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Adaptive Cell Range Expansion in Multi-Band UAV Communication Networks

Xinsong Feng, Ian P. Roberts

TL;DR

A novel approach to modeling mmWave antenna gain in unmanned aerial vehicle (UAV) communication networks operating across low-frequency and millimeter-wave (mmWave) bands is introduced, which allows us to better capture and account for interference in analysis and optimization.

Abstract

This paper leverages stochastic geometry to model, analyze, and optimize multi-band unmanned aerial vehicle (UAV) communication networks operating across low-frequency and millimeter-wave (mmWave) bands. We introduce a novel approach to modeling mmWave antenna gain in such networks, which allows us to better capture and account for interference in our analysis and optimization. We then propose a simple yet effective user-UAV association policy, which strategically biases users towards mmWave UAVs to take advantage of lower interference and wider bandwidths compared to low-frequency UAVs. Under this scheme, we analytically derive the corresponding association probability, coverage probability, and spectral efficiency. We conclude by assessing our proposed association policy through simulation and analysis, demonstrating its effectiveness based on coverage probability and per-user data rates, as well as the alignment between analytical and simulation results.

Adaptive Cell Range Expansion in Multi-Band UAV Communication Networks

TL;DR

A novel approach to modeling mmWave antenna gain in unmanned aerial vehicle (UAV) communication networks operating across low-frequency and millimeter-wave (mmWave) bands is introduced, which allows us to better capture and account for interference in analysis and optimization.

Abstract

This paper leverages stochastic geometry to model, analyze, and optimize multi-band unmanned aerial vehicle (UAV) communication networks operating across low-frequency and millimeter-wave (mmWave) bands. We introduce a novel approach to modeling mmWave antenna gain in such networks, which allows us to better capture and account for interference in our analysis and optimization. We then propose a simple yet effective user-UAV association policy, which strategically biases users towards mmWave UAVs to take advantage of lower interference and wider bandwidths compared to low-frequency UAVs. Under this scheme, we analytically derive the corresponding association probability, coverage probability, and spectral efficiency. We conclude by assessing our proposed association policy through simulation and analysis, demonstrating its effectiveness based on coverage probability and per-user data rates, as well as the alignment between analytical and simulation results.

Paper Structure

This paper contains 11 sections, 16 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Performance Analysis
  4. Serving Distance Distribution
  5. Antenna Gain of Interfering UAVs
  6. Cell Range Expansion Scheme and Association Probability
  7. Coverage Probability
  8. Spectral Efficiency
  9. Laplace Transformation of Interference
  10. Simulation Results
  11. Conclusion

Figures (3)

  • Figure 1: UAVs at height $h$, users on the ground. Users associate with either low-frequency or mmWave UAVs, with interference from non-serving UAVs.
  • Figure 2: An example bias function with $\beta_0=5, \alpha=1$, and $\zeta=1$.
  • Figure 3: Comparison of coverage probability, per-user data rate, and spectral efficiency in multi-band UAV networks under our proposed CRE scheme.