# A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning

arXiv: https://arxiv.org/abs/2101.11246 · HTML: https://mlsanity.com/paper/2101.11246/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2021.

## Built on by (most cited first, 15 of 15)

- [An Energy and Carbon Footprint Analysis of Distributed and Federated Learning](https://mlsanity.com/paper/2206.10380/) (2022)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Machine Learning and Analytical Power Consumption Models for 5G Base Stations](https://mlsanity.com/paper/2209.11600/) (2022)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Power Consumption Modeling of 5G Multi-Carrier Base Stations: A Machine Learning Approach](https://mlsanity.com/paper/2212.04318/) (2022)
- [Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need](https://mlsanity.com/paper/2406.16929/) (2024)
- [Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach](https://mlsanity.com/paper/2401.00443/) (2023)
- [Efficient Beam Search for Initial Access Using Collaborative Filtering](https://mlsanity.com/paper/2209.06669/) (2022)
- [Mobile Network Control with a World Model](https://mlsanity.com/paper/2607.17747/) (2026)
- [Reducing the Environmental Impact of Wireless Communication via Probabilistic Machine Learning](https://mlsanity.com/paper/2311.12807/) (2023)
- [Intelligent Duty Cycling Management and Wake-up for Energy Harvesting IoT Networks with Correlated Activity](https://mlsanity.com/paper/2405.06372/) (2024)
- [A Multi-Armed Bandit Framework for Online Optimisation in Green Integrated Terrestrial and Non-Terrestrial Networks](https://mlsanity.com/paper/2506.09268/) (2025)
- [Reinforcement Learning with Reward Machines for Sleep Control in Mobile Networks](https://mlsanity.com/paper/2604.07411/) (2026)
- [A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites](https://mlsanity.com/paper/2609.03809/) (2026)
- [Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks](https://mlsanity.com/paper/2609.17297/) (2026)

## Abstract (arXiv)

Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low-latency communications. In addition, the third generation partnership project (3GPP) new radio (NR) specification also provides tools to significantly decrease the energy consumption and the green house emissions of next generations networks, thus contributing towards information and communication technology (ICT) sustainability targets. In this survey paper, we thoroughly review the state-of-the-art on current energy efficiency research. We first categorise and carefully analyse the different power consumption models and energy efficiency metrics, which have helped to make progress on the understanding of green networks. Then, as a main contribution, we survey in detail -- from a theoretical and a practical viewpoint -- the main energy efficiency enabling technologies that 3GPP NR provides, together with their main benefits and challenges. Special attention is paid to four key enabling technologies, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence capabilities. We dive into their implementation and operational details, and thoroughly discuss their optimal operation points and theoretical-trade-offs from an energy consumption perspective. This will help the reader to grasp the fundamentals of -- and the status on -- green networking. Finally, the areas of research where more effort is needed to make future networks greener are also discussed.