  Reconfigurable antennas that can dynamically change their operation state
exhibit excellent adaptivity and flexibility over traditional antennas, and
MIMO arrays that consist of multifunctional and reconfigurable antennas (MRAs)
are foreseen as one promising solution towards future Holographic MIMO.
Specifically, in pattern reconfigurable MIMO (PR-MIMO) communication systems,
accurate acquisition of channel state information (CSI) of all the radiation
modes is a challenging task, because using conventional pilot-based channel
estimation techniques in PR-MIMO systems incurs overwhelming pilot overheads.
In this letter, we leverage deep learning methods to design a PR neural
network, which can use the estimated CSI for one radiation mode to infer CSIs
for the other radiation modes. In order to reduce the pilot overheads, we
propose a new channel estimation method specially for PR-MIMO systems, which
divides the transmit antennas of PR-MIMO into groups and antennas in different
groups employ different radiation modes. Compared with conventional
full-connected real-valued deep neural networks (DNN), the PR neural network
which uses complex-valued coefficients can work directly in the complex domain.
Experiment results show that the proposed channel extrapolation method offers
significant performance gains in terms of extrapolation accuracy over benchmark
schemes.
