# A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

arXiv: https://arxiv.org/abs/1604.00786 · HTML: https://mlsanity.com/paper/1604.00786/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [ML Approach for Power Consumption Prediction in Virtualized Base Stations](https://mlsanity.com/paper/2301.05764/) (2023)
- [A General Framework for Scalable UE-AP Association in User-Centric Cell-Free Massive MIMO based on Recurrent Neural Networks](https://mlsanity.com/paper/2503.04278/) (2025)
- [Energy-Aware 6G Network Design: A Survey](https://mlsanity.com/paper/2509.11289/) (2025)
- [Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?](https://mlsanity.com/paper/2606.23047/) (2026)
- [Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints](https://mlsanity.com/paper/2608.01745/) (2026)

## Abstract (arXiv)

After about a decade of intense research, spurred by both economic and operational considerations, and by environmental concerns, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, with millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribution to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.