发表机构
The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data; Columbia University; Sun Yat-sen University; Tongji University; University of Chinese Academy of Sciences; Huawei Technologies（香港中文大学（深圳）; 深圳大数据研究院; 哥伦比亚大学; 中山大学; 同济大学; 中国科学院大学; 华为技术有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对大规模5G网络PCI分配难题，提出带神经块求解器的同余分解框架，将问题解耦为子问题并结合图神经网络优化，实验显示其性能优于现有基线方法。
AI 中文摘要
物理小区标识（PCI）分配是密集5G网络中干扰管理的关键环节。随着蜂窝网络规模扩大，PCI重用不可避免，可能引发碰撞、混淆及多种形式的模块化干扰。联合缓解这些问题形成了大规模多目标组合优化问题，在实际网络规模下难以高效求解。本研究提出一种带神经块求解器的同余分解框架用于大规模PCI分配，该分解利用PCI值的算术结构，将多个模块化干扰目标解耦为一组分块Min-k-划分子问题，随后通过图着色过程解决PCI冲突。针对所得的NP难Min-k-划分子问题，我们开发了神经块求解器，用图神经网络参数化其松弛二次公式，实现大规模高效优化；通过带理论保证的条件期望舍入恢复离散分配。在合成蜂窝图和真实5G网络上的实验表明，所提方法在模块化干扰降低、冲突消除及计算效率上，始终优于现有感知模块化干扰的基线方法。
英文摘要
Physical Cell Identity (PCI) assignment is essential for interference management in dense 5G networks. As cellular networks scale, PCI reuse becomes unavoidable, which may cause collisions, confusions, and multiple forms of modular interference. Jointly mitigating these effects gives rise to a large-scale, multi-objective combinatorial optimization problem that is difficult to solve efficiently at practical network scales. In this work, we propose a congruence decomposition framework with neural block solvers for large-scale PCI assignment. The proposed decomposition exploits the arithmetic structure of PCI values to decouple multiple modular interference objectives into a collection of blockwise Min-$k$-Partition subproblems, followed by a graph coloring procedure to resolve PCI conflicts. For the resulting NP-hard Min-$k$-Partition subproblems, we develop neural block solvers by parameterizing their relaxed quadratic formulations with graph neural networks, enabling efficient optimization at large scales. Discrete assignments are recovered through conditional expectation rounding with theoretical guarantees. Experiments on synthetic cellular graphs and real-world 5G networks show that the proposed method consistently outperforms existing modular-interference-aware baselines in modular interference reduction, conflict elimination, and computational efficiency.
