Channel estimation of wirless communication systems using neural networks

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.