AI-driven graph coloring for frequency assignment in wireless networks

Chae, Gyoo Soo

doi:10.47974/JDMSC-2551

Open Access Research Article

AI-driven graph coloring for frequency assignment in wireless networks

^*Gyoo Soo ChaeCorresponding authorgschae@bu.ac.krDivision of Advanced Information TechnologyBaekseok UniversityCheonan, 31069, Republic of KoreaView full profile →

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pp. 987–994Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2551 Crossmark XML

Received:: 08 Apr 2025
Published Online:: 18 Feb 2026
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2551
Pages:: 987–994

Abstract

To keep disturbance to a minimum and make the best use of the airwaves, wireless networks need to give frequencies efficiently. To solve the frequency assignment problem well, this study suggests an AI-driven graph colouring method that uses Deep Reinforcement Learning (Deep RL), Reinforcement Learning (RL), and Genetic Algorithms (GA). Adding subject knowledge and network limitations to AI models makes sure that the solutions are accurate and useful. By improving learning techniques and heuristic searches, our method solves the problems of processing complexity and scaling that are common in large-scale wireless networks. The suggested method does better than traditional graph colouring algorithms in both quality of solutions and speed of convergence.

Keywords

Frequency assignment Graph coloring Reinforcement learning Deep reinforcement learning Genetic algorithms Wireless networks

Subject Classifications

31A1033E30

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