Scholarlink

The emergence of 5G technology (Singh et al. in IETE Tech Rev 34:30–39, 2017) has revolutionized the telecommunications industry, offering unprecedented data rates, ultra-low latency, and massive connectivity (Gopalaiah S, Khaitan A, Darbari S, Abhishek V, Kachru V, Srivastava N “5G: the Catalyst to Digital Revolution in India”, Confederation of Indian Industry, Deloitte, pg 10, 30–33. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016). Deep learning (Jagdale KJ, Shelke CJ, Achary R, WankhedeDS, Bhandaree TV Artificial intelligence and its subsets: machine learning and its algorithms, deep learning, and their future trends. JETIR 9(5):2–7, 2002), a subset of artificial intelligence (Chai J, Zeng H, Li A, Ngai EWT Deep learning in computer vision: a critical review of emerging techniques and application scenarios. Mach Learn Appl 6:10013, 2001), has shown tremendous potential in various domains, including computer vision, natural language processing, and data analytics (Zhang et al Computing resource allocation scheme of IOV using deep reinforcement learning in edge computing environment EURASIP J Adv Signal Processing 2021:33, 2021). This paper explores the integration of deep learning techniques with 5G technology to enhance performance, efficiency, and user experience in resource allocation (Odeyomi OT, Akintade OO, Olowu TO, Zaruba G A review of the convergence of 5G/6G architecture and deep learning. J Latex Cl Files 14(8), 2015). We discuss the challenges and opportunities associated with deep learning in 5G networks (Niu Y, Li Y, Jin D, Su L, and Vasilakos AV A survey of millimeter wave communications (mmwave) for 5G: opportunities and challenges. Wirel Netw 21(8):2657–2676, 2015) and present recent advancements, applications, and future directions for this powerful synergy.

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

Deep Learning-Based Resource Allocation in 5G Technology: A Case Study on Enhancing Performance and Efficiency

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Abburi Srirama Kanaka Ratnam,
  • Devireddy Srinivasa Kumar,
  • Vecha Sudhakar,
  • Arinapalli Santhi Latha,
  • Konakala Sirisha,
  • Yenireddy Nagapadmaja

摘要

The emergence of 5G technology (Singh et al. in IETE Tech Rev 34:30–39, 2017) has revolutionized the telecommunications industry, offering unprecedented data rates, ultra-low latency, and massive connectivity (Gopalaiah S, Khaitan A, Darbari S, Abhishek V, Kachru V, Srivastava N “5G: the Catalyst to Digital Revolution in India”, Confederation of Indian Industry, Deloitte, pg 10, 30–33. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016). Deep learning (Jagdale KJ, Shelke CJ, Achary R, WankhedeDS, Bhandaree TV Artificial intelligence and its subsets: machine learning and its algorithms, deep learning, and their future trends. JETIR 9(5):2–7, 2002), a subset of artificial intelligence (Chai J, Zeng H, Li A, Ngai EWT Deep learning in computer vision: a critical review of emerging techniques and application scenarios. Mach Learn Appl 6:10013, 2001), has shown tremendous potential in various domains, including computer vision, natural language processing, and data analytics (Zhang et al Computing resource allocation scheme of IOV using deep reinforcement learning in edge computing environment EURASIP J Adv Signal Processing 2021:33, 2021). This paper explores the integration of deep learning techniques with 5G technology to enhance performance, efficiency, and user experience in resource allocation (Odeyomi OT, Akintade OO, Olowu TO, Zaruba G A review of the convergence of 5G/6G architecture and deep learning. J Latex Cl Files 14(8), 2015). We discuss the challenges and opportunities associated with deep learning in 5G networks (Niu Y, Li Y, Jin D, Su L, and Vasilakos AV A survey of millimeter wave communications (mmwave) for 5G: opportunities and challenges. Wirel Netw 21(8):2657–2676, 2015) and present recent advancements, applications, and future directions for this powerful synergy.