mlsanityData to 7 Oct 2026

A Review of the Convergence of 5G/6G Architecture and Deep Learning

arXiv 2208.07643 ↗First posted 16 Aug 2022cs.LG cs.AI cs.NIcited by 0

Not on the map. Why?

Abstract (arXiv)

The convergence of 5G architecture and deep learning has gained a lot of research interests in both the fields of wireless communication and artificial intelligence. This is because deep learning technologies have been identified to be the potential driver of the 5G technologies, that make up the 5G architecture. Hence, there have been extensive surveys on the convergence of 5G architecture and deep learning. However, most of the existing survey papers mainly focused on how deep learning can converge with a specific 5G technology, thus, not covering the full spectrum of the 5G architecture. Although there is a recent survey paper that appears to be robust, a review of that paper shows that it is not well structured to specifically cover the convergence of deep learning and the 5G technologies. Hence, this paper provides a robust overview of the convergence of the key 5G technologies and deep learning. The challenges faced by such convergence are discussed. In addition, a brief overview of the future 6G architecture, and how it can converge with deep learning is also discussed.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 65 of its 206 references that are in the map. First posted on arXiv 16 Aug 2022.

Builds on

65 of 206 references.

References · 65

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2014Going Deeper with Convolutionscited by 4,231
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  4. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  5. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  7. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  8. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  9. 2014Conditional Generative Adversarial Netscited by 2,152
  10. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  11. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  12. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
38 more
  1. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
  2. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  3. 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 703
  4. 2017Dynamic Routing Between Capsulescited by 689
  5. 2013Generating Sequences With Recurrent Neural Networkscited by 549
  6. 2017Progressive Neural Architecture Searchcited by 483
  7. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  8. 2015Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networkscited by 305
  9. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
  10. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  11. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 163
  12. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 157
  13. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  14. 2018Deep Learning for Sentiment Analysis : A Surveycited by 139
  15. 2014Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimationcited by 129
  16. 2019DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applicationscited by 121
  17. 2019Convergence of Edge Computing and Deep Learning: A Comprehensive Surveycited by 120
  18. 2017Distributed Deep Neural Networks over the Cloud, the Edge and End Devicescited by 119
  19. 2018Deep Learning in Mobile and Wireless Networking: A Surveycited by 117
  20. 2017Learning to Optimize: Training Deep Neural Networks for Wireless Resource Managementcited by 96
  21. 2017Vulnerability of Deep Reinforcement Learning to Policy Induction Attackscited by 91
  22. 2016DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processingcited by 84
  23. 2014On Using Very Large Target Vocabulary for Neural Machine Translationcited by 80
  24. 2015A Primer on Neural Network Models for Natural Language Processingcited by 75
  25. 2017Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Carcited by 65
  26. 2017Deep Learning for IoT Big Data and Streaming Analytics: A Surveycited by 59
  27. 2014Question Answering with Subgraph Embeddingscited by 56
  28. 2018Model-Driven Deep Learning for Physical Layer Communicationscited by 50
  29. 2018Adversarial Attacks on Deep-Learning Based Radio Signal Classificationcited by 47
  30. 2018A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Securitycited by 42
  31. 2019DeepCaps: Going Deeper with Capsule Networkscited by 42
  32. 2019LOGAN: Latent Optimisation for Generative Adversarial Networkscited by 42
  33. 2018Software Engineering Challenges of Deep Learningcited by 41
  34. 2019Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutionscited by 29
  35. 2018Deep Reinforcement Learning for Resource Management in Network Slicingcited by 27
  36. 2019Artificial Intelligence-Enabled Intelligent 6G Networkscited by 27
  37. 2016Building Energy Load Forecasting using Deep Neural Networkscited by 24
  38. 2017DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Frameworkcited by 22

50 most cited of 65 All 65 (JSON)

Built on by

Not cited yet.