Massive Machine-type Communications in 5G: Physical and MAC-layer solutions
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Abstract (arXiv)
Machine-type communications (MTC) are expected to play an essential role within future 5G systems. In the FP7 project METIS, MTC has been further classified into "massive Machine-Type Communication" (mMTC) and "ultra-reliable Machine-Type Communication" (uMTC). While mMTC is about wireless connectivity to tens of billions of machine-type terminals, uMTC is about availability, low latency, and high reliability. The main challenge in mMTC is scalable and efficient connectivity for a massive number of devices sending very short packets, which is not done adequately in cellular systems designed for human-type communications. Furthermore, mMTC solutions need to enable wide area coverage and deep indoor penetration while having low cost and being energy efficient. In this article, we introduce the physical (PHY) and medium access control (MAC) layer solutions developed within METIS to address this challenge.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2016.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 4; 2025: 1.
11 papers.
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
- 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directionscited by 10
- 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
- 2024Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networkscited by 5
- 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
- 2023Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communicationscited by 3
- 2021Throughput and Latency in the Distributed Q-Learning Random Access mMTC Networkscited by 1
- 2022Activity Detection for Grant-Free NOMA in Massive IoT Networkscited by 1
- 2021Set-Theoretic Learning for Detection in Cell-Less C-RAN Systemscited by 0
- 2023Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approachcited by 0
- 2024Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMOcited by 0
- 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey13 Mar 2025
- 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions5 Jul 2024
- 2024Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO11 Jun 2024
- 2024Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks7 Jun 2024
- 2024Multiple Access in the Era of Distributed Computing and Edge Intelligence26 Feb 2024
- 2023Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach5 Mar 2023
- 2023Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications2 Jan 2023
- 2022Activity Detection for Grant-Free NOMA in Massive IoT Networks23 Dec 2022
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges14 Nov 2021
- 2021Throughput and Latency in the Distributed Q-Learning Random Access mMTC Networks30 Oct 2021
- 2021Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems21 Mar 2021