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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

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

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


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.

Open access

Saadia Tabassum, Sajjad Hussain and Abdul Ghafoor

References [1] MITOLA, J. III : Cognitive Radio: An Integrated Agent Archi- tecture for Software Defined Radio, Royal Institute of Technol- ogy (KTH), Stockholm, Sweden, May 2000. [2] RAJBANSHI, R.-WYGLINSKI, A. M.-MINDEN, G. J. : An Efficient Implementation of NC-OFDM Transceivers for Cogni- tive Radios, In Proceedings of the First International Conference on Cognitive Radio Oriented Wireless Networks and Communi- cations, Mykonos Island, Greece, June 2006. [3] O’NEILL, R.-LOPES, L. B. : Envelope Variations and

Open access

Dragana Perić and Miroslav Perić

-Complexity Non-Data-Aided Timing Recovery for PAM-based M-ary CPM Receivers, Radioengineering 21 No. 3 (Sep 2012). [9] LOTZE, J.—FAHMY, S. A.—NOGUERA, J.—DOYLE, L. E. : A Model-Based Approach to Cognitive Radio Design, IEEE J. Sel. Areas Commun. 29 No. 2 (Feb 2011). [10] XIONG, F. : Digital Modulation Techniques, Second Edition, Artech House, 2006. [11] CORDESSES, L. : A Direct Digital Synthesis: A Tool for Periodic Wave Generation (Part I)a, IEEE Signal Processing Magazine 21 No. 4 (July 2004), 50–54. [12] CESSNA, R. J.—DONALD, L. M. : Phase

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Evaldas Stankevičius and Šarūnas Oberauskas

using cognitive base-stations for UMTS Lte,” IEEE Communications Magazine, Volume 49, Issue 8, IEEE, 2011, pp. 152-159. [5] X. Hognian, S. Hakola “The investigation of power control schemes for a device-to-device communication integrated into Ofdma cellular system,” in 2010 IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications, 2010, pp. 1775-1780. [6] C.U. Castellanos et al. “Performance of Uplink Fractional Power Control in UTRAN LTE,” in 2008 Vehicular Technology Conference, 2008, pp. 2517-2521. [7] R. Love et al. “Downlink

Open access

Ahmed Qaddus, Shahzad Hassan and Abid Ali Minhas

Management in Energy Efficient 5G Cognitive Radio Networks,” in Proc. 1st International Conference on 5G for Ubiquitous Connectivity (5GU), 2014. [8] S. Aleksic, “Towards Fifth-Generation (5G) Optical Transport Networks,” in 2015 17th International Conference on Transparent Optical Networks (ICTON), Jul. 2015. [9] V. Yazıcı, C. Kozat, and M. Sunay, “A New Control Plane for 5G Network Architecture With a Case Study on Unified Handoff, Mobility, and Routing

Open access

M. Walenczykowska and A. Kawalec

transform and neural network”, Electronics - Constructions, Technologies, Behaviours 53, 120-123 (2012), (in Polish). [17] M. Walenczykowska and A. Kawalec, “Analysis of automatic modulation recognition algorithm for Cognitive Radio (CR) and radio intelligence (SIGINT)”, Electronics - Constructions, Technologies, Behaviours 56, 36-39 (2015), DOI:10.15199/13.2015.4.7. [18] P.S. Addison, The Illustrated Wavelet Handbook, Introuctory Theory and Applications in Science, Engeneering, Medicine and Finance, Taylor and Francis Group, London, 2002

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Łukasz Brocki and Krzysztof Marasek

References 1. A ckley D., H inton G., S ejnowski T. (1985), A Learning Algorithm for Boltzmann Machines , Cognitive Science, 9 , 1, 147–169. 2. B ishop C.M. (1995), Neural networks for pattern recognition , Oxford University Press, ISBN 0-19-853864-2. 3. B rocki Ł., K orzinek D., M arasek K. (2006), Recognizing Connected Digit Strings Using Neural Networks , TSD 2006, Brno, Czech Republic. 4. D ahl G.E., Y u D., D eng L., A cero A. (2011), Large vocabulary continuous speech recognition with context-dependent DBN