Machine Learning-assisted Node Scheduling in Multi-user Network-coded Relay Networks

This paper investigates efficient data delivery methods, through integrating non-orthogonal transmission with network coding techniques, in wireless networks with multiple sources, multiple relays, and multiple destinations. We propose properly scheduling terminals into transmitting clusters to optimize system error performance. Considering communication over general Nakagami-m fading channels, an error probability approximation analysis is first presented. To reduce computation complexity for solving the node scheduling design problem, we further propose transforming the problem into a data classification problem and applying data-driven machine learning tools to reach rapid decision-making. Taking two-user clustering as an example, simulation results show that our method can attain similar performance as the optimal solution, and significantly outperform conventional methods that orthogonalize or randomly schedule terminals. The proposed method and the analytical framework can be extended to larger networks with diverse node clusters and more complex signal propagation environments.