  This letter proposes a deep learning based pilot design scheme to minimize
the sum mean square error (MSE) of channel estimation for multi-user
distributed massive multiple-input multiple-output (MIMO) systems. The pilot
signal of each user is expressed as a weighted superposition of orthonormal
pilot sequence basis, where the power assigned to each pilot sequence is the
corresponding weight. A multi-layer fully connected deep neural network (DNN)
is designed to optimize the power allocated to each pilot sequence to minimize
the sum MSE, which takes the channel large-scale fading coefficients as input
and outputs the pilot power allocation vector. The loss function of the DNN is
defined as the sum MSE, and we leverage the unsupervised learning strategy to
train the DNN. Simulation results show that the proposed scheme achieves better
sum MSE performance than other methods with low complexity.
