Research Article Open Access

Enhancing Telecommunication Network Management with Autonomous Optimization Agents and Machine Learning

Danny Adil Sibarani1 and Evaristus Didik Madyatmadja2
  • 1 Department of Information System Management, BINUS Graduate Program Master of Information System Management, Bina Nusantara University, Jakarta, Indonesia
  • 2 Department of Information Systems, School of Information Systems, Bina Nusantara University, Jakarta, Indonesia

Abstract

This study explores the integration of Machine Learning (ML) and Autonomous Optimization Agents (AOAs) in the management and optimization of Radio Access Networks (RAN). The research addresses the growing challenges posed by the need for skilled network experts capable of managing and analyzing large-scale network data, including Key Performance Indicators (KPIs) and thousands of network configuration parameters. To overcome these challenges, the study proposes ML-based AOAs that autonomously monitor, manage, and optimize network performance, thereby reducing reliance on human expertise. Specifically, the study utilizes Deep Reinforcement Learning (DRL) to analyze network data and optimize key network parameters. Focusing on 4G LTE networks in a region of Indonesia, managed by a well-known operator, the study demonstrates the potential of AOAs in improving network efficiency, managing information overload, and optimizing critical KPIs. The findings highlight the significant impact of ML and AOAs on telecommunication network management, offering a more sustainable, efficient, and effective solution for RAN optimization.

Journal of Computer Science
Volume 21 No. 2, 2025, 322-335

DOI: https://doi.org/10.3844/jcssp.2025.322.335

Submitted On: 12 August 2024 Published On: 4 March 2025

How to Cite: Sibarani, D. A. & Madyatmadja, E. D. (2025). Enhancing Telecommunication Network Management with Autonomous Optimization Agents and Machine Learning. Journal of Computer Science, 21(2), 322-335. https://doi.org/10.3844/jcssp.2025.322.335

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Keywords

  • Machine Learning
  • Autonomous Optimization Agents
  • Telecommunication Networks
  • Radio Access Network Optimization
  • Deep Reinforcement Learning