  To compensate the loss from outdated channel state information in wideband
massive multiple-input multipleoutput (MIMO) systems, channel prediction can be
performed by leveraging the temporal correlation of wireless channels. Machine
learning (ML)-based channel predictors for massive MIMO systems were designed
recently; however, the time overhead to collect a large amount of training data
directly affects the latency of the system. In this paper, we propose a novel
ML-based channel prediction technique, which can reduce the time overhead to
collect the training data by transforming the domain of channels from
subcarrier to antenna in wideband massive MIMO systems. Numerical results show
that the proposed technique can not only reduce the time overhead but also give
additional performance gain compared to the ML-based channel prediction
techniques without the domain transformation.
