Parameter Optimization for Conventional Soft Frequency Reuse in Multi Cell Networks using Extreme Learning Machine and Genetic Algorithm

Authors

  • M. F. Misran Electronic System Engineering, MJIIT.
  • M. I. Shapiai Electronic System Engineering, MJIIT. Centre of Artificial Intelligence and Robotics (CAIRO).
  • R. A. Dziyauddin RAZAK School Technology and Engineering Advanced, UTM Kuala Lumpur, Malaysia.
  • H. M. S. A. Jalil Electronic System Engineering, MJIIT.

Keywords:

Soft Frequency Reuse, Extreme Learning Machine, Genetic Algorithm, LTE

Abstract

Soft Frequency reuse (SFR) has been used as a method to reduce the inter-cell interference between cell edge user and cell center users. With a proper adjustment of the system, it can gives us the best throughput of the SFR. The objective of this study is to optimize the three parameters of the SFR obtained from Taguchi model. These three parameters consist of power ratios of the base station (β), concentration of cell-edge user and user distribution of sub-subcarriers. Firstly, the SFR data is modeled using Extreme Learning Machine (ELM). The model is then validated using the unseen samples based on the mean square error (MSE) criterion. Secondly, the best model with the lowest MSE is then used for the parameters optimization by using genetic algorithm (GA) in order to obtain the highest throughput. In the series of experiments, the obtained results offer the highest throughput with reliable and consistent selected parameters. In this paper, the combination ELM-GA technique has been proven to increase the throughput of the system.

References

S. Sesia, I. Toufik, and M. Baker, LTE-the UMTS long term evolution: Wiley Online Library, 2015.

M. Sauter, "Long Term Evolution (LTE)," in Grundkurs Mobile Kommunikationssysteme, ed: Springer, 2013, pp. 229-296.

G. L. Stüber, Principles of mobile communication: Springer Science & Business Media, 2011.

J. H. Koo, W. B. Lee, S. N. Kim, B. C. Ihm, and H. S. Ko, "Method for estimating channel state in a wireless communication system using fractional frequency reuse and mobile station using the same," ed: Google Patents, 2015.

N. Saquib, E. Hossain, and D. I. Kim, "Fractional frequency reuse for interference management in LTE-advanced hetnets," IEEE Wireless Communications, vol. 20, pp. 113-122, 2013.

G. Giambene and T. A. Yahiya, "LTE planning for soft frequency reuse," in Wireless Days (WD), 2013 IFIP, 2013, pp. 1-7.

M. Qian, W. Hardjawana, Y. Li, B. Vucetic, J. Shi, and X. Yang, "Inter-cell interference coordination through adaptive soft frequency reuse in LTE networks," in 2012 IEEE Wireless Communications and Networking Conference (WCNC), 2012, pp. 1618-1623.

M. Bohge, J. Gross, and A. Wolisz, "Optimal power masking in soft frequency reuse based OFDMA networks," in Wireless Conference, 2009. EW 2009. European, 2009, pp. 162-166.

R. A. Dziyauddin, F. Cao, and Y. Jin, "An adaptive SFR in multicell networks," in 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2013, pp. 1764-1768.

K. Hornik, "Approximation capabilities of multilayer feedforward networks," Neural networks, vol. 4, pp. 251-257, 1991.

S. Lu, X. Wang, G. Zhang, and X. Zhou, "Effective algorithms of the Moore-Penrose inverse matrices for extreme learning machine," Intelligent Data Analysis, vol. 19, pp. 743-760, 2015.

G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, pp. 489-501, 2006.

J. H. Holland, "Genetic algorithms," Scientific american, vol. 267, pp. 66-72, 1992.

T. Chow, G. Zhang, Z. Lin, and C. Song, "Global optimization of absorption chiller system by genetic algorithm and neural network," Energy and buildings, vol. 34, pp. 103-109, 2002.

D. F. Cook, C. T. Ragsdale, and R. Major, "Combining a neural network with a genetic algorithm for process parameter optimization," Engineering applications of artificial intelligence, vol. 13, pp. 391-396, 2000.

A. Maliha, R. Yusof, and A. Madani, "Online sequential-extreme learning machine based detector on training-learning-detection framework," in Control Conference (ASCC), 2015 10th Asian, 2015, pp. 1-5.

Downloads

Published

2016-12-01

How to Cite

Misran, M. F., Shapiai, M. I., Dziyauddin, R. A., & A. Jalil, H. M. S. (2016). Parameter Optimization for Conventional Soft Frequency Reuse in Multi Cell Networks using Extreme Learning Machine and Genetic Algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(11), 23–28. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1405