Scholarlink

As 5G networks continue to evolve and pave the way for future telecommunication technologies, the role of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing traffic management becomes increasingly crucial. This paper explores the integration of AI and ML in telecommunication networks, focusing on their applications, challenges, and potential solutions for traffic optimization in 5G and beyond. The paper looks into specific use cases, such as network congestion management, quality of service (QoS) enhancement, and energy efficiency improvements. Additionally, the paper discusses the implications of AI-driven traffic optimization on network performance, user experience, and the broader telecommunication industry landscape. Through this review, the paper shed light on the transformative potential of AI and ML in shaping the future of telecommunication networks.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-Driven Traffic Optimization in 5G and Beyond: Challenges, Strategies, Solutions, and Prospects

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Ezekiel Ehime Agbon,
  • Aminu Chiroma Muhammad,
  • Christopher Akinyemi Alabi,
  • Agburu Ogah Adikpe,
  • Sena Timothy Tersoo,
  • Agbotiname Lucky Imoize,
  • Samarendra Nath Sur

摘要

As 5G networks continue to evolve and pave the way for future telecommunication technologies, the role of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing traffic management becomes increasingly crucial. This paper explores the integration of AI and ML in telecommunication networks, focusing on their applications, challenges, and potential solutions for traffic optimization in 5G and beyond. The paper looks into specific use cases, such as network congestion management, quality of service (QoS) enhancement, and energy efficiency improvements. Additionally, the paper discusses the implications of AI-driven traffic optimization on network performance, user experience, and the broader telecommunication industry landscape. Through this review, the paper shed light on the transformative potential of AI and ML in shaping the future of telecommunication networks.