[Paper Review] Energy Harvesting in 5G Networks: Taxonomy, Requirements, Challenges, and Future Directions

Muhammad Imran, Latif U. Khan+4 more|arXiv (Cornell University)|Oct 2, 2019

Energy Harvesting in Wireless NetworksEngineering13 references17 citations

TL;DR

This paper proposes a comprehensive taxonomy and analysis of energy harvesting in 5G networks, categorizing energy sources, harvesting methods, and system models to address energy efficiency challenges. It identifies key requirements, open challenges, and future research directions, including SWIPT-NOMA integration and energy-optimized wearable networks, to enable sustainable, self-powered 5G infrastructure.

ABSTRACT

Consciousness of energy saving is increasing in fifth-generation (5G) wireless networks due to the high energy consumption issue. Energy harvesting technology is a possible appealing solution for ultimately prolonging the lifetime of devices and networks. Although considerable research efforts have been conducted in the context of using energy harvesting technology in 5G wireless networks, these efforts are in their infancy, and a tutorial on this topic is still lacking. This study aims to discuss the beneficial role of energy harvesting technology in 5G networks. We categorize and classify the literature available on energy harvesting in 5G networks by devising a taxonomy based on energy sources; energy harvesting devices, phases, and models; energy conversion methods, and energy propagation medium. The key requirements for enabling energy harvesting in 5G networks are also outlined. Several core research challenges that remain to be addressed are discussed. Furthermore, future research directions are provided.

Motivation & Objective

• To address the growing energy consumption in 5G networks driven by massive connectivity and high data rates.
• To identify and classify energy harvesting techniques applicable to 5G multi-tier heterogeneous networks.
• To outline key technical and operational requirements for deploying energy harvesting in 5G infrastructure.
• To highlight unresolved challenges such as hardware impairments and imperfect channel state information.
• To propose future research directions including energy-optimized wearables, three-tier cooperative networks, and SWIPT-NOMA integration.

Proposed method

• Developed a multi-dimensional taxonomy of energy harvesting in 5G networks based on energy sources, harvesting devices, phases, models, conversion methods, and propagation media.
• Analyzed energy harvesting from ambient RF signals, solar, thermal, wind, vibration, and human body sources for 5G devices.
• Proposed adaptive energy management schemes using game theory, learning theory, and optimization to allocate harvested energy efficiently.
• Explored simultaneous wireless information and power transfer (SWIPT) in conjunction with Non-Orthogonal Multiple Access (NOMA) for dual-purpose RF transmission.
• Modeled energy harvesting in three-tier cooperative 5G networks to jointly optimize energy efficiency, spectral efficiency, and mobility handling.
• Evaluated the impact of practical constraints such as residual hardware impairments and imperfect channel state information in SWIPT-NOMA systems.

Experimental results

Research questions

• RQ1 How can energy harvesting be systematically categorized and classified within 5G network architectures based on key parameters like energy sources and conversion methods?
• RQ2 What are the core technical requirements for enabling energy harvesting in 5G networks, especially in multi-tier heterogeneous deployments?
• RQ3 What are the major open challenges in implementing energy harvesting, particularly concerning hardware impairments and channel state information accuracy?
• RQ4 How can SWIPT-NOMA systems be optimized under realistic conditions such as imperfect hardware and channel estimation?
• RQ5 What future network architectures—such as energy-optimized wearables or three-tier cooperative networks—can best leverage energy harvesting for improved sustainability?

Key findings

• Energy harvesting from ambient RF signals, solar, thermal, and human body sources can significantly extend the lifetime of 5G devices and base stations.
• A multi-faceted taxonomy based on energy sources, harvesting devices, and system models enables systematic classification of existing research in 5G energy harvesting.
• Adaptive energy resource allocation using game theory and learning-based models can improve energy efficiency and fairness in 5G networks.
• SWIPT-NOMA integration shows promise for enhancing network throughput and energy efficiency, but performance degrades under hardware impairments and imperfect channel state information.
• Future 5G networks must jointly optimize energy efficiency, spectral efficiency, and mobility support, with energy harvesting as a core enabler.
• Energy-optimized wearable 5G networks require further research into selecting and optimizing harvesting techniques tailored to device-specific energy demands and usage patterns.