MIMO OFDM Blind Channel Equalization using Multilayer Neural Network in Impulsive Noise Environment
S. P. Girija1, Rameshwar Rao2
1S. P. Girija*, Research Scholar, Department of ECE, OUCE, Osmania University, Hyderabad, Telangana, India.
2Rameshwar Rao, Professor, Department of ECE, OUCE, Osmania University, Hyderabad, Telangana, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 30, 2020. | PP: 256-261 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7134038620/2020©BEIESP | DOI: 10.35940/ijrte.F7134.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Multiple Input Multiple Output (MIMO) system has several input and output antennas for executing the data transmission. Channel Estimation (CE) is required in MIMO, to achieve the effective signal transmission over the various amount of antennas. By using CE over the MIMO, the noiseless data transmission is performed. Hence in this paper, a Multi-layer Neural Network (MNN) is used for identifying the CE and this system is named as Multi-layer Neural Network-MIMO-Digital Filter (MNN-MIMO-CE) is proposed for blind channel equalization. The MNN-MIMO-CE has Feed forward Artificial Neural Network (FANN) with back propagation in Levenberg-Marquardt (LM) algorithm and it has two processes MNN training and MNN testing. LM algorithm is used to train the MNN. These processes are used to provide the CE for different combination of antennas. The performance of the MNN-MIMO-CE method is evaluated in comparison with the existing method [25] through simulations using BER as the performance measure.
Keywords: Multiple input multiple output, Feed forward neural network, Back propagation, Levenberg-Marquardt algorithm, Channel estimation, Signal to noise ratio and Bit error rate.
Scope of the Article: Neural Network