Efficient Spectrum Sensing in Cognitive Radio Networks Using Hybridized Particle Swarm Intelligence and Ant Colony Algorithm
Cognitive Radio is a technology that enables unlicensed users (referred to as Secondary users) to use the spectrum of the licensed users (i.e. the Primary Users) whenever the licensed user is not transmitting data. This utilizes the spectrum efficiently. Cognitive Radio Networks (CRNs) detect Primary Users (PUs) using spectrum sensing and utilizes the spectrum holes (or vacant bands) for the transmission of data of Secondary User (SU). Spectrum sensing is carried out in a fixed time period called ‘Time Frame’. This Time Frame is divided into sensing time and transmission time. Higher sensing time will lead to better detection of PU but will lead to lesser transmission time and hence lesser throughput. On the contrary, if transmission time is higher, then sensing time is less, so PU detection will be compromised. It also leads to PU interference. There is a need of a tradeoff between sensing time and transmission time. Thus, there is a need for some optimal sensing time at which there is maximum possible throughput and no interference with the licensed user. This paper proposes a hybridized Ant Colony Optimization (ACO) - Particle Swarm Optimization (PSO) technique for spectrum sensing in cognitive radio networks. The results depict that the proposed technique is better than standalone optimization technique in terms of total error rate, throughput, probability of detection for varying sensing time and probability of false alarm.
Copyright © 2017 Praise Worthy Prize - All rights reserved.
T. Yucek, H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications, IEEE Communications Surveys & Tutorials, Volume 11, (Issue 1), January 2009, Pages 116-130.
Orumwense, E., Oyerinde, O., Mneney, S., Impact of Primary User Emulation Attacks on Cognitive Radio Networks, (2014) International Journal on Communications Antenna and Propagation (IRECAP), 4 (1), pp. 19-26.
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.
H. Prahan, S.S. Kalamkar, A. Banerjee, Sensing-throughput tradeoff in cognitive radio with random arrivals and departures of multiple primary users, IEEE Communications Letters, Volume 19, (Issue 3), March 2015, Pages 415-418.
Sahoo, A., Mishra, G., Jena, M., Mangaraj, B., Optimal Design and Comparative Study of Circular Patch Antennas Using Different Feeds for WLAN and WiMAX Applications, (2016) International Journal on Communications Antenna and Propagation (IRECAP), 6 (3), pp. 188-196.
Hannane, A., Fizazi, H., Metaheuristics and Neural Network for Satellite Images Classification, (2016) International Review of Aerospace Engineering (IREASE), 9 (4), pp. 107-113.
Aruchamy, S., Vijayakumar, P., Senthilkumar, A., Design of Ant Colony Optimized Shunt Active Power Filter for Load Compensation, (2014) International Review of Electrical Engineering (IREE), 9 (4), pp. 725-734.
Saraereh, O., Al Saraira, A., Alsafasfeh, Q., Arfoa, A., Bio-Inspired Algorithms Applied on Microstrip Patch Antennas: a Review, (2016) International Journal on Communications Antenna and Propagation (IRECAP), 6 (6), pp. 336-347.
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 (IREMOS), 7 (4), pp. 543-553.
Khoucha, F., Habib, M., Benbouzid, M., Kheloui, A., Rule-Based Energy Management Strategy Optimized Using PSO for Fuel Cell/Battery Electric Vehicle, (2015) International Journal on Energy Conversion (IRECON), 3 (1), pp. 10-16.
Y. Meng, O. Kazeem, J.C. Muller, A Hybrid ACO/PSO control algorithm for distributed swarm robots, IEEE Swarm Intelligence Symposium, pp. 273-280, Honolulu, USA, April 2007.
R.A. Rashid, A.H.F.B.A. Hamid, N. Fisal, S.K.S. Yusof, H. Hosseini, A. Lo, A. Farzamnia, Efficient in-band spectrum sensing using swarm intelligence for cognitive radio network, Canadian Journal of Electrical and Computer Engineering, Volume 38, (Issue 2), February 2015, Pages 106-115.
