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Joint Energy and SINR Coverage Probability in UAV Corridor-assisted RF-powered IoT Networks

Harris K. Armeniakos, Petros S. Bithas, Konstantinos Maliatsos, Athanasios G. Kanatas

TL;DR

This work addresses joint energy and SINR-based coverage in UAV corridor-assisted RF-powered IoT networks by modeling UAVs along a ground-projected corridor as a $1$D BPP. It derives an exact energy-coverage expression and a tight Gamma-distribution-based approximation for joint coverage, incorporating Nakagami fading and inverse-Gamma shadowing. Key contributions include closed-form-like analyses for distance distributions, energy and SINR coverages via Laplace transforms and moment matching, and the identification of optimal UAV density $N^*$ and corridor parameters $(R,h)$ that maximize joint performance. The results offer practical design guidelines for UAV corridor deployment, showing how charging duration and UAV density trade off to achieve robust joint energy and communication performance.

Abstract

This letter studies the joint energy and signal-to-interference-plus-noise (SINR)-based coverage probability in Unmanned Aerial Vehicle (UAV)-assisted radio frequency (RF)-powered Internet of Things (IoT) networks. The UAVs are spatially distributed in an aerial corridor that is modeled as a one-dimensional (1D) binomial point process (BPP). By accurately capturing the line-of-sight (LoS) probability of a UAV through large-scale fading: i) an exact form expression for the energy coverage probability is derived, and ii) a tight approximation for the overall coverage performance is obtained. Among several key findings, numerical results reveal the optimal number of deployed UAV-BSs that maximizes the joint coverage probability, as well as the optimal length of the UAV corridors when designing such UAV-assisted IoT networks.

Joint Energy and SINR Coverage Probability in UAV Corridor-assisted RF-powered IoT Networks

TL;DR

This work addresses joint energy and SINR-based coverage in UAV corridor-assisted RF-powered IoT networks by modeling UAVs along a ground-projected corridor as a D BPP. It derives an exact energy-coverage expression and a tight Gamma-distribution-based approximation for joint coverage, incorporating Nakagami fading and inverse-Gamma shadowing. Key contributions include closed-form-like analyses for distance distributions, energy and SINR coverages via Laplace transforms and moment matching, and the identification of optimal UAV density and corridor parameters that maximize joint performance. The results offer practical design guidelines for UAV corridor deployment, showing how charging duration and UAV density trade off to achieve robust joint energy and communication performance.

Abstract

This letter studies the joint energy and signal-to-interference-plus-noise (SINR)-based coverage probability in Unmanned Aerial Vehicle (UAV)-assisted radio frequency (RF)-powered Internet of Things (IoT) networks. The UAVs are spatially distributed in an aerial corridor that is modeled as a one-dimensional (1D) binomial point process (BPP). By accurately capturing the line-of-sight (LoS) probability of a UAV through large-scale fading: i) an exact form expression for the energy coverage probability is derived, and ii) a tight approximation for the overall coverage performance is obtained. Among several key findings, numerical results reveal the optimal number of deployed UAV-BSs that maximizes the joint coverage probability, as well as the optimal length of the UAV corridors when designing such UAV-assisted IoT networks.
Paper Structure (21 sections, 20 equations, 4 figures)

This paper contains 21 sections, 20 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Model
  4. Path Loss and Channel Models
  5. Path loss
  6. Small-scale fading
  7. Large-scale fading
  8. Time-Slotted Architecture
  9. Charging sub-slot
  10. Communication sub-slot
  11. Performance Metrics
  12. Performance Analysis
  13. Distance Distributions
  14. Energy Coverage Probability
  15. Conditional Energy Coverage Probability
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Illustration of the time-slotted architecture of the UAV corridor-assisted RF-powered IoT network.
  • Figure 2: Energy coverage probability versus $\tau$ for different values of $h$.
  • Figure 4: Overall coverage probability versus $N$ for different values of $\tau$. Red stars denote the optimal number of deployed UAV-BSs for maximizing the joint coverage performance. (Markers denote the analytical results.)
  • Figure 5: Heatmap of overall coverage probability versus $R$ and $h$ for different values of $N$. (Markers denote the optimal sets $(R,h)$ for maximizing the overall coverage performance.)