Energy Harvesting in Wireless Communication Systems

Energy harvesting in wireless communication systems refers to the capture and conversion of ambient energy—such as solar, radio‐frequency, thermal and mechanical vibrations—into electrical power to sustain or supplement the operation of wireless devices. By integrating miniature energy converters and storage elements, contemporary networks aim for energy‐neutral operation, in which harvested energy meets device demand over time. Key challenges include the inherent intermittency and unpredictability of ambient sources, stringent size and cost constraints on storage, and the need to co-optimise energy management with data‐transmission protocols. Recent advances focus on the joint design of physical‐layer techniques—such as simultaneous wireless information and power transfer (SWIPT) and adaptive modulation—and higher-layer strategies like dynamic scheduling, power allocation, and machine-learning-based control. Practical implementations span Internet of Things (IoT) sensor networks, relay-assisted links and cloud-radio access networks powered by renewable sources. These systems promise extended lifetimes, reduced maintenance and new deployment paradigms in remote or difficult-to-service environments.

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Energy Harvesting in Wireless Communication Systems publication trend

The graph below shows the total number of articles in energy harvesting in wireless communication systems across all publications each year (not limited to Nature Index journals).

Technical terms

Energy harvesting: The process of capturing ambient energy (solar, RF, thermal, mechanical) and converting it into electrical power to supply electronic devices.

Energy neutral operation: A design objective whereby a system’s energy consumption is balanced by its harvested energy over a given time horizon, ensuring sustainable operation without external power.

Simultaneous wireless information and power transfer (SWIPT): A technique allowing a receiver to decode information and harvest energy from the same transmitted electromagnetic signal, thereby improving resource utilisation.

Reinforcement learning: A branch of machine learning in which an agent learns to make decisions by interacting with its environment and maximising a cumulative reward signal, often under uncertainty.

References

1. Machine Learning Applications in Energy Harvesting Internet of Things Networks: A Review. IEEE Access (2025).
2. Sparse Beamforming for Real-Time Resource Management and Energy Trading in Green C-RAN. IEEE Transactions on Smart Grid (2016).
3. A Survey on Energy Harvesting Wireless Networks: Channel Capacity, Scheduling, and Transmission Power Optimization. Electronics (2021).
4. Deep Reinforcement Learning-Based Access Control for Buffer-Aided Relaying Systems With Energy Harvesting. IEEE Access (2020).