Multiple Input Multiple Output Detection Using Roulette Wheel Based Ant Colony Optimization Technique
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Multiple Input Multiple Output Detection Using Roulette Wheel Based Ant Colony Optimization Technique

Authors: B. Rebekka, B. Malarkodi

Abstract:

This paper describes an approach to detect the transmitted signals for 2×2 Multiple Input Multiple Output (MIMO) setup using roulette wheel based ant colony optimization technique. The results obtained are compared with classical zero forcing and least mean square techniques. The detection rates achieved using this technique are consistently larger than the one achieved using classical methods for 50 number of attempts with two different antennas transmitting the input stream from a user. This paves the path to use alternative techniques to improve the throughput achieved in advanced networks like Long Term Evolution (LTE) networks.

Keywords: MIMO, ant colony optimization, roulette wheel, soft computing, LTE.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125595

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1076

References:


[1] Cappozi. F, Piro. G, Grieco. L. A, Boggia. G, and Camarda. P., ”Downlink packet scheduling in LTE cellular networks: Key design issues and a survey”, IEEE Communications Surveys and Tutorials, Vol. 15, No. 2, pp. 678-700, 2013.
[2] Dorigo. M, Birattari. M, Stiitzle. T., ”Ant colony optimization. Artificial ants as a computational intelligence technique”, IEEE Computational Intelligence Magazine, pp. 28-39, 2006.
[3] Zhao. Y, Xu. X, Hao. Z, Tao. X, and Zhang. P., ”Resource allocation in multiuser OFDM system based on ant colony optimization”, IEEE Wireless Communications and Networking Conference (WCNC), pp. 1 6, 2010.
[4] Xu. C, Yang. L. -L., and Hanzo . L., ”Ant-colony-based multiuser detection for MC DS- CDMA systems”, IEEE 66th Vehicular Technology Conference, pp. 960 964, 2007.
[5] Khurshid. K, Irteza. S, and Khan. A. A., ”Application of ant colony optimization based algorithm in MIMO detection”, CEC10 - IEEE Congress on Evolutionary Computation, pp. 17, 2010.
[6] Jos Carlos. M and Taufik. A., ”Lattice reduction aided detector for MIMO communication using Ant colony optimization”,Wireless Personal Communications, Springer, 77(1):63-85, 2013.
[7] Khawaja Tauseef Tasneem, ”Reduced complexity detection techniques for multi antenna communication systems”, Ph.D. Thesis, University of Canterbury, New Zealand, 2013.
[8] Kailath. T, Vikalo. H, and Hassibi. B, ”MIMO Receive algorithms, Space-Time Wireless systems: From Array processing to MIMO Communications”, Cambridge University Press, 2005.
[9] Ayyoub. A. M , Sabira. K, Borhanuddin. M. A, Raja S. A. R. Abdullah., ”QoS based fair load-balancing: Paradigm to IANRA Routing algorithm for wireless networks”, Proceedings of 11th International Conference on Computer and Information Technology, Bangladesh, 2008.
[10] Lain. J. K and Lai.J. J, ”Ant Colony Optimisation-Based multiuser detection for Direct-Sequence CDMA Systems with diversity reception”, IET Communications, 1(4): 556561, 2007.
[11] Vincent Poor. H., ”An Introduction to signal detection and estimation”, 2nd edition, Springer-Verlag, 1994.
[12] Gopi. E. S., ”Algorithm collections for digital signal processing applications using matlab”, Springer, 2007.
[13] Rakesh. R and Jyotishree, ”Blending Roulette wheel selection and Rank selection in Genetic Algorithms”, International Journal of Machine Learning and Computing, Vol. 2, No. 4, August 2012.