Graph-theoretic approach to mobility-aware frequency assignment via deep Q-learning

Due to the increasing demand for frequency resources in wireless networks, efficient frequency assignment has become a critical challenge. Unlike conventional cellular systems, where frequency allocation is centrally managed by a base station, device-to-device (D2D) communication, especially in mission-critical scenarios, introduces additional complexity due to its decentralized nature. In this study, we model a D2D communication network as a graph and formulate the frequency assignment task as a graph coloring problem. While previous research has primarily relied on heuristic or artificial intelligence (AI)-based methods to determine node ordering, we propose a novel framework that integrates deep Q-learning (DQN) with graph neural networks (GNNs) to enhance assignment efficiency. To ensure interference-free operation, we explicitly incorporate net filter discrimination (NFD), which captures realistic interference constraints. Unlike previous AI-based approaches that focus solely on minimizing the number of assigned frequency blocks, our method jointly optimizes both the total frequency span and the ordering cost. Extensive simulations show that the proposed approach significantly outperforms greedy baselines, particularly in complex and dynamic environments. Furthermore, by incorporating device mobility into the simulations, we validate the robustness and adaptability of the proposed framework. These results underscore the potential of DQN-based methods to enable scalable and reliable frequency assignment in mission-critical wireless networks.