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Data-Driven Inter-Cell Interference Coordination for Mobile Cellular Networks
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DOI：10.1109/OJCOMS.2025.3645857.png)
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Abstract
En 中文
Compared with traditional fixed frequency reuse patterns or optimization relying on channel information, artificial intelligence (AI)-based approaches using mobile big data provide another effective and efficient way for interference management. This paper proposes a data-driven interference coordination framework. First, Graphormer is adopted to model the inter-cell interference, where the node features capture the cell’s parameters, like power and allocated spectrum, and the edge features capture the interference relationships between neighboring cells. Second, a performance evaluation module is designed to establish a comprehensive understanding between network performance and wireless resource allocation, traffic requirement, interference, and so on. Then, proximal policy optimization (PPO) is utilized to dynamically optimize the spectrum allocation to enhance network performance while meeting dynamic traffic demands. Experimental results demonstrate that: i) the Graphormer-based interference modeling outperforms other algorithms in estimation accuracy; ii) the proposed approach effectively reduces inter-cell interference and improves network performance compared to other benchmark algorithms.
Keywords:
Interference coordination
mobile big data
spectrum allocation
graphormer
reinforcement learning
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