Channel Estimation and Signal Detection in OFDM Systems using Deep Learning

This paper presents results on deep learning-based signal recognition and channel estimation using orthogonal frequency-division multiplexing (OFDM) systems. Here, deep learning is used to fully regulate wireless OFDM channels. Instead of estimating CSI explicitly before identifying or recovering the broadcast symbols using the estimated CSI, as is the case with standard OFDM receivers, The suggested deep learning-based solution directly recovers the transmitted symbols while implicitly estimating channel state information (CSI). A deep learning model is initially trained offline using data creation in order to eliminate channel distortion based on channel characteristics, and it is then utilized to directly extract the live transmitted data. The simulation outcomes exhibit the potential of the deep learning-based method can identify transmitted symbols and correct for channel distortion with a level of performance equivalent to the minimum mean square error (MMSE) estimator. Less training pilots, the removal of the cyclic prefix (CP), and the presence of nonlinear clipping noise make deep learning approaches more trustworthy than conventional procedures. For channel estimation and signal recognition deep learning is an effective method in wireless communications characterized by intricate channel distortion and interference.