Learning to Perform Downlink Channel Estimation in Massive MIMO Systems
- URL: http://arxiv.org/abs/2109.02463v1
- Date: Mon, 6 Sep 2021 13:42:32 GMT
- Title: Learning to Perform Downlink Channel Estimation in Massive MIMO Systems
- Authors: Amin Ghazanfari, Trinh Van Chien, Emil Bj\"ornson, Erik G. Larsson
- Abstract summary: We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system.
A common approach is to use the mean value as the estimate, motivated by channel hardening.
We propose two novel estimation methods.
- Score: 72.76968022465469
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study downlink (DL) channel estimation in a multi-cell Massive
multiple-input multiple-output (MIMO) system operating in a time-division
duplex. The users must know their effective channel gains to decode their
received DL data signals. A common approach is to use the mean value as the
estimate, motivated by channel hardening, but this is associated with a
substantial performance loss in non-isotropic scattering environments. We
propose two novel estimation methods. The first method is model-aided and
utilizes asymptotic arguments to identify a connection between the effective
channel gain and the average received power during a coherence block. The
second one is a deep-learning-based approach that uses a neural network to
identify a mapping between the available information and the effective channel
gain. We compare the proposed methods against other benchmarks in terms of
normalized mean-squared error and spectral efficiency (SE). The proposed
methods provide substantial improvements, with the learning-based solution
being the best of the considered estimators.
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