Deep Learning for MANET Routing

Abstract

Power control and scheduling are among the most well-known resource allocation challenges in wireless networks, and are often solved as optimization problems with constraints. However, solving these optimization challenges by using optimal algorithms often incurs a significant time complexity, which creates considerable discrepancies between the theoretical results and real-time processing required. In this study, we propose a novel machine learning-based perspective to address this issue. We propose a scheduling and power control deep neural network SPCDNet method and its modification SPCDNetR. SPCDNet solves the scheduling problem for point-to-point transmission requests while SPCDNetR solves the more complex problem, where the input transmission list is composed of ordered routes which should be satisfied. Both SPCDNet and SPCDNetR are trained in a supervised manner and show near-optimal performance on the test set. Our results demonstrate that SPCDNet and SPCDNetR can serve as a computationally inexpensive solution (regarding time complexity), compared with state-of-the-art schemes, while showing to be near-optimal approximation solutions to the time scheduling and power control challenges. Moreover, we found that both SPCDNet and SPCDNetR reach efficient solutions for large problem instances, even though they were trained on small problems.