[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125984-en":3,"doc-seo-125984-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},125984,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Hybrid Machine Learning Approaches for 5G Traffic Prediction - Research Article","Accurate traffic prediction remains challenging due to the continuously increasing scale, diversity, and evolving characteristics of 5G network traffic driven by user demand. Traditional simulation and conventional models can therefore produce incorrect estimations and inefficient resource usage. A hybrid machine learning model is proposed by integrating support vector machine (SVM) and decision tree algorithms. The hybrid structure dynamically adjusts hidden layers and units to improve accuracy, evaluated with MSE, MAE, and RMSE. Results show consistently lower errors than SVM alone, supported by R-squared comparisons versus signal-to-noise ratios, highlighting improved traffic prediction for network control.","Research Article  \nHybrid Machine Learning Approaches for 5G Traffic  \nPrediction  \nMohamed Burhan Mohamed   \nSunni Endowment Office [mohamedalmajamie@gmail.com](mohamedalmajamie@gmail.com)  \nA R T I C L E I N F O  \nArticle History  \nReceived: 20/08/2024  \nAccepted: 04/10/2024  \nPublished:12/11/2024 This is an open-access article under the CC BY 4.0 license:  \n[http://creativecommons](http://creativecommons). org/licenses/by/4.0/  \nABSTRACT  \nAccurate traffic prediction poses great difficulties because of the continuously increasing scale and diversity of 5G network traffic, which is driven by user demands. Moreover, certain characteristics of 5G traffic are constantly changing; thus, simulations using traditional models often lead to incorrect estimations or inefficient utilization of available resources. Consequently, we propose a hybrid machine learning model that integrates support vector machine (SVM) and decision tree algorithms to enhance efficiency of 5G traffic prediction. The structure of the hybrid model dynamically adjusts by adding or removing hidden layers and units within the network to improve prediction performance. The efficacy of the proposed model is evaluated using metrics like mean squared error, mean absolute error, and root mean squared error (RMSE) . Findings show that the hybrid model consistently achieves lower error rates than SVM alone. Further performance enhancement of the hybrid model in predicting 5G traffic is also supported by comparisons of Rsquared values against signal-to-noise ratios. These outcomes show the potential of the proposed method to improve traffic prediction accuracy in 5G networks, serving as a powerful tool for network control.  \nKeywords: ML, SVM, DT, RMSE, MAE, AI.  \n1. INTRODUCTION  \nThe development and application of 5G technology is meeting the growing demand for mobile communication [1] . The economy and society are becoming increasingly digital, networked, and intelligent thanks to the new, rapidly expanding technological revolution [2] . A 5G network offers several advantages, including high speed, extreme reliability, and minimal latency. With global accessibility, 5G technology satisfies the substantial resource demands of extensive terminal networks. However, it also introduces exponential increases in network traffic, heterogeneity, and complexity as shown in Figure (1) . To manage the considerable traffic load caused by massive, heterogeneous data streams in traditional cellular networks, 5G operators deploy numerous low-power micro- and pico-base stations around macro-base stations. This configuration serves to offload traffic and maintain load balance across macro-base stations [3, 4] . Accurate traffic prediction is essential for optimizing the deployment and allocation of 5G cellular network resources in large-scale cities and enhance the intelligence and reliability of traffic management systems [5] . Given that 5G network traffic is inherently time-series data, the prediction challenge can be framed as a time-series prediction modeling problem [6] . Past methods mostly used mathematical theories, such as statistics and probability distributions, to model and forecast traffic flow. This kind of approach rely on finite parameters rather than dataset size [7] .  \nSeveral studies have explored 5G traffic prediction using machine learning and deep learning. For example, developed machine learning models based on lower-layer parameters that characterize the radio environment to predict the available throughput. These models were tested on the LTE network before they were applied to thenon-standalone 5G network. also considered extending the proposed models to future standalone 5G networks. For LTE and non-standalone 5G networks, the end-user throughput was modeled with R-squared values of 93% and 84% and mean squared errors of 0.06, 0.47, and 17, respectively [8] .  \nFig. 1. 5G Network [9]  \nWeiwei Jiang categorized prediction difficulties into temporal and spat","cbCain7h7skMHaIC","https://ap.wps.com/l/cbCain7h7skMHaIC","pdf",803451,1,"English","en",105,"# Abstract\n# Introduction\n## Background and challenges of 5G traffic prediction\n## Network architecture and resource management context\n## Prior work using statistical, machine learning, and deep learning methods","[{\"question\":\"Why is 5G traffic prediction difficult?\",\"answer\":\"5G traffic scales rapidly, varies across users, and changes over time, making traditional models prone to inaccurate estimation and inefficient resource usage.\"},{\"question\":\"What hybrid approach is proposed for the prediction model?\",\"answer\":\"The model combines support vector machine (SVM) and decision tree algorithms and uses a dynamically adjusted structure by adding or removing hidden layers and units.\"},{\"question\":\"How is the model’s performance evaluated?\",\"answer\":\"Evaluation uses MSE, MAE, and RMSE, and further performance is supported by comparing R-squared values against signal-to-noise ratios.\"}]","Hybrid Machine Learning Approaches for 5G Traffic Prediction - 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