Deep Learning Frameworks for Solving Infeasible Optimization Problems in Vehicular Communications

Resource allocation in vehicular communication often encounters stringent constraints that are hard to satisfy due to high mobility and a complicated communication environment, making the optimization problem infeasible. However, even in such infeasible scenarios, the resource allocation strategy should be able to provide viable solutions within a short computation time. To address this challenge, we explore the potential of a deep learning (DL) framework that can provide reasonable resource allocation solutions even when certain constraints cannot be satisfied. In particular, we focus on resource allocation to maximize overall energy efficiency while ensuring minimum spectral efficiency, where resource allocation constraints may not always be satisfied, unlike traditional works that consider only the feasible scenarios. We propose a DL framework that uses deep neural network (DNN) models to approximate resource allocation. In addition, an unsupervised learning-based training methodology is developed such that the DNN model approximates the optimal resource allocation for feasible cases while for infeasible cases, the trade-off between the objective and the constraint can be achieved, all with low computation time. Our simulation results confirm that near-optimal performance can be achieved for feasible cases, while achieving performance that balances objective and constraint satisfaction in the case of infeasible scenarios, all with low computational overhead.