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English(EN) Graph Neural Networks for Resource Allocation in Integrated Sensing and Communication

图神经网络优化集成传感与通信中的功率分配

研究人员开发了一种新颖的图神经网络(GNN)算法,称为GNN-ISAC,用于优化集成传感与通信(ISAC)网络中的功率分配。该方法解决了在满足传感信噪比约束的同时最大化通信总速率的复杂非凸问题。与WMMSE等传统方法相比,GNN-ISAC表现出具有竞争力的性能,在显著降低计算成本和减少传感链路违规的情况下,实现了相似的通信速率。该模型还显示出强大的泛化能力,无需重新训练即可在更大的网络上表现良好。 AI

影响 这项研究可能为未来通信和传感网络中更高效的资源管理带来新的可能。

排序理由 该集群包含一篇详细介绍针对特定技术问题的算法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

图神经网络优化集成传感与通信中的功率分配

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Junyu Liao, Lili Chen, Tian Han, Jamie Evans ·

    面向集成感知与通信的资源分配的图神经网络

    arXiv:2611.07277v1 Announce Type: new Abstract: In vehicular integrated sensing and communication (ISAC) networks, each service-provider vehicle uses a limited power budget to serve multiple sensing targets and communication receivers. Maximizing the communication sum-rate subjec…