  Deep learning (DL) based resource allocation (RA) has recently gained a lot
of attention due to its performance efficiency. However, most of the related
studies assume an ideal case where the number of users and their utility
demands, e.g., data rate constraints, are fixed and the designed DL based RA
scheme exploits a policy trained only for these fixed parameters. A
computationally complex policy retraining is required whenever these parameters
change. Therefore, in this paper, a DL based resource allocator (ALCOR) is
introduced, which allows users to freely adjust their utility demands based on,
e.g., their application layer. ALCOR employs deep neural networks (DNNs), as
the policy, in an iterative optimization algorithm. The optimization algorithm
aims to optimize the on-off status of users in a time-sharing problem to
satisfy their utility demands in expectation. The policy performs unconstrained
RA (URA) -- RA without taking into account user utility demands -- among active
users to maximize the sum utility (SU) at each time instant. Based on the
chosen URA scheme, ALCOR can perform RA in a model-based or model-free manner
and in a centralized or distributed scenario. Derived convergence analyses
provide guarantees for the convergence of ALCOR, and numerical experiments
corroborate its effectiveness.
