  In this paper, we investigate a neural network-based learning approach
towards solving an integer-constrained programming problem using very limited
training. To be specific, we introduce a symmetric and decomposed neural
network structure, which is fully interpretable in terms of the functionality
of its constituent components. By taking advantage of the underlying pattern of
the integer constraint, as well as of the affine nature of the objective
function, the introduced neural network offers superior generalization
performance with limited training, as compared to other generic neural network
structures that do not exploit the inherent structure of the integer
constraint. In addition, we show that the introduced decomposed approach can be
further extended to semi-decomposed frameworks. The introduced learning
approach is evaluated via the classification/symbol detection task in the
context of wireless communication systems where available training sets are
usually limited. Evaluation results demonstrate that the introduced learning
strategy is able to effectively perform the classification/symbol detection
task in a wide variety of wireless channel environments specified by the 3GPP
community.
