Five Disruptive Technology Directions for 5G
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Abstract (arXiv)
New research directions will lead to fundamental changes in the design of future 5th generation (5G) cellular networks. This paper describes five technologies that could lead to both architectural and component disruptive design changes: device-centric architectures, millimeter Wave, Massive-MIMO, smarter devices, and native support to machine-2-machine. The key ideas for each technology are described, along with their potential impact on 5G and the research challenges that remain.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2013.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 0; 2024: 1; 2025: 3; 2026: 2.
12 papers.
- 2021Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrayscited by 6
- 2021Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO Systemcited by 4
- 2021Learn to Communicate with Neural Calibration: Scalability and Generalizationcited by 3
- 2021Machine Learning and Artificial Intelligence in Next-Generation Wireless Networkcited by 3
- 2025Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Networkcited by 2
- 2022Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learningcited by 1
- 2024Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decodingcited by 1
- 2022Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMOcited by 0
- 2025Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Predictioncited by 0
- 2025CSI Prediction Using Diffusion Modelscited by 0
- 2026Spatio-Temporal Scheduling Prediction Under Backhaul Delay for Resilient Coordinated Beamformingcited by 0
- 2026A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networkscited by 0
- 2026A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks9 Aug 2026
- 2026Spatio-Temporal Scheduling Prediction Under Backhaul Delay for Resilient Coordinated Beamforming9 Jul 2026
- 2025CSI Prediction Using Diffusion Models13 Oct 2025
- 2025Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Network14 May 2025
- 2025Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction14 Jan 2025
- 2024Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding9 Jun 2024
- 2022Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO28 Nov 2022
- 2022Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning26 Mar 2022
- 2021Machine Learning and Artificial Intelligence in Next-Generation Wireless Network30 Dec 2021
- 2021Learn to Communicate with Neural Calibration: Scalability and Generalization1 Oct 2021
- 2021Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System1 May 2021
- 2021Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays5 Jan 2021