Nirmalkumar S. Reshamwala; Pooja S. Suratia; Satish K. Shah

Artificial Neural Network trained by Genetic Algorithm for Smart MIMO Channel Estimation for Downlink LTE-Advance System

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Author(s)

Nirmalkumar S. Reshamwala ^1,* Pooja S. Suratia ¹ Satish K. Shah ¹

1. Department of Electrical Engineering, The Maharaja Sayajirao University of Baroda Vadodara, Gujarat, India.

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2014.03.02

Received: 15 Jun. 2013 / Revised: 11 Oct. 2013 / Accepted: 2 Dec. 2013 / Published: 8 Feb. 2014

Index Terms

LTE-A, MIMO, Artificial Neural Network (ANN), Back-Propagation, Layered Recurrent Neural Network (LRN), Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN), Genetic Algorithm (GA)

Abstract

Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a ﬂat radio-network architecture and signiﬁcant increase in spectrum efficiency, throughput and user capacity. In this paper, performance analysis of robust channel estimators for Downlink Long Term Evolution-Advanced (DL LTE-A) system using three Artificial Neural Networks: Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN) and Layered Recurrent Neural Network (LRN) are trained separately using Back-Propagation Algorithm and also ANN is trained by Genetic Algorithm (GA). The methods use the information got by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase it estimates the channel matrix to improve performance of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator in MATLAB software. Performance of the proposed channel estimator, ANN trained by Genetic Algorithm (ANN-GA) is compared with traditional Least Square (LS) algorithm and ANN based other estimator like Feed-forward neural network, Layered Recurrent Neural Network and Cascade-forward neural network for Closed Loop Spatial Multiplexing (CLSM)-Single User Multi-input Multi-output (MIMO-2×2 and 4×4) in terms of throughput. Simulation result shows proposed ANN-GA gives better performance than other ANN based estimations methods and LS.

Cite This Paper

Nirmalkumar S. Reshamwala, Pooja S. Suratia, Satish K. Shah, "Artificial Neural Network trained by Genetic Algorithm for Smart MIMO Channel Estimation for Downlink LTE-Advance System", International Journal of Computer Network and Information Security(IJCNIS), vol.6, no.3, pp.10-19, 2014. DOI:10.5815/ijcnis.2014.03.02

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