Adaptive Congestion Control in 5G Networks - Integrating Supervised and Unsupervised Machine Learning Techniques for Real-Time Traffic Management

Aug 5, 2026

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5G evolution makes congestion control essential to sustain low latency, high reliability, and consistent service quality. This work proposes a congestion prediction and mitigation model that combines supervised learning and unsupervised clustering to anticipate congestion events and adapt network parameters dynamically. The study benchmarks 26 supervised algorithms and 7 clustering methods using accuracy, precision, and recall, then integrates the best-performing models to enhance real-time traffic management and network efficiency across complex urban scenarios.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Introduction

Literature Review

Methodology

Results and Evaluation

Integration into 5G Traffic Management

Conclusion

"Adaptive Congestion Control in 5G Networks: Integrating Supervised and Unsupervised Machine Learning Techniques for Real-Time Traffic Management" Dr. Srinivasa Gowda GK Dean Bravee Multiskilling Acdemy Bangalore,india [Seenugowda2008@gmail.com](Seenugowda2008@gmail.com) Mr. Panchaxari Asssistant Professor ACS College of engineering Bangalore Abstract—With the advent of 5G technology, managing network traffic congestion efficiently has become crucial. This paper introduces an advanced congestion control prediction model that employs both supervised and unsupervised machine learning techniques to predict and mitigate congestion in 5G environments. The study evaluates 26 supervised learning algorithms and 7 clustering algorithms, identifying the most effective models based on accuracy, precision, and recall. Integrating these models into 5G networks enhances real-time traffic management, improves user experience, and optimizes network efficiency in complex urban environments. Keywords-component; formatting; style; styling; insert (key words) I. INTRODUCTION The emergence of 5G technology has brought about significant advancements in network capabilities, including higher speeds, lower latency, and improved coverage. These enhancements are essential to support various applications across different domains, from mobile communications to the Internet of Things, requiring intelligent management strategies to cope with the diverse and heterogeneous traffic patterns encountered in contemporary urban environments (Elsayed & Erol ‐Kantarci, 2019). As such, there is a pressing need for innovative congestion control solutions that effectively integrate machine learning techniques to facilitate real-time adaptability and efficiency, thereby ensuring optimal resource allocation and enhanced user experiences in increasingly complex network scenarios (Elsayed & Erol ‐ Kantarci, 2019) (Nouruzi et al., 2022) . In particular, the implementation of machine learning models allows for the prediction of congestion events and the adjustment of network parameters dynamically, thus addressing the challenges posed by the rapid growth of connected devices and varying traffic types (Li et al., 2020) . Moreover, these machine learningdriven methodologies can significantly enhance the performance of 5G networks by actively monitoring and analyzing traffic patterns, thus allowing for timely interventions to prevent congestion and maintain quality of service, which is crucial given the demands of modern applications that require low latency and high reliability.(Shehzad et al., 2022) (Li et al., 2020) (Elsayed & Erol ‐ Kantarci, 2019) (Nouruzi et al., 2022) In this paper, we investigate the use of both supervised and unsupervised machine learning approaches to predict and mitigate congestion in 5G networks. Our study evaluates a range of algorithms designed to enhance congestion control mechanisms by leveraging real-time data to improve decisionmaking processes, which is becoming increasingly essential as network conditions evolve and user expectations rise. To this end, we explore various machine learning models that demonstrate the potential to revolutionize congestion management strategies tailored to 5G environments, thereby enabling more efficient digital communication solutions that can adapt to ongoing network changes and provide a consistent user experience under high-demand A diagram illustrating the 5G network's architecture, showing the various traffic sources, potential congestion points, and how machine learning models can be integrated to predict and manage congestion. LITERATURE REVIEW Recent studies have highlighted the growing importance of incorporating machine learning techniques into congestion control strategies for 5G networks to address the complexities introduced by the surge in connected devices and diverse traffic types –. Recent studies have highlighted the growing importance of incorporat

FAQs

What problem does the paper address in 5G networks?

It targets efficient prediction and mitigation of traffic congestion in 5G to maintain low latency, reliability, and user experience.

How does the proposed approach use machine learning?

It integrates supervised learning for prediction with unsupervised clustering to detect patterns and support proactive congestion management.

Which algorithms and metrics are used to evaluate performance?

The study evaluates 26 supervised learning algorithms and 7 clustering algorithms using accuracy, precision, and recall.

Aug 5, 2026

Upload by Rowan