C. Blum, Ant Colony Optimization: Introduction and recent trends, Physics of Life Reviews, Volume 2, (Issue 4), December 2005, Pages 353-373.
G. R. Huang, X.B. Cao, X.F. Wang, An Ant Colony Optimization algorithm based on pheromone diffusion, Acta Electronica Sinica, Volume 32, (Issue 5), 2004, Pages 865-868.
C. Chen, F. Ye, Particle Swarm Optimization algorithm and its application to clustering analysis, IEEE Conference on Electrical Power Distribution Networks, Vol. 17, pp. 22-27, Tehran, Iran, May 2012.
Y. Liang, Y. Zeng, E.C.Y. Peh, A.T. Hoang, Sensing-throughput trade-off for cognitive radio networks, IEEE Transactions on Wireless Communications, Volume 7, (Issue 4), April 2008, Pages 1326-1337.
Q. Bai, Analysis of Particle swarm optimization algorithm, Computer and Information Science, Volume 3, (Issue 1), January 2010, Page 180-189.
M. Imran, R. Hashim, N.E.A. Khalid, An overview of Particle swarm optimization variants, Procedia Engineering, Volume 53,(Issue 1), January 2013, Pages 491-496.
M. Dorigo, C. Blum, Ant Colony Optimization theory: A Survey, Theoretical Computer Science, Volume 344, (Issue 2-3), November 2005, Pages 243-278.
V. Selvi, R. Umarani, Comparative analysis of Ant colony and Particle swarm optimization techniques, International Journal of Computer Applications, Volume 5, (Issue 4), August 2010, Pages 1-6.
Q. Li, Z. Li, J. Shen, R. Gao, A novel spectrum sensing method in cognitive radio based on suprathreshold stochastic resonance, IEEE International Conference on Communication, pp. 4426-4430, Ottawa, Canada, June 2012.
Y. Hur, J. Park, W. Woo, K. Lim, C. Lee, H.S. Kim, J. Laskar, A wideband analog multi-resolution spectrum sensing (MRSS) technique for cognitive radio (CR) systems, IEEE International Symposium on Circuits and Systems, pp. 4090-4093, Island of Kos, Greece, May 2006.
A.E. Omer, Review of spectrum sensing techniques in cognitive radio networks, IEEE International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, pp. 439-446, Khartoum, Sudan, September 2015.
E.C.Y. Peh, Y. Liang, Y.L. Guan, Y. Zeng, Optimization of cooperative sensing in cognitive radio networks: A sensing- throughput tradeoff view, IEEE Transactions on Vehicular technology, Volume 58, (Issue 9), November 2009, Pages 5294-5299.
C. Huang, W. Huang, H. Chang, Y. Yeh, C. Tsai, Hybridization strategies for continuous Ant colony optimization and Particle swarm optimization applied to data clustering, Applied Soft Computing, Volume 13, (Issue 9), September 2013, Pages 3864-3872.
Ibrahim, H., Mahmoud, A., DC Motor Control Using PID Controller Based on Improved Ant Colony Algorithm, (2014) International Review of Automatic Control (IREACO), 7 (1), pp. 1-6.
Zongo, O., Oonsivilai, A., Comparison between Harmony Search Algorithm, Genetic Algorithm and Particle Swarm Optimization in Economic Power Dispatch, (2015) International Review of Electrical Engineering (IREE), 10 (2), pp. 286-292.
Kerdphol, T., Qudaih, Y., Mitani, Y., Optimal Battery Energy Storage Size Using Particle Swarm Optimization for Microgrid System, (2015) International Review of Electrical Engineering (IREE), 10 (2), pp. 277-285.
Abadi, I., Musyafa’, A., Soeprijanto, A., Design and Implementation of Active Two Axes Solar Tracking System Using Particle Swarm Optimization Based Fuzzy Logic Controller, (2015) International Review on Modelling and Simulations (IREMOS), 8 (6), pp. 640-652.
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.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2020 Praise Worthy Prize