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.

翻译：5G技术的到来预示着电信领域的范式转变，提供了前所未有的速度和连接性。然而，5G网络中流量的高效管理仍然是一个关键挑战。这是由于网络流量的动态性和异质性、用户行为的差异性、网络规模的扩展以及多样化的应用，所有这些都要求高度准确且适应性强的预测模型来优化网络资源分配和管理。本文研究了实时场景下用于预测蜂窝网络流量的实时预测算法的有效性。我们在机器学习模型上应用了两种实时预测算法，其中一种是近期提出的快速实时流预测（FLSP）算法。我们在两种不同的数据采集方法下检验了这些算法的性能：同步采集，即所有网络小区同时报告统计数据；以及异步采集，即跨连续时隙进行报告。我们的研究深入探讨了这些采集场景对流量模型预测性能的影响。研究揭示，与传统在线预测算法相比，FLSP算法可将异步数据报告所需的带宽减半，同时提高预测精度并降低处理负载。此外，我们对各种机器学习模型的算法复杂度和内存需求进行了深入分析。通过实证评估，我们提供了关于不同预测策略中固有权衡的见解，为动态环境中的网络优化和资源分配提供了宝贵指导。