# Massive Access for 5G and Beyond

arXiv: https://arxiv.org/abs/2002.03491 · HTML: https://mlsanity.com/paper/2002.03491/

## Key facts

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

## Built on by (most cited first, 23 of 23)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses](https://mlsanity.com/paper/2302.08418/) (2023)
- [HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks](https://mlsanity.com/paper/2204.06760/) (2022)
- [Non-Coherent Over-the-Air Decentralized Gradient Descent](https://mlsanity.com/paper/2211.10777/) (2022)
- [Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics](https://mlsanity.com/paper/2208.02724/) (2022)
- [Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks](https://mlsanity.com/paper/2203.14339/) (2022)
- [Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach](https://mlsanity.com/paper/2309.08688/) (2023)
- [Delay-aware Backpressure Routing Using Graph Neural Networks](https://mlsanity.com/paper/2211.10748/) (2022)
- [Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications](https://mlsanity.com/paper/2301.00516/) (2023)
- [Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks](https://mlsanity.com/paper/2312.02471/) (2023)
- [Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models](https://mlsanity.com/paper/2311.09349/) (2023)
- [Biased Backpressure Routing Using Link Features and Graph Neural Networks](https://mlsanity.com/paper/2407.09753/) (2024)
- [Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems](https://mlsanity.com/paper/2502.13390/) (2025)
- [Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization](https://mlsanity.com/paper/2108.13669/) (2021)
- [Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network](https://mlsanity.com/paper/2209.06392/) (2022)
- [TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2211.11692/) (2022)
- [Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication](https://mlsanity.com/paper/2403.07255/) (2024)
- [Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO](https://mlsanity.com/paper/2406.07160/) (2024)
- [Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2503.11305/) (2025)
- [Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)](https://mlsanity.com/paper/2509.05447/) (2025)
- [A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking](https://mlsanity.com/paper/2512.10874/) (2025)
- [Meta-Learning-Based Handover Management in NextG O-RAN](https://mlsanity.com/paper/2512.22022/) (2025)
- [MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computation](https://mlsanity.com/paper/2602.18332/) (2026)

## 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.