LOAD FORECASTING IN HETEROGENEOUS TELECOMMUNICATION NETWORKS BASED ON THE DEVELOPED NEURAL MODEL
DOI:
https://doi.org/10.28925/2663-4023.2024.26.788Keywords:
гетерогенна телекомунікаційна мережа, нейронна мережа, прогнозування навантаження, багатошаровий перцептрон, інформаційна технологія.Abstract
The paper addresses the relevant scientific challenge of traffic load forecasting in heterogeneous telecommunication networks using artificial neural networks. With the rapid deployment of 5G technologies, IoT devices, smart city infrastructures, and mobile cloud computing, there is a growing need for intelligent models capable of analyzing vast volumes of telecommunications data and accurately forecasting network load. One of the key challenges in heterogeneous networks is the uneven distribution of traffic across different communication technologies (e.g., LTE, Wi-Fi, NB-IoT), which requires adaptive resource management strategies. The proposed model is based on a multilayer perceptron (MLP) neural network trained on time series data representing network component loads. Input features include previous traffic load values, time-based characteristics, network type, and QoS levels. The model is trained using backpropagation and the mean squared error (MSE) loss function. The output of the model is a predicted load value for a given future time interval. The study provides a detailed algorithmic description of the model's operation, including its mathematical formulation, objective function, and system constraints. The model accounts for the structural characteristics of heterogeneous networks, offers adaptability to changing environments, and can be scaled to suit various telecommunication platforms. A literature review highlights relevant research on network load prediction, including works by G. Zhang, M. Chen, Y. Sun, as well as Ukrainian researchers such as D.V. Kozlov, A.O. Hrabovets, and Y.P. Solovey. The results of this research can be applied to optimize network traffic distribution, enable dynamic load balancing, support adaptive resource planning, and prevent overloads. In the future, the model can be integrated into real-time monitoring systems and adapted to emerging 6G networks and hybrid communication architectures. The proposed solution contributes to improving the efficiency of telecommunications infrastructure management in the context of rapidly expanding digital services and high network usage intensity.
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