Spectrum Allocation Algorithm in Cognitive Radio Networks: Graph Theory-Based Color-Sensitive Algorithm

Spectrum allocation in cognitive radio networks is a critical issue aimed at efficiently utilizing limited spectrum resources. The graph theory-based color-sensitive algorithm serves as an effective solution by modeling the spectrum allocation problem as a graph coloring problem. The core concept of this algorithm involves representing wireless network nodes as graph vertices and modeling interference relationships between nodes as edges. By assigning colors to vertices (i.e., allocating spectrum), the algorithm ensures adjacent vertices receive different colors, thereby minimizing interference. The color-sensitive algorithm further optimizes this process by incorporating considerations for spectrum continuity and bandwidth requirements, enabling more flexible and efficient allocation. Compared to traditional methods, the color-sensitive algorithm demonstrates superior adaptability to dynamic wireless environments, improves spectrum utilization efficiency, and reduces computational complexity. This algorithm exhibits excellent performance in cognitive radio networks, particularly suitable for high-density, highly dynamic network scenarios. From an implementation perspective, the algorithm typically involves constructing an interference graph where vertices represent cognitive radio users and edges indicate potential interference. Key functions include graph initialization, vertex ordering based on priority metrics, and iterative color assignment with conflict detection. The color-sensitive approach may incorporate additional constraints through modified greedy algorithms or heuristic methods that consider both discrete and continuous spectrum blocks, often implemented using adjacency matrices and priority queues for efficient spectrum slot management.