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Optimal SSB Beam Planning and UAV Cell Selection for 5G Connectivity on Aerial Highways

Matteo Bernabe, David Lopez-Perez, Nicola Piovesan, Giovanni Geraci, David Gesbert

TL;DR

This paper proposes a solution to 5G massive multiple-input multiple-output (mMIMO) connectivity for unmanned aerial vehicles (UAVs) on aerial highways through strategic cell association based on the suitable selection of serving cells based on a new metric which differs from the classical terrestrial approaches based on maximum RSRP.

Abstract

In this article, we introduce a method to optimize 5G massive multiple-input multiple-output (mMIMO) connectivity for unmanned aerial vehicles (UAVs) on aerial highways through strategic cell association. UAVs operating in 3D space encounter distinct channel conditions compared to traditional ground user equipment (gUE); under the typical line of sight (LoS) condition, UAVs perceive strong reference signal received power (RSRP) from multiple cells within the network, resulting in a large set of suitable serving cell candidates and in low signal-to-interference-plus-noise ratio (SINR) due to high interference levels. Additionally, a downside of aerial highways is to pack possibly many UAVs along a small portion of space which, when taking into account typical LoS propagation conditions, results in high channel correlation and severely limits spatial multiplexing capabilities. In this paper, we propose a solution to both problems based on the suitable selection of serving cells based on a new metric which differs from the classical terrestrial approaches based on maximum RSRP. We then introduce an algorithm for optimal planning of synchronization signal block (SSB) beams for this set of cells, ensuring maximum coverage and effective management of UAVs cell associations. Simulation results demonstrate that our approach significantly improves the rates of UAVs on aerial highways, up to four times in achievable data rates, without impacting ground user performance.

Optimal SSB Beam Planning and UAV Cell Selection for 5G Connectivity on Aerial Highways

TL;DR

This paper proposes a solution to 5G massive multiple-input multiple-output (mMIMO) connectivity for unmanned aerial vehicles (UAVs) on aerial highways through strategic cell association based on the suitable selection of serving cells based on a new metric which differs from the classical terrestrial approaches based on maximum RSRP.

Abstract

In this article, we introduce a method to optimize 5G massive multiple-input multiple-output (mMIMO) connectivity for unmanned aerial vehicles (UAVs) on aerial highways through strategic cell association. UAVs operating in 3D space encounter distinct channel conditions compared to traditional ground user equipment (gUE); under the typical line of sight (LoS) condition, UAVs perceive strong reference signal received power (RSRP) from multiple cells within the network, resulting in a large set of suitable serving cell candidates and in low signal-to-interference-plus-noise ratio (SINR) due to high interference levels. Additionally, a downside of aerial highways is to pack possibly many UAVs along a small portion of space which, when taking into account typical LoS propagation conditions, results in high channel correlation and severely limits spatial multiplexing capabilities. In this paper, we propose a solution to both problems based on the suitable selection of serving cells based on a new metric which differs from the classical terrestrial approaches based on maximum RSRP. We then introduce an algorithm for optimal planning of synchronization signal block (SSB) beams for this set of cells, ensuring maximum coverage and effective management of UAVs cell associations. Simulation results demonstrate that our approach significantly improves the rates of UAVs on aerial highways, up to four times in achievable data rates, without impacting ground user performance.
Paper Structure (10 sections, 15 equations, 2 figures, 1 algorithm)

This paper contains 10 sections, 15 equations, 2 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Cell Association and Precoding
  5. SINR and Achievable Data Rate
  6. Cell Selection and SSB Beam Planning
  7. Aerial Highway Segment-to-Cell Association Metric
  8. SSB Beam and Power Selection Algorithm
  9. Simulation Results
  10. Conclusion

Figures (2)

  • Figure 1: SINR and achievable data rate distribution for 12 UAV on an AH positioned across cell edges at a height of 100 m.
  • Figure 2: 5%-tile achievable data rate for different traffic density and $d_{\rm IUD}$.