mlsanityData to 14 Oct 2026

Massive Access for 5G and Beyond

arXiv 2002.03491 ↗First posted 10 Feb 2020cs.IT eess.SP math.ITcited by 23

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Abstract (arXiv)

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of aspects of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Feb 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 7; 2023: 5; 2024: 3; 2025: 5; 2026: 1.

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  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  2. 2023Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaversescited by 22
  3. 2022HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networkscited by 12
  4. 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
  5. 2022Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statisticscited by 5
  6. 2022Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networkscited by 4
  7. 2023Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approachcited by 4
  8. 2022Delay-aware Backpressure Routing Using Graph Neural Networkscited by 3
  9. 2023Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communicationscited by 3
  10. 2023Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networkscited by 3
  11. 2023Generative AI-Based Probabilistic Constellation Shaping With Diffusion Modelscited by 2
  12. 2024Biased Backpressure Routing Using Link Features and Graph Neural Networkscited by 2
11 more
  1. 2025Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systemscited by 1
  2. 2021Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimizationcited by 0
  3. 2022Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Networkcited by 0
  4. 2022TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement Learningcited by 0
  5. 2024Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communicationcited by 0
  6. 2024Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMOcited by 0
  7. 2025Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networkscited by 0
  8. 2025Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)cited by 0
  9. 2025A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networkingcited by 0
  10. 2025Meta-Learning-Based Handover Management in NextG O-RANcited by 0
  11. 2026MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computationcited by 0