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Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems. (arXiv:2104.11052v3 [cs.IT] CROSS LISTED)
Jan. 20, 2022, 2:11 a.m. | Xisuo Ma, Zhen Gao, Feifei Gao, Marco Di Renzo
cs.LG updates on arXiv.org arxiv.org
This paper proposes a model-driven deep learning (MDDL)-based channel
estimation and feedback scheme for wideband millimeter-wave (mmWave) massive
hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay
domain channels' sparsity is exploited for reducing the overhead. Firstly, we
consider the uplink channel estimation for time-division duplexing systems. To
reduce the uplink pilot overhead for estimating the high-dimensional channels
from a limited number of radio frequency (RF) chains at the base station (BS),
we propose to jointly train the phase shift network …
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