干扰驱动的聚类优化用于FM频谱协调
Interference-Driven Clustered Optimisation for FM Spectrum Coordination
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中文总结 AI 辅助
针对大规模跨境FM频谱协调，提出干扰驱动的聚类优化框架，分解功率控制问题，降低复杂度并保持保护与覆盖。
中文摘要 AI 辅助
跨境FM频谱协调涉及在保护外国广播服务的同时保持国内覆盖，而无线电规划数据集日益庞大，包含数千个发射机和数百万个发射机-像素关系。在此类场景中，由于相关功率控制问题的高维性，传统优化方法计算需求巨大。本文提出了一种干扰驱动的聚类优化框架，用于大规模FM频谱协调。所提方法利用了以下观察：外国服务保护的违规通常由有限子集的发射机主导。因此，分析受保护服务以识别主导干扰源并量化其对干扰的影响。这些关系通过干扰图表示，从中提取面向优化的发射机聚类。这些聚类将全局功率控制问题分解为较小的优化任务，使用聚类模拟退火求解，随后进行全局细化以捕获剩余的聚类间交互。覆盖和干扰使用频率相关的保护标准和动态最强服务分配模型进行评估。为实现运营规模规划，该框架使用稀疏矩阵和GPU加速计算。在现实的跨境FM协调场景上的测试表明，聚类策略大幅降低了优化复杂度和运行时间，同时保持了外国服务保护和国内覆盖。该方法还生成了对有害干扰贡献最大的发射机的可解释排名，支持优化和频谱规划。
英文摘要
Cross-border FM spectrum coordination involves protecting foreign broadcasting services while preserving domestic coverage, amid increasingly large radio-planning datasets containing thousands of transmitters and millions of transmitter-pixel relationships. In such scenarios, conventional optimisation approaches become computationally demanding due to the high dimensionality of the associated power-control problem. This paper proposes an interference-driven clustered optimisation framework for large-scale FM spectrum coordination. The proposed method exploits the observation that violations of foreign-service protection are typically dominated by a limited subset of transmitters. Protected services are therefore analysed to identify dominant interferers and quantify their impact on interference. These relationships are represented through an interference graph from which optimisation-oriented transmitter clusters are extracted. The clusters decompose the global power-control problem into smaller optimisation tasks solved with clustered simulated annealing, followed by a global refinement that captures residual inter-cluster interactions. Coverage and interference are evaluated using frequency-dependent protection criteria and a dynamic strongest-service assignment model. To enable operational-scale planning, the framework uses sparse matrices and GPU-accelerated computations. Tests on realistic cross-border FM coordination scenarios show that the clustering strategy greatly reduces optimisation complexity and runtime while maintaining foreign-service protection and domestic coverage. The method also yields an interpretable ranking of transmitters that contribute most to harmful interference, supporting optimisation and spectrum planning.
发表机构
- Fondazione Ugo Bordoni（乌戈·博尔多尼基金会）
机构由 AI 辅助整理，请以论文原文为准。
