  This paper studies a deep learning approach for binary assignment problems in
wireless networks, which identifies binary variables for permutation matrices.
This poses challenges in designing a structure of a neural network and its
training strategies for generating feasible assignment solutions. To this end,
this paper develop a new Sinkhorn neural network which learns a non-convex
projection task onto a set of permutation matrices. An unsupervised training
algorithm is proposed where the Sinkhorn neural network can be applied to
network assignment problems. Numerical results demonstrate the effectiveness of
the proposed method in various network scenarios.
