Energy Harvesting Techniques in Cognitive Radio Networks

Cognitive radio networks (CRNs) address the twin pressures of spectrum scarcity and energy limitation by enabling secondary users to adaptively access underutilised frequency bands while scavenging energy from ambient sources. Energy harvesting techniques in this context draw on radio frequency (RF) signals, solar irradiation, mechanical vibration or thermal differentials to replenish on-board storage without reliance on fixed power supplies. Architectures typically follow harvest-then-transmit or simultaneous wireless information and power transfer (SWIPT) paradigms, implemented via time-switching or power-splitting circuits. Underlay, overlay and hybrid spectrum access modes govern interference to primary users, balancing energy intake, sensing accuracy and transmission power. Joint optimisation frameworks determine sensing duration, harvest intervals and power allocation so as to maximise throughput, energy efficiency or quality of service under constraints of energy causality and interference thresholds. Key challenges include the stochastic nature of ambient energy, limited conversion efficiency and finite battery capacity, which complicate resource management across the physical, MAC and network layers. Practical deployments span low-power sensor networks, Internet of Things nodes in smart cities and next-generation cellular relays, where self-sustaining operation can extend network lifetime and reduce maintenance. Emerging trends focus on machine-learning-driven adaptation, cooperative harvesting among clusters of devices and cross-layer protocols that jointly manage spectrum, energy and data flows for resilient, green wireless infrastructures.

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Energy Harvesting Techniques in Cognitive Radio Networks publication trend

The graph below shows the total number of articles in energy harvesting techniques in cognitive radio networks across all publications each year (not limited to Nature Index journals).

Technical terms

Cognitive Radio Network (CRN): A wireless system in which unlicensed users dynamically access underutilised spectrum without causing harmful interference to licensed users.

Spectrum Sensing: The process by which secondary users detect the presence or absence of primary transmissions to identify idle channels for opportunistic access.

Radio Frequency Energy Harvesting (RF EH): The conversion of ambient electromagnetic waves into usable electrical power via rectifying antenna circuits.

Simultaneous Wireless Information and Power Transfer (SWIPT): A technique enabling concurrent data reception and energy harvesting from the same RF signal using time-switching or power-splitting hardware.

Underlay and Overlay Access: Spectrum-sharing modes where secondary transmissions either remain below a strict interference threshold (underlay) or opportunistically use idle bands without co-channel primary transmissions (overlay).

Energy Causality Constraint: A requirement that a node’s energy expenditure at any time does not exceed the cumulative harvested energy stored in its battery.

Quality of Service (QoS): The performance level of a communication service, typically defined by metrics such as throughput, delay and error rate.

References

1. Radio-in-the-Loop simulation and emulation modeling for energy-efficient and cognitive Internet of Things in smart cities: A cross-layer optimization case study. Computer Communications (2024).
2. Throughput Optimization of Multichannel Allocation Mechanism under Interference Constraint for Hybrid Overlay/underlay Cognitive Radio Networks with Energy Harvesting. Electronics (2020).
3. Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network. Sensors (2017).
4. Resource Allocation in Multi-Cluster Cognitive Radio Networks With Energy Harvesting for Hybrid Multi-Channel Access. IEEE Access (2023).
5. Shapley-Value-Based Hybrid Metaheuristic Multi-Objective Optimization for Energy Efficiency in an Energy-Harvesting Cognitive Radio Network. Mathematics (2023).
6. Maximizing Average Throughput of Cooperative Cognitive Radio Networks Based on Energy Harvesting. Sensors (2022).