  Resource allocation and transceivers in wireless networks are usually
designed by solving optimization problems subject to specific constraints,
which can be formulated as variable or functional optimization. If the
objective and constraint functions of a variable optimization problem can be
derived, standard numerical algorithms can be applied for finding the optimal
solution, which however incur high computational cost when the dimension of the
variable is high. To reduce the on-line computational complexity, learning the
optimal solution as a function of the environment's status by deep neural
networks (DNNs) is an effective approach. DNNs can be trained under the
supervision of optimal solutions, which however, is not applicable to the
scenarios without models or for functional optimization where the optimal
solutions are hard to obtain. If the objective and constraint functions are
unavailable, reinforcement learning can be applied to find the solution of a
functional optimization problem, which is however not tailored to optimization
problems in wireless networks. In this article, we introduce unsupervised and
reinforced-unsupervised learning frameworks for solving both variable and
functional optimization problems without the supervision of the optimal
solutions. When the mathematical model of the environment is completely known
and the distribution of environment's status is known or unknown, we can invoke
unsupervised learning algorithm. When the mathematical model of the environment
is incomplete, we introduce reinforced-unsupervised learning algorithms that
learn the model by interacting with the environment. Our simulation results
confirm the applicability of these learning frameworks by taking a user
association problem as an example.
