Abstract
The emergence of highly directional beamforming technology makes millimeter wave frequency band communication possible in future wireless communication networks. Based on the multipath characteristics of millimeter wave frequency communication, a high-precision multipath channel estimation algorithm based on signal subspace is proposed. In the mobile terminal, an iterative heuristic radiofrequency combination algorithm based on spatial points is proposed. The analog precoding at the base station uses deep learning to accelerate the calculation, and then the multi-user communication is modeled to design the digital precoding. The simulation results show that the multi-channel estimation algorithm can estimate 4 paths with an error of no more than 0.3 rad. The proposed DL algorithm takes only 20% of the time when it is close to the 87% spectral efficiency of the traditional algorithm.
Recommended Citation
R. Hu et al., "Hybrid Beamforming With Deep Learning For Large-Scale Antenna Arrays," IEEE Access, vol. 9, pp. 54690 - 54699, article no. 9387291, Institute of Electrical and Electronics Engineers, Jan 2021.
The definitive version is available at https://doi.org/10.1109/ACCESS.2021.3069037
Department(s)
Electrical and Computer Engineering
Publication Status
Open Access
Keywords and Phrases
channel estimation; deep learning (DL); hybrid beamforming; large-scale antenna arrays; Millimeter wave; multiple-input multiple-output (MIMO); space point iteration
International Standard Serial Number (ISSN)
2169-3536
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Publication Date
01 Jan 2021
Comments
National Natural Science Foundation of China, Grant 62071290