Abstract
The research described in this paper shows how by combining CoMP with adaptable semi-smart antennas it is possible to improve the throughput of an OFDMA LTE system and at the same time reduce the power required for transmission; i.e. providing better performance at lower cost. Optimisation of antenna patterns is performed by a Genetic Algorithm to satisfy an objective function that considers throughput, number of UEs handled as well as power in the transmission. It is shown that the dominant effect is using the semi-smart antennas and that the results are not sensitive to small amounts of movement.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Marsch, P., Fettweis, G.P.: Coordinated Multi-Point in Mobile Communications: From Theory to Practice. Cambridge University Press, New York (2011)
Yang, X., Wang, Y., Zhang, T., Cuthbert, L., Xiao, L.: Combining CoMP with semi-smart antennas to improve performance. Electron. Lett. 47(13), 775–776 (2011)
Zhou, S., Zhao, M., Xu, X., Wang, J., Yao, Y.: Distributed wireless communication system: a new architecture for future public wireless access. IEEE Commun. Mag. 41(3), 108–113 (2003)
Nahi, P., Parini, C.G., Papadopoulos, S., Du, L., Bigham, J., Cuthbert, L.: A semi-smart antenna concept using real-time synthesis for use in a distributed load balancing scheme for cellular networks. In: Antennas and Propagation, (ICAP 2003), vol. 1, pp. 168–171 (2003)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)
Carroll, D.L.: Chemical laser modeling with genetic algorithms. AIAA J. 34(2), 338–346 (1996)
Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)
Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Method Appl. Mech. Eng. 186(2), 311–338 (2000)
Powell, D., Skolnick, M.M.: Using genetic algorithms in engineering design optimization with non-linear constraints. In: 5th International conference on Genetic Algorithms, pp. 424–431 (1993)
Mehra, M., Jayalal, M.L., Arul, J., Rajeswari, S., Kuriakose, K.K., Murty, S.A.V.S.: Study on different crossover mechanisms of genetic algorithm for test interval optimization for nuclear power plants. Int. J. Intell. Syst. Appl. 6(1), 20 (2013)
DeJong, K.: An analysis of the behavior of a class of genetic adaptive systems. Ph.D. thesis, University of Michigan (1975)
Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE Trans. Syst. Man Cybern. 16(1), 122–128 (1986)
Yang, X., Wang, Y., Zhang, D., Cuthbert, L.G.: Resource allocation in LTE OFDMA systems using genetic algorithm and semi-smart antennas. In: Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE, Sydney (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Y., Yang, X., Cuthbert, L., Zhang, T., Xiao, L. (2017). Improving Performance and Energy Efficiency for OFDMA Systems Using Adaptive Antennas and CoMP. In: Kim, K., Joukov, N. (eds) Information Science and Applications 2017. ICISA 2017. Lecture Notes in Electrical Engineering, vol 424. Springer, Singapore. https://doi.org/10.1007/978-981-10-4154-9_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-4154-9_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4153-2
Online ISBN: 978-981-10-4154-9
eBook Packages: EngineeringEngineering (R0)