Learn to Coloring: Fast Response to Perturbation in UAV-Assisted Disaster Relief Networks

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 3; pp. 3505 - 3509
• Main Authors: Wang, Bowen, Sun, Yanjing, Zhao, Nan, Gui, Guan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Apexes; Cochannel interference; Color; Computer simulation; Convergence; Disaster relief; disaster relief networks; Graph coloring; Graph theory; Heuristic algorithms; Interference; Machine learning; Multicast; Network topology; Perturbation; resource allocation; Topology; Unmanned aerial vehicle; Unmanned aerial vehicles; Vehicle dynamics
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2020.2967124

In this paper, we address the unmanned aerial vehicle (UAV)-assisted disaster relief networks, where UAVs can disseminate the emergency information to those terrestrial users in a multicast manner. Due to the limitation of spectrum resources, multiple UAVs have to reuse the same channel while the co-channel interference management is needed. The dynamic topology structure induced by mobility can be modeled as a dynamic graph in which the existence of an edge (interference relationship) between two vertices (multicast clusters) is dynamically changing. As such, the channel selection problem can be transformed into a dynamic graph coloring problem in which the graph structure evolves in continuous time slots and the previous coloring strategy becomes valid. In this regard, we propose a stochatic learning based algorithm which can converge rapidly. Simulation results demonstrate that our proposed method is fast in response to the perturbations in dynamic environments.