  There exists many resource allocation problems in the field of wireless
communications which can be formulated as the generalized assignment problems
(GAP). GAP is a generic form of linear sum assignment problem (LSAP) and is
more challenging to solve owing to the presence of both equality and inequality
constraints. We propose a novel deep unsupervised learning (DUL) approach to
solve GAP in a time-efficient manner. More specifically, we propose a new
approach that facilitates to train a deep neural network (DNN) using a
customized loss function. This customized loss function constitutes the
objective function and penalty terms corresponding to both equality and
inequality constraints. Furthermore, we propose to employ a Softmax activation
function at the output of DNN along with tensor splitting which simplifies the
customized loss function and guarantees to meet the equality constraint. As a
case-study, we consider a typical user-association problem in a wireless
network, formulate it as GAP, and consequently solve it using our proposed DUL
approach. Numerical results demonstrate that the proposed DUL approach provides
near-optimal results with significantly lower time-complexity.
