Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Based Pilot Design for Multi-user Distributed Massive MIMO
  Systems   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.

1
The categories of this paper are: cs
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th