Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey
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Abstract (arXiv)
The advancements in smart power grid and the advocation of ``green communications'' have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthroughs on the utilization, redistribution, trading and planning of energy harvested in future wireless networks interoperating with smart grids. This article starts with classical models of renewable energy harvesting technologies. We embark on constrained operation and optimization of different energy harvesting wireless systems, such as point-to-point, multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell systems. We also review wireless power and information transfer technologies which provide a special implementation of energy harvesting wireless communications. A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place, and two-way energy trading between cellular networks and smart grids. Applications of different optimization tools, such as convex optimization, Lagrangian dual-based method, subgradient method, and Lyapunov-based online optimization, are compared. This article also collates the potential applications of energy harvesting techniques in emerging (or upcoming) 5G/B5G communication systems. It is revealed that an effective redistribution and two-way trading of energy can significantly reduce the electricity bills of wireless service providers and decrease the consumption of brown energy. A list of interesting research directions are provided, requiring further investigation.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 28
- 2023Queue Scheduling with Adversarial Bandit Learningcited by 3
- 2021Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Surveycited by 2
- 2026Networking-Aware Energy Efficiency in Agentic AI Inference: A Surveycited by 0
- 2026Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey9 Apr 2026
- 2023Queue Scheduling with Adversarial Bandit Learning3 Mar 2023
- 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey7 Nov 2022
- 2021Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey20 Jan 2021