mlsanityData to 7 Oct 2026

Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G

arXiv 1907.07862 ↗First posted 18 Jul 2019cs.IT eess.SP math.ITcited by 25

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Abstract (arXiv)

Mobile Network Operators (MNOs) are in process of overlaying their conventional macro cellular networks with shorter range cells such as outdoor pico cells. The resultant increase in network complexity creates substantial overhead in terms of operating expenses, time, and labor for their planning and management. Artificial intelligence (AI) offers the potential for MNOs to operate their networks in a more organic and cost-efficient manner. We argue that deploying AI in 5G and Beyond will require surmounting significant technical barriers in terms of robustness, performance, and complexity. We outline future research directions, identify top 5 challenges, and present a possible roadmap to realize the vision of AI-enabled cellular networks for Beyond-5G and 6G.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2019.

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Citing papers per year: 2021: 9; 2022: 5; 2023: 5; 2024: 5; 2025: 0; 2026: 1.

25 papers.

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  1. 2021Differential Privacy Meets Federated Learning under Communication Constraintscited by 15
  2. 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 8
  3. 2022Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysiscited by 7
  4. 2021RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detectioncited by 6
  5. 2021Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challengescited by 5
  6. 2022AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networkscited by 4
  7. 2022Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processingcited by 4
  8. 2021The Next Decade of Telecommunications Artificial Intelligencecited by 3
  9. 2024Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextGcited by 3
  10. 2023Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processingcited by 2
  11. 2024Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visionscited by 2
  12. 2021Classification of Common Waveforms Including a Watchdog for Unknown Signalscited by 1
13 more
  1. 2023Distributed Learning Meets 6G: A Communication and Computing Perspectivecited by 1
  2. 2024OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation with Optimization-based Deep Learningcited by 1
  3. 2021Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computingcited by 0
  4. 2021Harnessing Tensor Structures -- Multi-Mode Reservoir Computing and Its Application in Massive MIMOcited by 0
  5. 2021Federated Dynamic Spectrum Accesscited by 0
  6. 2021Open-set Classification of Common Waveforms Using A Deep Feed-forward Network and Binary Isolation Forest Modelscited by 0
  7. 2022Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RANcited by 0
  8. 2023Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computingcited by 0
  9. 20232D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detectioncited by 0
  10. 2023An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environmentscited by 0
  11. 2024Attacks Against Mobility Prediction in 5G Networkscited by 0
  12. 2024Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrollingcited by 0
  13. 2026DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programmingcited by 0