  Deep neural networks (DNNs) have been employed for designing wireless
networks in many aspects, such as transceiver optimization, resource
allocation, and information prediction. Existing works either use
fully-connected DNN or the DNNs with specific structures that are designed in
other domains. In this paper, we show that a priori information widely existed
in wireless tasks is permutation invariant. For these tasks, we propose a DNN
with special structure, where the weight matrices between layers of the DNN
only consist of two smaller sub-matrices. By such way of parameter sharing, the
number of model parameters reduces, giving rise to low sample and computational
complexity for training a DNN. We take predictive resource allocation as an
example to show how the designed DNN can be applied for learning the optimal
policy with unsupervised learning. Simulations results validate our analysis
and show dramatic gain of the proposed structure in terms of reducing training
complexity.
