Traffic pattern prediction has emerged as a promising approach for efficiently managing and mitigating the impacts of event-driven bursty traffic in massive machine-type communication (mMTC) networks. However, achieving accurate predictions of bursty traffic remains a non-trivial task due to the inherent randomness of events, and these challenges intensify within live network environments. Consequently, there is a compelling imperative to design a lightweight and agile framework capable of assimilating continuously collected data from the network and accurately forecasting bursty traffic in mMTC networks. This paper addresses these challenges by presenting a machine learning-based framework tailored for forecasting bursty traffic in multi-channel slotted ALOHA networks. The proposed machine learning network comprises long-term short-term memory (LSTM) and a DenseNet with feed-forward neural network (FFNN) layers, where the residual connections enhance the training ability of the machine learning network in capturing complicated patterns. Furthermore, we develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network. Simulation results and complexity analysis demonstrate the superiority of our proposed algorithm in terms of both accuracy and complexity, making it well-suited for time-critical live scenarios. We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics. Comprehensive evaluations and simulations indicate that our proposed machine learning approach achieves a remarkable $52\%$ higher accuracy in long-term predictions compared to traditional methods, without imposing additional processing load on the system.

翻译：流量模式预测已成为有效管理和缓解大规模机器类通信（mMTC）网络中事件驱动突发性流量影响的一种有前途的方法。然而，由于事件的固有随机性，实现突发流量的准确预测仍然是一项艰巨任务，这些问题在实时网络环境中更为突出。因此，迫切需要设计一个轻量级且敏捷的框架，能够同化从网络连续收集的数据，并准确预测mMTC网络中的突发流量。本文通过提出一种基于机器学习的框架来应对这些挑战，该框架专门用于预测多信道时隙ALOHA网络中的突发流量。所提出的机器学习网络包含长短期记忆网络（LSTM）和带有前馈神经网络（FFNN）层的DenseNet，其中残差连接增强了机器学习网络在捕捉复杂模式方面的训练能力。此外，我们开发了一种新的低复杂度在线预测算法，通过利用从mMTC网络频繁收集的数据来更新LSTM网络的状态。仿真结果和复杂度分析表明，我们提出的算法在准确性和复杂度方面均具有优越性，使其非常适合时间关键的实时场景。我们在一个包含单个基站和数千个具有不同流量生成特征的设备分组网络中评估了所提出框架的性能。全面的评估和仿真表明，与传统方法相比，我们提出的机器学习方法在长期预测中实现了显著提高的$52\%$准确率，而无需给系统增加额外的处理负载。