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Open access

Yongcheng Li, Hai Shen and Manxi Wang

References 1. Mitola, J. I., G. Q. Maguire. Cognitive Radio: Making Software Radios More Personal. - IEEE Personal Communications, Vol. 6, 1999, No 4, pp. 13-18. 2. Qadir, J. Artificial Intelligence Based Cognitive Routing for Cognitive Radio Networks. - Artificial Intelligence Review, Vol. 45, 2016, No 1, pp. 25-96. 3. Abbas, N., Y. Nasser, K. E. Ahmad. Recent Advances on Artificial Intelligence and Learning Techniques in Cognitive Radio Networks. - Eurasip Journal on Wireless Communications & Networking, Vol

Open access

Hamza Nachouane, Abdellah Najid, Abdelwahed Tribak and Fatima Riouch

R eferences [1] Federal Communications Commission, Spectrum Policy Task Force, “Report of the Spectrum Efficiency Working Group,” November 2002. [2] Federal Communications Commission, Facilitating Opportunities for Flexible, “Efficient and Reliable Spectrum Use Employing Cognitive Radio Technologies,” notice of proposed rulemaking and order, FCC 03-322, December 2003. [3] N. Kaabouch and W. C. Hu, Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic , University of North Dakota, US Spectrum Management, 2014

Open access

Nawel Benghabrit and Mejdi Kaddour

References 1. Ashrafinia, S., Pareek, U., Naeem, M. and Lee, D. (2011) Biogeography-based optimization for joint relay assignment and power allocation in cognitive radio systems. IEEE Symposium on Swarm Intelligence, pp. 1-8. 2. Benaya, A.M., Rosas, A.A. and Shokair, M. (2016) Maximization of minimal throughput using genetic algorithm in MIMO underlay cognitive radio networks. 33rd National Radio Science Conference (NRSC), pp. 141-148. 3. Bhardwaj, P., Panwar, A., Ozdemir, O., Masazade, E., Kasperovich, I

Open access

Jiliang Zhang, Hong Jiang and Gaofeng Pan

R eferences [1] E. Hossain and V. Bhargava, Cognitive Wireless Communication Networks , Springer, 2007. [2] N. S. Ferdinand, D. B. da Costa, A. F. de Almeida, and M. Latvaaho, “Physical layer secrecy performance of TAS wiretap channels with correlated main and eavesdropper channels,” IEEE Commun. Lett. , vol. 3, no. 1, pp. 86-89, Feb. 2014. [3] U. M. Maurer, “Secret key agreement by public discussion from common information,” IEEE Trans. Inf. Theory , vol. 39, no. 3, pp. 733-742, May 1993. [4] Q. Li, H. Song, and K. Huang, “Achieving

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Leopoldo Angrisani, Domenico Capriglione, Gianni Cerro, Luigi Ferrigno and Gianfranco Miele

References [1] European Parliament and Council of the European Union (2010). Directive 2010/40/EU - on the framework for the deployment of Intelligent Transport Systems in the field of road transport and for interfaces with other modes of transport, Brussels, Belgium, 1-13. [2] Chen, S. (2012). Vehicular Dynamic Spectrum Access: Using Cognitive Radio for Automobile Networks. Ph.D. Dissertation. Worcester Polytechnic Institute. [3] Jiang, D., Delgrossi, L. (2008). IEEE 802.11p: Towards an international standard

Open access

Arun Kumar, Hemant Rathore and Shikha Bharti

, Elvino S. Sousa, Collaborative spectrum sensing for opportunistic access in fading environments. New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2005, First IEEE International Symposium on. IEEE, 2005. [5] Ghasemi, Amir, Elvino S. Sousa, Optimization of spectrum sensing for opportunistic spectrum access in cognitive radio networks, 4th IEEE Consumer Transmissions and Networking Conference, 2007. [6] Arun K, Manisha G, Design, comparative study and analysis of CDMA for different modulation techniques, EGYPTIAN INFORMATIC JOURNAL 2015; 16(3): 351

Open access

Zijuan Shi and Gaofeng Luo

References 1. Broderson, R. W., A. Wolisz, D. Cabric et al. CORVUS: A Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum. http://bwrc.eecs.berkeley.edu/Research/MCMA/CR/Whitepaper_nal1.pdf 2. Mitola, J. Cognitive Radio: Making Software Radios More Personal. - IEEE Pers Commun, Vol. 6, 1999, No 4, pp. 13-18. 3. Feng, W. J., R. Jiang, P. Han, W. Liao, H. He. Performance Analysis of Cognitive Radio Spectrum Access with Different Primary User Access Schemes. - Wireless Personal Communications, Vol. 75

Open access

Qun Li and Ding Xu

R eferences [1] J. Xue, Z. Feng, and K. Chen, “Beijing spectrum survey for cognitive radio applications,” in IEEE Vehicular Technology Conference Fall , 2013, pp. 1–5. [2] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications , vol. 23, no. 2, pp. 201–220, 2005. [3] X. Kang, Y.-C. Liang, A. Nallanathan, K. Garg, and R. Zhang, “Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity,” IEEE Transactions on Wireless

Open access

Mohanad Abdulhamid

Used sources [1] H. Sun, “Collaborative Spectrum Sensing in Cognitive Radio Networks,” Ph.D. Dissertation, University of Edinburgh, 2011. [2] S. Pudi, T. Sundara, and N. Padmaja, “Performance analysis of cognitive radio based on cooperative spectrum sensing,” International Journal of Engineering Trends and Technology, Vol.4, Issue 4, 2013. [3] G. Padmavathi and S. Shanmugavel, “Performance Analysis of Centralized Cooperative Spectrum Sensing Technique for Cognitive Radio Networks,” Asian Journal of Scientific Research, Vol.7, No.4, PP. 536

Open access

Tomas Cuzanauskas and Aurimas Anskaitis

Abstract

Easy usage and integration with various applications made IEEE 802.11 one of the most used technologies these days, both at home and business premises. Over the years, there have been many additional improvements to the 802.11 standards. Nevertheless, the algorithms and Media Access Control (MAC) layer methods are almost the same as in previous Wi-Fi versions. In this paper, a set of methods to improve the total system capacity is proposed – such as efficient transmit power management based on Game Theory with a custom wireless medium protocol. The transmit power management and wireless medium protocol is verified by both simulation and real application scenarios. The results conclude that the capacity of the proposed wireless medium protocol is overall 20 percent higher than the standard 802.11 wireless medium access protocols. Additional TCP Acknowledgment filtering, which was tested together with the proposed wireless medium access protocol, can provide up to 10-percent-higher TCP throughput in high-density scenarios, especially for asymmetrical traffic cases. The conducted research suggests that efficient power management could result in lighter transmit power allocation rules that are currently set by the local regulators for current Wi-Fi devices. Thus, better propagation characteristics and wireless medium management would lead to an overall higher wireless system capacity.