearson approach
Abstract
Because a large number of licensed users have occasionally used their allocated bandwidth in recent years, efficient use of frequency band is a vital issue. While licensed users continue to use this bandwidth for publishing, unused bands can be allocated for reuse to provide broadband network services. These empty bands are described as white space and cover a large part of the bandwidth. It is possible to activate this great potential by cognitive radio technology. In this study, energy detection technique was used to detect unused bands. With this technique, it is determined which frequency band is suitable and there will be done appropriately the assignment of the second user.
YILMAZEL, R., SEYMAN, M. N., TUNA, E. (2018). Energy Detection Approach for Spectrum Sensing in Cognitive Radio Systems. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, Cilt: 10 - Sayı: 2, 1–9. https://ajindex.com/articles/889441
S. Haykin, D.J.Thompson, J.H.Reed (2009). Spectrum Sensing for Cognitive Radio. Proceeding of IEEE, vol.97, no.5, pp. 849-877,
J. Mitola G. Q. Maguire (1999). Cognitive Radio: making software radios more personel ERRR Personal Communications, vol6, no.4, pp. 13-18 FCC (Federal Communications Commission) (2002). Spectrum policy task force report. Technical Report, 02-135, 2002.
S. İngök (2002). Bilişsel Radyoda Özdeğer Tabanlı Spektrum Sezme Yöntemleri. Msc Thesis, İstanbul Technical University, August F.K. Jondral (2005). Software-Defined Radio—Basics and Evolution to Cognitive Radio. EURASIP Journal on Wireless Communications and Networking, vol. 2005, no. 3, pp. 275-283.
E. Tuna, M.,Karagöz (2012). Gelecek Nesil Ağlar İçin Spektrum Tahsisinde Yeni Bir Yaklaşım: Bilişsel Radyo. International Journal of Engineering Research and Development, vol.4, no.1, C. Pham, N.H. Tran, C.T. Do, C, S. Moon, C.S. Hong (2014). Spectrum Handoff Model based on Hidden Markov Model in Cognitive Radio Networks. Conference Proceedings, IEEE International Conf. on Information Networking, Phuket, pp. 406 – 411. L. Khalid, A. Anpalagan (2010). Emerging cognitive radio technology:Principles, challenges and opportunities. Computer and Electrical Engineering, vol.36, no.2 pp.358-366.
A. Lertsinsrubtavee, A., Malouch, S. Fdid (2014).Hybrid Spectrum Sharing Through Adaptive Spectrum Handoff for Cognitive Radio Networks. IEEE Networking Conference, Trondheim, pp. 1 – 9. I.F. Akyildiz, W.Y. Lee, K.R. Chowdhury (2009). CRAHNs: Cognitive radio ad hoc networks. Ad Hoc Networks, vol.7, no.5, pp.810-836.
S. Atapattu, C. Tellambura, H. Jiang (2011). Spectrum Sensing via Energy Detector in Low SNR. IEEE International Conf. on Communications, Kyoto, Japan. S.Y. Lien, C.C. Tseng, K.C. Chen (2008). Carrier Sensing based Multiple Access Protocols for Cognitive Radio Networks. IEEE International Conf. on Communications, Beijing, China. F.F. Digha, M.S. Alouini, M.K. Simon (2007). On the Energy Detection of Unknown Signals over Fading Channels. IEEE Transactions on Communications, vol.55, no.1, pp.21-24, 21-24.
Emrullah GAZIOGLU (2015). Bilissel Radyo Aglarında Ust Sezgiseller İle Kanal Atama Problemının Çözülmesı. İstanbul Teknık Ünıversıtesı Fen Bılımlerı Enstıtüsü, Yüksek Lisans Tezi.