Ref. | Summary and focus | Muticarrier systems | Dedicated to AI-based channel estimation? | AI-based channel estimation discussion | ||
---|---|---|---|---|---|---|
Brief | Moderate | Extensive | ||||
[21] | A discussion about the motivations for employing AI-enabled cellular networks. | OFDM | No | \(\checkmark\) | Â | Â |
[22] | A discussion about key requirements, challenges, deployment strategies, and enabling technologies for applying deep learning in 6G future communication systems physical layer. | OFDM | No | \(\checkmark\) | Â | Â |
[23] | A summary of deep learning-based physical layer application in 5G wireless communication systems. | OFDM | No | \(\checkmark\) | Â | Â |
[24] | A comparison of deep learning-based channel estimation with conventional methods. | OFDM | Yes | Â | \(\checkmark\) | Â |
[39] | An overview of deep learning usage in a wireless networks, comprising different layers. | OFDM | No | \(\checkmark\) | Â | Â |
[40] | A survey on massive MIMO channel techniques for modeling and estimation. | OFDM | No | \(\checkmark\) | Â | Â |
[41] | An extensive survey on deep learning in mobile and wireless networks. | OFDM | No | \(\checkmark\) | Â | Â |
[32] | A machine learning techniques overview to solve different challenges in wireless networks. | OFDM | No | \(\checkmark\) | Â | Â |
[33] | A review of applications of machine learning techniques for the next-generation wireless network. | OFDM | No | \(\checkmark\) | Â | Â |
[30] | A discussion about mMIMO channel estimation techniques using deep learning. | OFDM | No | \(\checkmark\) | Â | Â |
[42] | A discussion about deep learning in terms of model-based block architecture and algorithm design for wireless communication. | OFDM | No | \(\checkmark\) | Â | Â |
[43] | A comprehensive survey on machine learning applications in the vehicular network context. | OFDM | No | Â | \(\checkmark\) | Â |
[44] | A survey of four intelligent signal processing topics for the wireless physical layer of MIMO systems: modulation classification, signal detection, beamforming, and channel estimation. | OFDM | No | Â | \(\checkmark\) | Â |
[45] | A discussion about several novel deep learning applications for the physical layer. | – | No | \(\checkmark\) |  |  |
[46] | A physical layer review of the challenges of machine learning in wireless communication. | OFDM | No | \(\checkmark\) | Â | Â |
[47] | Performance analysis of machine learning applied to channel estimation. | OFDM | Yes | Â | \(\checkmark\) | Â |
[48] | A tutorial on recurrent neural networks for channel prediction. | OFDM | Yes | Â | \(\checkmark\) | Â |
[49] | A brief review of deep learning channel estimation techniques for wireless systems. | OFDM | Yes | Â | \(\checkmark\) | Â |
[50] | A comprehensive overview of model-driven deep learning in physical layer communications. | OFDM | No | \(\checkmark\) | Â | Â |
[31] | A discussion about deep learning-based block-structured functions for the physical layer and deep learning-based end-to-end communication systems. | OFDM | No | \(\checkmark\) | Â | Â |
[51] | An overview of deep learning wireless communication systems applied to the physical layer. | OFDM | No | \(\checkmark\) | Â | Â |
This work | A comprehensive survey of AI-based channel estimation techniques for multicarrier systems comprising classical machine learning techniques and neural networks. | OFDM, GFDM, FBMC, UMFC | Yes | Â | Â | \(\checkmark\) |