Abstract
We propose a new Machine Learning (ML) approach to channel estimation (CE) in Massive Multiple Input Multiple Output (MIMO) receivers. The algorithm employs a recurrent neural network (RNN) for iterative channel tap search and nonlinear de-noising of their amplitudes in the time domain. Our method outperforms the sparse minimum mean square error (MMSE) CE that is based on time-domain windowing and a further linear denoising of channel taps within the window. Simulation results are presented for user speed of 5km/h in non-line-of-sight scenarios of the 5G QuaDRiGa 2.0 channel. The results are provided in both antenna and beamspace domains of the 64 antennas receiver.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.