The advent of 5G technology promises a paradigm shift in the realm of
telecommunications, offering unprecedented speeds and connectivity. However,
the efficient management of traffic in 5G networks remains a critical
challenge. It is due to the dynamic and heterogeneous nature of network
traffic, varying user behaviors, extended network size, and diverse
applications, all of which demand highly accurate and adaptable prediction
models to optimize network resource allocation and management. This paper
investigates the efficacy of live prediction algorithms for forecasting
cellular network traffic in real-time scenarios. We apply two live prediction
algorithms on machine learning models, one of which is recently proposed Fast
LiveStream Prediction (FLSP) algorithm. We examine the performance of these
algorithms under two distinct data gathering methodologies: synchronous, where
all network cells report statistics simultaneously, and asynchronous, where
reporting occurs across consecutive time slots. Our study delves into the
impact of these gathering scenarios on the predictive performance of traffic
models. Our study reveals that the FLSP algorithm can halve the required
bandwidth for asynchronous data reporting compared to conventional online
prediction algorithms, while simultaneously enhancing prediction accuracy and
reducing processing load. Additionally, we conduct a thorough analysis of
algorithmic complexity and memory requirements across various machine learning
models. Through empirical evaluation, we provide insights into the trade-offs
inherent in different prediction strategies, offering valuable guidance for
network optimization and resource allocation in dynamic environments.

我们研究了实时场景下应用于移动网络流量预测的两种实时预测算法的有效性，发现 Fast LiveStream Prediction（FLSP）算法相对于传统的在线预测算法，在异步数据汇报情况下，能够减少所需带宽一半，并提高预测准确性和降低处理负载。我们还通过对各种机器学习模型的算法复杂度和内存需求进行了细致分析，从而提供有关不同预测策略的权衡，为动态环境下的网络优化和资源分配提供了有价值的指导。

使用在线预测算法进行蜂窝网络流量预测

Cellular Traffic Prediction Using Online Prediction Algorithms

Emerging with the support of computing and communications technologies,
Metaverse is expected to bring users unprecedented service experiences.
However, the increase in the number of Metaverse users places a heavy demand on
network resources, especially for Metaverse services that are based on
graphical extended reality and require rendering a plethora of virtual objects.
To make efficient use of network resources and improve the
Quality-of-Experience (QoE), we design an attention-aware network resource
allocation scheme to achieve customized Metaverse services. The aim is to
allocate more network resources to virtual objects in which users are more
interested. We first discuss several key techniques related to Metaverse
services, including QoE analysis, eye-tracking, and remote rendering. We then
review existing datasets and propose the user-object-attention level (UOAL)
dataset that contains the ground truth attention of 30 users to 96 objects in
1,000 images. A tutorial on how to use UOAL is presented. With the help of
UOAL, we propose an attention-aware network resource allocation algorithm that
has two steps, i.e., attention prediction and QoE maximization. Specially, we
provide an overview of the designs of two types of attention prediction
methods, i.e., interest-aware and time-aware prediction. By using the predicted
user-object-attention values, network resources such as the rendering capacity
of edge devices can be allocated optimally to maximize the QoE. Finally, we
propose promising research directions related to Metaverse services.

设计了一种基于用户兴趣的网络资源分配方案，以提高用户体验质量，并提出了关于 Metaverse 服务的有前途的研究方向。