Implementation of Particle Swarm Optimization Technique for Spectrum Sensing in Cognitive Radio Network
The scarcity of spectrum can be overcome by using the spectrum efficiently and effectively. There is a need for devising techniques that can help in sensing the spectrum judiciously so that the Secondary Users can optimally access the spectrum holes. This will support higher transmission of data, thereby enhancing the throughput. Further, the presence of Primary Users should be detected accurately in order to avoid interference. The sensing-throughput trade-off problem of spectrum sensing stage of cognitive radios can be optimized by means of computational intelligence techniques like swarm intelligence. This paper implements Particle Swarm Optimization technique to optimize Throughput, Probability of false alarm and sensing time under varying Signal-to-Noise Ratio conditions. It has been studied that bigger swarm-size does not improve the results, but takes longer processing time.
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L. Giupponi, A. Galindo-Serrano, P. Blasco, M. Dohler, Docitive networks: An emerging paradigm for dynamic spectrum management [Dynamic Spectrum Management], IEEE Wirel. Commun., Volume 17, (Issue 4), April 2010, Pages 47–54.
R. Garg, N. Saluja, Current trends and research challenges in spectrum-sensing for Cognitive radios, Int. J. Adv. Comput. Sci. Appl., Volume 7, (Issue 7), July 2016, Pages 403–408.
S. Ozbay, E. Ercelebi, A new wireless network scheme for spectrum sensing in cognitive radio, Elektron. ir Elektrotechnika, Volume 21,(Issue 6), June 2015, Pages 90–95.
T. Z. Oo, N. H. Tran, D. N. M. Dang, Z. Han, L. B. Le, C. S. Hong, OMF-MAC: An opportunistic matched filter-based MAC in cognitive radio networks, IEEE Trans. Veh. Technol., Volume 65, (Issue 4), April 2016, Pages 2544–2559.
Orumwense, E., Afullo, T., Srivastava, V., Using Massive MIMO and Small Cells to Deliver a Better Energy-Efficient Cognitive Radio Network, (2016) International Journal on Communications Antenna and Propagation (IRECAP), 6 (5), pp. 274-281.
Hashmi, S., Sattar, S., Soundararajan, K., Multi Objective Coordination Approach for Resource Utilization in Heterogeneous Cognitive Radio Network, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (1), pp. 72-79.
X. Xu, R. Qu, J. Zhao, B. Chen, Cooperative spectrum sensing in cognitive radio networks with kernel least mean square, IEEE International Conference on Information Science and Technology, Vol. 5, pp. 574–578, Changsha, China, April 2015.
Khaddaj Mallat, N., Zia, M., Mirza, N., Comparison of RLS and LMS Algorithms for Interference Cancelation in a Fixed Point to Point Microwave Link, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (5), pp. 448-456.
J. So, T. Kwon, Limited reporting-based cooperative spectrum sensing for multiband cognitive radio networks, AEU-Int. J. Electron. Commun., Volume 70, (Issue 4), April 2016, Pages 386–397.
S. Stotas, A. Nallanathan, Overcoming the sensing-throughput tradeoff in cognitive radio networks, IEEE International Conference on Communications, pp. 3–7, Cape Town, South Africa, May 2010.
P. Pandya, A. Durvesh, N. Parekh, Energy detection based spectrum sensing for cognitive radio network, IEEE International Conference on Communication Systems and Network Technologies, Vol. 5, pp. 201–206, Gwalior, India, October 2015.
H. Kim, K. G. Shin, In-band spectrum sensing in cognitive radio networks: Energy detection or feature detection?, ACM MobiCom, Vol.14, pp. 14–25, San Francisco, California, September 2008.
A. A. Sharifi, M. Sharifi, M. J. M. Niya, Secure cooperative spectrum sensing under primary user emulation attack in cognitive radio networks: Attack-aware threshold selection approach, AEU-Int. J. Electron. Commun., Volume 70, (Issue 1), January 2016, Pages 95–104.
J. Martyna, Cooperative spectrum sensing in cognitive radio networks with QoS requirements, Internet Things, Smart Spaces, Next Gener. Networks Syst., Volume 9247, August 2015, Pages 505–517.
M. J. Saber, S. M. S. Sadough, Optimal soft combination for multiple antenna energy detection under primary user emulation attacks,AEU-Int. J. Electron. Commun., Volume 69, (Issue 9), September 2015, Pages 1181–1188.
Alnabelsi, S., Saifan, R., Almasaeid, H., Improving Routing Performance Using Cooperative Spectrum Sensing in Cognitive Radio Networks, (2016) International Review on Computers and Software (IRECOS), 11 (10), pp. 923-930.
