Designing intelligent energy harvesting communication systems
By Deniz Gündüz, Kostas Stamatiou, Nicolò Michelusi, Michele Zorzi. IEEE Communications Magazine, 2014
308 citations recorded by the source corpus.
From being a scientific curiosity only a few years ago, energy harvesting (EH) is well on its way to becoming a game-changing technology in the field of autonomous wireless networked systems. The promise of long-term, uninterrupted and self-sustainable operation in a diverse array of applications has captured the interest of academia and industry alike. Yet the road to the ultimate network of perpetual communicating devices is plagued with potholes: ambient energy is intermittent and scarce, energy storage capacity is limited, and devices are constrained in size and complexity. In dealing with these challenges, this article will cover recent developments in the design of intelligent energy management policies for EH wireless devices and discuss pressing research questions in this rapidly growing field.
Subfields: Electrical and Electronic Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/6856edede28aaeb4e22f44cb7577a49f7fb77dfb
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In Electrical and Electronic Engineering
- Rui Zhang
- Robert Schober
- Qingqing Wu
- Derrick Wing Kwan Ng
- Alessio Zappone
- H. Vincent Poor
- Geoffrey Y. Li
- Merouane Debbah
In Mechanical Engineering
Citations in the corpus
- The Power of Models: Modeling Power Consumption for IoT Devices (2015, background): “A natural constraint follows from energy causality, which dictates that energy cannot be used before it is available [7].”
- An Overview of Sustainable Green 5G Networks (2016, background): “Distinctive Energy-Data Causality and Battery Capacity Constraints: A fundamental feature that distinguishes energy harvesting communication systems from non-energy harvesting communication systems is the existence of an energy causality constraint [3].”
- Energy Harvesting Wireless Communications: A Review of Recent Advances (2015, unknown): “In Section IV, we consider online optimization of general reward functions, including the throughput.”
- A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead (2016, background): “Alternatively, approaches based on learning theory provide the means to design energy harvesting systems by having the users adapt to the environment based on past observations [128], [129].”