Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks (1302.4793v2)

Abstract: Wireless networks can be self-sustaining by harvesting energy from ambient radio-frequency (RF) signals. Recently, researchers have made progress on designing efficient circuits and devices for RF energy harvesting suitable for low-power wireless applications. Motivated by this and building upon the classic cognitive radio (CR) network model, this paper proposes a novel method for wireless networks coexisting where low-power mobiles in a secondary network, called secondary transmitters (STs), harvest ambient RF energy from transmissions by nearby active transmitters in a primary network, called primary transmitters (PTs), while opportunistically accessing the spectrum licensed to the primary network. We consider a stochastic-geometry model in which PTs and STs are distributed as independent homogeneous Poisson point processes (HPPPs) and communicate with their intended receivers at fixed distances. Each PT is associated with a guard zone to protect its intended receiver from ST's interference, and at the same time delivers RF energy to STs located in its harvesting zone. Based on the proposed model, we analyze the transmission probability of STs and the resulting spatial throughput of the secondary network. The optimal transmission power and density of STs are derived for maximizing the secondary network throughput under the given outage-probability constraints in the two coexisting networks, which reveal key insights to the optimal network design. Finally, we show that our analytical result can be generally applied to a non-CR setup, where distributed wireless power chargers are deployed to power coexisting wireless transmitters in a sensor network.

• The paper introduces a novel integration of energy harvesting into cognitive radio networks, enabling opportunistic spectrum access and improved energy efficiency.
• It employs a stochastic-geometry model with guard and harvesting zones to derive key metrics for transmission probability, outage probability, and throughput.
• Numerical simulations validate that balancing transmitter power and density optimizes network performance under strict outage constraints.

Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks: An Overview

This paper presents a methodological advancement in the design of self-sustaining wireless networks by incorporating wireless energy harvesting into cognitive radio (CR) networks. It provides a comprehensive analysis of a novel system where secondary transmitters (STs) in a cognitive radio network harvest energy from primary transmitters (PTs) while opportunistically accessing the spectrum. This approach is based on a stochastic-geometry model, which considers both PTs and STs as independent homogeneous Poisson point processes (HPPPs). The research delineates strategies for maximizing the throughput of the secondary network under constraints related to outage probability in both primary and secondary networks.

Key Contributions and Methodology

The primary contribution of this research lies in the integration of energy harvesting with cognitive radio technology, facilitating a dual-function system where energy and spectrum are efficiently utilized. This integration is achieved through a guard zone and harvesting zone model. Guard zones are used to mitigate interference with primary networks, and harvesting zones are established for the STs to collect energy.

1. Stochastic-Geometry Model: The paper employs a stochastic-geometry model to analyze the distribution of PTs and STs. This model not only allows the derivation of key throughput and outage probability metrics but also guides the optimal design of CR networks.
2. Guard and Harvesting Zones: Each PT is associated with a guard zone to prevent interference with its intended receiver, while a smaller harvesting zone allows STs to harvest energy from nearby PTs. The model assumes negligible overlap between different harvesting zones due to the low density of PTs.
3. Transmission Probability Analysis: Using Markov chain models, the paper derives transmission probabilities for STs under different charging conditions—single-slot and multi-slot scenarios—highlighting the impact of network parameters like transmission power and transmitter density.
4. Outage Probability and Network Throughput: The authors derive the outage probabilities for both primary and secondary networks, considering mutual interference. The analytical results indicate that a balance between transmission power and density is critical for achieving optimal throughput under predefined outage constraints.
5. Extension to Wireless Sensor Networks: The paper demonstrates the application of its analytical framework beyond cognitive radio networks to wireless sensor networks powered by distributed wireless power chargers, showcasing the broader applicability of the proposed model.

Numerical Results and Implications

The researchers provide numerical simulations to validate their theoretical models. These simulations reveal that optimal network performance in cognitive radio environments can be achieved through careful tuning of the transmission power and density of STs. The results show that an increase in ST transmission power generally decreases the transmission probability due to energy harvesting constraints, although secondary network throughput can be increased by optimizing these parameters.

The implications of this work are significant for the design of energy-efficient wireless networks. The integration of energy harvesting into cognitive radio networks could improve the sustainability of low-power devices, such as wireless sensors, by reducing dependency on traditional power sources. Additionally, the methodology could be extended to other applications, such as wireless sensor networks, contributing to advancements in sustainable network architecture design.

Future Directions

The methodology and results presented open several avenues for future research. Potential extensions include exploring the impact of more complex propagation environments and heterogeneity in transmitter power levels. Moreover, further investigation into the joint optimization of energy harvesting and information transmission functions could provide deeper insights into the trade-offs and synergies within opportunistic wireless systems.

This research provides a rigorous framework for understanding and exploiting the interplay between energy harvesting and spectrum access in cognitive radio networks, suggesting practical strategies for enhancing network performance in energy-constrained environments.