  Increased complexity and heterogeneity of emerging 5G and beyond 5G (B5G)
wireless networks will require a paradigm shift from traditional resource
allocation mechanisms. Deep learning (DL) is a powerful tool where a
multi-layer neural network can be trained to model a resource management
algorithm using network data.Therefore, resource allocation decisions can be
obtained without intensive online computations which would be required
otherwise for the solution of resource allocation problems. In this context,
this article focuses on the application of DL to obtain solutions for the radio
resource allocation problems in multi-cell networks. Starting with a brief
overview of a deep neural network (DNN) as a DL model, relevant DNN
architectures and the data training procedure, we provide an overview of
existing state-of-the-art applying DL in the context of radio resource
allocation. A qualitative comparison is provided in terms of their objectives,
inputs/outputs, learning and data training methods. Then, we present a
supervised DL model to solve the sub-band and power allocation problem in a
multi-cell network. Using the data generated by a genetic algorithm, we first
train the model and then test the accuracy of the proposed model in predicting
the resource allocation solutions. Simulation results show that the trained DL
model is able to provide the desired optimal solution 86.3% of time.
