使用图神经网络实现异构无线网络中的可扩展功率控制/波束成形
Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks
AI总结：
针对异构D2D网络功率控制/波束成形在规模扩展时的难题，提出无监督HIGNN框架，利用异构干扰图和有限邻域信息交换学习传输方案，在小规模训练后即可稳健扩展并显著提升执行效率。
摘要：
机器学习（ML）已被广泛用于无线网络中的高效资源分配（RA）。尽管在小型、简单网络上取得了卓越性能，但当异构性出现且网络规模扩大时，大多数现有基于ML的方法都面临困难。本文专门针对异构设备到设备（D2D）网络中的功率控制/波束成形（PC/BF），提出一种名为异构干扰图神经网络（HIGNN）的新型无监督学习框架，以应对这些挑战。首先，我们利用异构图表征多样化的链路特征和干扰关系。随后提出HIGNN，使每条链路在与相邻链路进行有限信息交换后，能够获得各自的传输方案。值得注意的是，HIGNN可扩展至规模不断增长的无线网络，并且在小型网络上训练后仍具有稳健性能。数值结果表明，与最先进的基准方法相比，HIGNN在提供强劲性能的同时，实现了高得多的执行效率。
英文摘要：
Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks. Although superb performance is achieved on small and simple networks, most existing ML-based approaches are confronted with difficulties when heterogeneity occurs and network size expands. In this paper, specifically focusing on power control/beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks, we propose a novel unsupervised learning-based framework named heterogeneous interference graph neural network (HIGNN) to handle these challenges. First, we characterize diversified link features and interference relations with heterogeneous graphs. Then, HIGNN is proposed to empower each link to obtain its individual transmission scheme after limited information exchange with neighboring links. It is noteworthy that HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks. Numerical results show that compared with state-of-the-art benchmarks, HIGNN achieves much higher execution efficiency while providing strong performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
