Deep Learning Based Over-the-Air Channel Estimation Using a ZYNQ SDR Platform
- Resource Type
- Periodical
- Authors
- Banerjee, B.; Khan, Z.; Lehtomaki, J.J.; Juntti, M.
- Source
- IEEE Access Access, IEEE. 10:60610-60621 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Channel estimation
Symbols
OFDM
Wireless communication
Deep learning
Convolutional neural networks
5G mobile communication
single-input-single-output
single-input-multiple-output
convolutional neural networks
deep learning
OTA transmission
Zynq FPGA
- Language
- ISSN
- 2169-3536
Deep learning based channel estimation techniques have recently found an overwhelming interest owing to data-driven learning-based adaptability compared to conventional estimation techniques which rely on model-based approach. This paper exploits the availability of low cost software defined radio (SDR) devices to implement and test over-the-air (OTA) deep learning driven channel estimation solutions in realistic settings for 5G and beyond wireless communications. We have designed and implemented a ZYNQ SDR-based deep learning driven channel estimation platform which utilises real-world 5G new radio (NR) signals to develop and test the performance of a deep learning solution for wireless channel estimators. To this end, we have considered the time-frequency response of an OTA communication channel for single-input-single-output (SISO) and single-input-multiple-output (SIMO) networks under various line-of-sight and non-line-of-sight scenarios as a two-dimensional image. We have utilised convolutional deep learning technique for channel estimation. The main purpose is to use OTA samples obtained via the SDR platform to determine the unknown values of the channel response using known values at the pilot locations and evaluate performance compared to conventional technique. Our results show that the performance of the deep learning channel estimator using OTA data is as good as that of conventional methods with the flexibility to adapt based on data analytics of the time-varying nature of the channel.