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Modeling and Performance Analysis of LBT-Based RF-Powered NR-U Network for IoT
Varada Potnis Kulkarni1, Radhika D Joshi1
1Department of Electronics and Telecommunication Engineering, COEP Technological University, Formerly College of Engineering Pune, Wellesley Road, Shivajinagar, Pune 411005, India.
Sensors (Basel, Switzerland)
|August 29, 2024
Summary
RF-powered New Radio Unlicensed (NR-U) networks offer a sustainable solution for cellular Internet of Things (IoT) by combining energy harvesting and spectrum sharing. This approach addresses spectrum demand and IoT energy needs effectively.
Area of Science:
- Wireless communication networks
- Energy harvesting technologies
- Internet of Things (IoT) systems
Background:
- Growing demand for wireless spectrum and energy-efficient solutions for IoT devices.
- Emergence of New Radio Unlicensed (NR-U) technology for utilizing unlicensed spectrum.
- Potential of radio frequency (RF) energy harvesting to power IoT devices sustainably.
Purpose of the Study:
- To model and analyze the performance of RF-powered NR-U networks for cellular IoT.
- To derive key Quality of Service (QoS) metrics for network nodes and the base station.
- To investigate the impact of various network parameters on system performance.
Main Methods:
- Modeling the NR-U network using a Markov chain approach.
- Deriving closed-form expressions for normalized saturated throughput and mean packet delay.
- Computing transmit outage probability and analyzing QoS parameters.
- Observing the effect of network density on collision, transmission, and energy harvesting probabilities.
Main Results:
- Closed-form expressions for key performance indicators like throughput and packet delay were derived.
- Analysis revealed the influence of congestion window size, TXOP, and energy levels on QoS.
- Network density was shown to affect collision, transmission, and energy harvesting probabilities.
- The proposed model was validated through simulations, confirming its accuracy.
Conclusions:
- RF-powered NR-U networks present a viable and sustainable solution for cellular IoT.
- The developed Markov chain model provides valuable insights into network performance and QoS.
- Optimizing parameters like energy levels and network density can enhance the efficiency of IoT networks.
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