Abstract:
This paper presents results on deep learning-based signal recognition and channel estimation using orthogonal frequency-division multiplexing (OFDM) systems. Here, deep l...Show MoreMetadata
Abstract:
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.
Published in: 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)
Date of Conference: 17-18 March 2023
Date Added to IEEE Xplore: 05 May 2023
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Learning ,
- Signal Detection ,
- Channel Signal ,
- Channel Estimation ,
- Orthogonal Frequency Division Multiplexing ,
- Orthogonal Frequency Division Multiplexing System ,
- Mean Square Error ,
- Deep Learning Models ,
- Minimum Mean ,
- Minimum Mean Square Error ,
- Minimum Mean Square ,
- Cyclic Prefix ,
- Channel State Information Estimation ,
- Neural Network ,
- Signal-to-noise ,
- Least-squares ,
- Artificial Neural Network ,
- Deep Neural Network ,
- Black Box ,
- Online Training ,
- Deep Neural Network Model ,
- Bit Error Rate ,
- Pilot Sequences ,
- Pilot Symbols ,
- Channel Model ,
- Data Block ,
- Flat Fading ,
- Channel Equalization
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Learning ,
- Signal Detection ,
- Channel Signal ,
- Channel Estimation ,
- Orthogonal Frequency Division Multiplexing ,
- Orthogonal Frequency Division Multiplexing System ,
- Mean Square Error ,
- Deep Learning Models ,
- Minimum Mean ,
- Minimum Mean Square Error ,
- Minimum Mean Square ,
- Cyclic Prefix ,
- Channel State Information Estimation ,
- Neural Network ,
- Signal-to-noise ,
- Least-squares ,
- Artificial Neural Network ,
- Deep Neural Network ,
- Black Box ,
- Online Training ,
- Deep Neural Network Model ,
- Bit Error Rate ,
- Pilot Sequences ,
- Pilot Symbols ,
- Channel Model ,
- Data Block ,
- Flat Fading ,
- Channel Equalization
- Author Keywords