Rayleigh Fading MIMO Channel Prediction Using RNN with Genetic Algorithm
The spectral efficiency and reliability of Multi-Input Multi-Output (MIMO) systems greatly depends on the prediction result of channel state information (CSI), such that the transmitter and/or the receiver have perfect knowledge of CSI. The employment of linear predictors used for narrow-band prediction has produced poor results in prediction of correlation coefficients of the channel in the presence of received data that has undergone non-linear distortions. Hence, one of the potential solutions to this challenge is artificial neural networks (ANN). In this paper we used fully connected recurrent neural network to predict the Rayleigh fading channel coefficients using genetic algorithm based learning, which is compared with split complex real time and fully complex real time based recurrent learning process.
KeywordsMulti-Input Multi-Output (MIMO) Neural Network Split Complex Real Time Recurrent Learning (SCRTRL) Fully Complex Real Time Recurrent Learning (FCRTRL) Genetic Algorithm (GA)
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- 1.Paulraj, A.J., Gore, D.A., Nabar, R.U., Bolcskei, H.: An overview of mimo communication-a key to gigabit wireless. In: Proceeding of the IEEE, vol. 92, pp. 198–218 (February 2004)Google Scholar
- 3.Proakis, J.G., Salehi, M.: Digital Communication, 5th edn. Mc Graw Hill (2008)Google Scholar
- 4.Huang, J., Winters, J.: Sinusoidal modelling and prediction of fast fading processes. In: Global Tel. Conf., pp. 892–897 (November 1998)Google Scholar
- 5.Andersen, J., Jensen, J., Jensen, S., Frederiksen, F.: Prediction of future fading based on past measurements. In: IEEE Vehicular Technology Conference, pp. 151–155 (1999)Google Scholar
- 8.Sarma, K.K., Mitra, A.: Estimation of MIMO channels using complex time delay fully Recurrent Neural Network. In: 2nd National Conference on Emerging Trends and Applications in Computer Science (NCETACS), March 4-5 (2011)Google Scholar
- 9.Yoo, T., Goldsmith, A.: Capacity of fading mimo channels with channel estimation error. IEEE Commun. Soc., 808–813 (2004)Google Scholar
- 11.Yoo, T., Yoon, E., Goldsmith, A.: Mimo capacity with channel uncertainty: Does feedback help? In: Proceedings of IEEE GlobeCom, pp. 96–100 (2004)Google Scholar
- 14.Goldberg, D.E.: Genetic Algorithms in search. Optimization and Machine Learning. Pearson Plc (2011)Google Scholar