Channel Estimation of Wirless Communication Systems using Neural Networks
Dr. P. Satyanarayana1, G. Durga Tushara2, G. Tejaswini3
1Dr. P. Satyanarayana, Department of Electronics and Communication, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India.
2Gudimetla Durga Tushara, Department of Electronics and Communication, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India.
3Gudimetla Tejaswini, Department of Electronics and Communication, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India.
Manuscript received on 09 April 2019 | Revised Manuscript received on 17 May 2019 | Manuscript published on 30 May 2019 | PP: 676-679 | Volume-8 Issue-1, May 2019 | Retrieval Number: F2812037619/19©BEIESP
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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: An exceptional circumstance of multiple-carrier transmission is Orthogonal Frequency Division Multiplexing (OFDM). It can domicile more data rate which will be helpful for multimedia wireless communications. The estimation of channel is an intrinsic parts. The understanding of channel estimation of OFDM systems is strenuous. So a Deep learning (DL) technique for channel estimation of OFDM is presented .The traditional techniques estimate channel characteristics first and then reconstruct the original data by means of expected Channel characteristics. Technique, which is projected in this paper first, evaluates Channel characteristics indirectly and reconstructs original data. A model on Deep Learning is first developed by means of the output obtained from model depending on channel characteristics, it helps to reconstruct original transferred symbols implicitly. The outcomes, which are obtained, are compared to Minimum Mean Square Error (MMSE) estimator. DL was a hopeful technology for channel estimation in wireless communications with channel disturbance.
Keywords: Channel Characteristics, Channel Disturbance, Deep Learning, Interference
Scope of the Article: Deep Learning