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 $SPCDNet^{R}$ . SPCDNet solves the scheduling problem for point-to-point transmission requests while $SPCDNet^{R}$ solves the more complex problem, where the input transmission list is composed of ordered routes which should be satisfied. Both SPCDNet and $SPCDNet^{R}$ are trained in a supervised manner and show near-optimal performance on the test set. Our results demonstrate that SPCDNet and $SPCDNet^{R}$ 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 $SPCDNet^{R}$ reach efficient solutions for large problem instances, even though they were trained on small problems.