Optimizing quality of service forecasting in mobile networks through modified walrus optimization and multivariate approaches

Bandu Uppalaiah (), D. Mallikarjuna Reddy (), Vediyappan Govindan () and Haewon Byeon ()

Edelweiss Applied Science and Technology, 2024, vol. 8, issue 6, 7878-7901

Abstract: This paper presents Ensemble-based Service Quality Prediction (EAQP), an automated method for predicting service quality under changing mobile network conditions. EAQP incorporates data preparation methods such as transformation, purification, & imputation, and then performs feature extraction utilizing statistical, geographical, as well as temporal approaches. An improved feature selection method, using a unique weighting approach and optimized by a modified Walrus Optimization Algorithm, improves the accuracy of predictions. EAQP utilizes a variety of prediction models such as support vector regression, recurrent neural network models, bi-directional short-term long-term memory networks, extreme learning machines, along with multi-layer perceptron neural networks to enhance predictive accuracy. EAQP uses complex optimization algorithms and ensemble learning approaches to provide precise and dependable predictions about service quality in real-time. This helps in proactive network management as well as improvement. This comprehensive approach shows potential for boosting network efficiency, optimizing the distribution of resources, and enhancing the end-user experience when using mobile communications systems.

Keywords: Ensemble-based prediction; Feature extraction; Recurrent neural networks; Service quality prediction; Walrus optimisation algorithm. (search for similar items in EconPapers)
Date: 2024
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