Abstract
So far, various data-driven approaches have been presented to obtain
channel state information (CSI) in mmWave multiple-input-multiple-output
(MIMO) wireless networks. In almost all previous works, training and
testing channels were assumed to have the same distribution, which may
not be the case in practice. In this paper, we address this challenge,
by proposing a learning framework that is a combination of a long
short-term memory (LSTM) network and a deep neural network (DNN) for
estimating CSI in a dynamic wireless communication environment.
Furthermore, we use federated learning (FL) to train the learning-based
channel estimation (CE) model. More specifically, we introduce a
two-stage downlink pilot transmission procedure, where in the initial
stage, long frame length downlink pilot signals are used to train the
introduced RNN-DNN model. Following that, users will receive
shorter-frame-length pilot signals that can be used for CSI estimation.
To speed up the training procedure of the proposed network, we first
generate a pre-trained model and then modify it according to the
collected data samples. Simulation results demonstrate that, when the
channel distribution is unavailable, the proposed approach performs
significantly better than the most recent channel estimation algorithms
in terms of estimation performance and computational complexity.