In this paper, we propose a supervised‐learning‐based spatial performance prediction (SLPP) framework for next‐generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine‐learning ubiquitous for accurate data‐based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis‐based prediction approach has been proposed in this paper. Comparison results with different machine‐learning techniques on real‐world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

中文翻译：

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一种基于监督学习的异构通信网络空间性能预测框架

在本文中，我们为下一代异构通信网络（HCN）提出了一种基于监督学习的空间性能预测（SLPP）框架。自适应资产放置，动态资源分配和负载平衡是HCN中的关键网络功能，以确保无缝网络管理并提高服务质量。尽管许多现有系统使用测量数据来对网络性能变化做出反应，但是对不同的系统执行准确的性能预测以支持各种网络功能是非常有益的。复杂的统计算法和计算效率方面的最新进展已使机器学习无处不在，从而可以进行精确的基于数据的预测。本文提出了一个鲁棒的网络性能预测框架，该框架可通过基于线性判别分析的预测方法来优化性能和资源利用。使用不同的机器学习技术对实际数据进行比较的结果表明，SLPP为固定和移动用户条件提供了卓越的准确性和计算效率。