A supervised‐learning‐based spatial performance prediction framework for heterogeneous communication networks

Abstract

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.

Application analysis of machine learning in cyberspace security research

“…If a security problem occurs in a certain link, it may have a greater impact on the operation of the system. Such as chip quality, Trojan hardware, etc [5]. Relevant studies have shown that the use of machine learning technology can help chips solve security problems, and can quickly identify inferior chips and hardware Trojans by performing variable signal analysis, image recognition, etc.…”

K-Means Clustering-Aided Power Control for UAV-Enabled OFDM Networks

“…A RTIFICIAL intelligence (AI) and machine learning (ML) are promising technologies for future wireless networks [1]- [4]. Recently, the IEEE 802.11 working group approved the formation of a topic interest group (TIG) for the AI/ML use in the IEEE 802.11 [5].…”