The evolution of cellular technology development has led to explosive growth in cellular network traffic. Accurate time-series models to predict cellular mobile traffic have become very important for increasing the quality of service (QoS) with a network. The modelling and forecasting of cellular network loading play an important role in achieving the greatest favourable resource allocation by convenient bandwidth provisioning and simultaneously preserve the highest network utilization. The novelty of the proposed research is to develop a model that can help intelligently predict load traffic in a cellular network. In this paper, a model that combines single-exponential smoothing with long short-term memory (SES-LSTM) is proposed to predict cellular traffic. A min-max normalization model was used to scale the network loading. The single-exponential smoothing method was applied to adjust the volumes of network traffic, due to network traffic being very complex and having different forms. The output from a single-exponential model was processed by using an LSTM model to predict the network load. The intelligent system was evaluated by using real cellular network traffic that had been collected in a kaggle dataset. The results of the experiment revealed that the proposed method had superior accuracy, achieving R-square metric values of 88.21%, 92.20%, and 89.81% for three one-month time intervals, respectively. It was observed that the prediction values were very close to the observations. A comparison of the prediction results between the existing LSTM model and our proposed system is presented. The proposed system achieved superior performance for predicting cellular network traffic.

中文翻译：

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基于智能模型的蜂窝交通预测

蜂窝技术发展的演进导致了蜂窝网络流量的爆炸性增长。用于预测蜂窝移动流量的准确时间序列模型对于提高网络的服务质量 (QoS) 变得非常重要。蜂窝网络负载的建模和预测在通过方便的带宽供应实现最大的有利资源分配方面发挥着重要作用，同时保持最高的网络利用率。拟议研究的新颖之处在于开发一种模型，可以帮助智能地预测蜂窝网络中的负载流量。在本文中，提出了一种将单指数平滑与长短期记忆（SES-LSTM）相结合的模型来预测蜂窝流量。使用最小-最大归一化模型来缩放网络负载。由于网络流量非常复杂且形式多样，因此采用单指数平滑方法来调整网络流量。单指数模型的输出通过使用 LSTM 模型来预测网络负载进行处理。该智能系统通过使用在 kaggle 数据集中收集的真实蜂窝网络流量进行评估。实验结果表明，该方法具有较高的准确率，达到了 该智能系统通过使用在 kaggle 数据集中收集的真实蜂窝网络流量进行评估。实验结果表明，该方法具有较高的准确率，达到了 该智能系统通过使用在 kaggle 数据集中收集的真实蜂窝网络流量进行评估。实验结果表明，该方法具有较高的准确率，达到了三个一个月时间间隔的R平方度量值分别为 88.21%、92.20% 和 89.81%。据观察，预测值非常接近观察值。介绍了现有 LSTM 模型和我们提出的系统之间的预测结果的比较。所提出的系统在预测蜂窝网络流量方面取得了卓越的性能。