S. Sun, N. Chen, T. Ran, J. Xiao, T. Tian, A Stackelberg game spectrum sharing scheme in cognitive radio-based heterogeneous wireless sensor networks, Signal Processing, Volume 126, (Issue 9), September 2016, Pages 18–26.
R. A. Rashid, A. H. F. B. A. Hamid, N. Fisal, S. Y. Kamilah, H. Hosseini, A. Lo, and A. Farzamnia, Efficient in-band spectrum sensing using swarm intelligence for cognitive radio network, Can. J. Electr. Comput. Eng., Volume 38, (Issue 2),February 2015, Pages 106–115.
Jhajj, H., Garg, R., Saluja, N., Efficient Spectrum Sensing in Cognitive Radio Networks Using Hybridized Particle Swarm Intelligence and Ant Colony Algorithm, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (7), pp. 586-593.
C.-L. Huang, W.-C. Huang, H.-Y. Chang, Y.-C. Yeh, C.-Y. Tsai, Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering, Appl. Soft Comput., Volume 13, (Issue 9),September 2013, Pages 3864–3872.
A. Elrharras, R. Saadane, M. Wahbi, A. Hamdoun, Hybrid architecture for spectrum sensing algorithm based on energy detection technique and artificial neural networks, IEEE Workshop on Codes, Cryptography and Communication Systems, Vol. 5, pp. 40-44, El Jadida, Morocco., November 2014.
Zambrano, D., Salcedo, O., Espitia, M., Modelling and Predicting the Behaviour of a Secondary User in Cognitive Radio Using Artificial Intelligence Techniques, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (4), pp. 348-355.
X. Huang, G. Wang, F. Hu, S. Kumar, The impact of spectrum sensing frequency and packet- loading scheme on multimedia transmission over cognitive radio networks, IEEE Trans. Multimed., Volume 13, (Issue 4), April 2011, Pages 748–761.
J. Martyna, Oligopoly Bertrand model for price competition in cognitive radio networks, IEEE International Symposium on Communication Systems, Networks & Digital Signal Processing, Vol. 9, pp. 227–231, Manchester, UK, October 2014.
T. Yücek, H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications, IEEE Commun. Surv. Tutorials, Volume 11, (Issue 1), January 2009, Pages 116–130.
P. V. Tuan, I. Koo, Throughput maximisation by optimising detection thresholds in full-duplex cognitive radio networks, IET Commun., Volume 10, (Issue 11), November 2016, Pages 1355–1364.
D. Cabric, S. M. Mishra, R. W. Brodersen, Implementation issues in spectrum sensing for cognitive radios, IEEE Asilomar Conference on Signals, Systems and Computers, Vol. 38, pp. 772–776, Pacific Grove, CA, November 2004.
Kota, P., Gaikwad, A., Fireflies Algorithm Based Optimal Scrambling to Reduce PAPR in SFBC Based MIMO-OFDM, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (7), pp. 626-634.
Manh, L., Grimaccia, F., Mussetta, M., Zich, R., A Soft Computing Hybridization Technique for Antenna Optimization, (2015) International Journal on Communications Antenna and Propagation (IRECAP), 5 (1), pp. 16-20.
Muthukumar, K., Jayalalitha, S., Ramaswamy, M., PSO Embedded Artificial Bee Colony Algorithm for Optimal Shunt Capacitor Allocation and Sizing in Radial Distribution Networks with Voltage Dependent Load Models, (2015) International Review of Electrical Engineering (IREE), 10 (2), pp. 305-320.
K. Damodaran, S., Sunil Kumar, T., Combined Economic and Emission Short-Term Hydrothermal Scheduling Using Particle Swarm Optimization, (2015) International Review of Electrical Engineering (IREE), 10 (3), pp. 434-441.
Dileep, M., Surekha, K., Vishnu, N., Ascent Phase Trajectory Optimization of Launch Vehicle Using Theta-Particle Swarm Optimization with Different Thrust Scenarios, (2016) International Review of Aerospace Engineering (IREASE), 9 (6), pp. 200-207.
D. Adolfo, D. P. Andrea, D. N. L. Pio and M. Santolo, "PSO-PR power flow control of a single-stage grid-connected PV inverter," 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA), San Diego, CA, 2017, pp. 788-792.
Meo, S., Zohoori, A., Vahedi, A., Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach, (2016) Energy Conversion and Management, 110, pp. 230-239.
Del Pizzo, A., Meo, S., Brando, G., Dannier, A., Ciancetta, F., An energy management strategy for fuel-cell hybrid electric vehicles via particle swarm optimization approach, (2014) International Review on Modelling and Simulations, 7 (4), pp. 543-553.
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