  In large scale dynamic wireless networks, the amount of overhead caused by
channel estimation (CE) is becoming one of the main performance bottlenecks.
This is due to the large number users whose channels should be estimated, the
user mobility, and the rapid channel change caused by the usage of the
high-frequency spectrum (e.g. millimeter wave). In this work, we propose a new
hybrid channel estimation/prediction (CEP) scheme to reduce overhead in
time-division duplex (TDD) wireless cell-free massive
multiple-input-multiple-output (mMIMO) systems. The scheme proposes sending a
pilot signal from each user only once in a given number (window) of coherence
intervals (CIs). Then minimum mean-square error (MMSE) estimation is used to
estimate the channel of this CI, while a deep neural network (DNN) is used to
predict the channels of the remaining CIs in the window. The DNN exploits the
temporal correlation between the consecutive CIs and the received pilot signals
to improve the channel prediction accuracy. By doing so, CE overhead is reduced
by at least 50 percent at the expense of negligible CE error for practical user
mobility settings. Consequently, the proposed CEP scheme improves the spectral
efficiency compared to the conventional MMSE CE approach, especially when the
number of users is large, which is demonstrated numerically.
