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Evaluation of 3D Terrestrial and Aerial Spectrum Sharing with Massive MIMO Systems

Achiel Colpaert, Zhuangzhuang Cui, Sofie Pollin

TL;DR

This study addresses 3D spectrum sharing for aerial and terrestrial users using a 64-antenna massive MIMO base station. It combines real CSI measurements from a drone-mounted UE with SUS and zero-forcing scheduling to evaluate spectral efficiency and determine optimal aerial-terrestrial user mixes. The key contributions are the CSI measurement campaign, demonstration of 3D sharing with advanced combining, and a detailed analysis of how user scheduling affects SE under different layer allocations. The findings show that semi-orthogonal scheduling extends SE and that the optimal aerial-terrestrial mix depends on channel characteristics and link geometry, informing design choices for future non-terrestrial and 3D networks.

Abstract

Connecting aerial and terrestrial users with a single base station (BS) is increasingly challenging due to the rising number of aerial users like unmanned aerial vehicles (UAVs). Traditional BSs, designed with down-tilted beams, focus mainly on ground users, but massive MIMO (mMIMO) systems can significantly enhance coverage in low-altitude airspace. This paper analyzes how a mMIMO BS serves both aerial and terrestrial users in a 3D spectrum-sharing scheme. Using Semi-orthogonal User Selection (SUS) and random scheduling, we assess the spectral efficiency and performance limits of these systems. Results reveal that mMIMO effectively supports more terrestrial users, influenced by channel characteristics and user scheduling strategies, providing key insights for future 3D aerial-terrestrial networks.

Evaluation of 3D Terrestrial and Aerial Spectrum Sharing with Massive MIMO Systems

TL;DR

This study addresses 3D spectrum sharing for aerial and terrestrial users using a 64-antenna massive MIMO base station. It combines real CSI measurements from a drone-mounted UE with SUS and zero-forcing scheduling to evaluate spectral efficiency and determine optimal aerial-terrestrial user mixes. The key contributions are the CSI measurement campaign, demonstration of 3D sharing with advanced combining, and a detailed analysis of how user scheduling affects SE under different layer allocations. The findings show that semi-orthogonal scheduling extends SE and that the optimal aerial-terrestrial mix depends on channel characteristics and link geometry, informing design choices for future non-terrestrial and 3D networks.

Abstract

Connecting aerial and terrestrial users with a single base station (BS) is increasingly challenging due to the rising number of aerial users like unmanned aerial vehicles (UAVs). Traditional BSs, designed with down-tilted beams, focus mainly on ground users, but massive MIMO (mMIMO) systems can significantly enhance coverage in low-altitude airspace. This paper analyzes how a mMIMO BS serves both aerial and terrestrial users in a 3D spectrum-sharing scheme. Using Semi-orthogonal User Selection (SUS) and random scheduling, we assess the spectral efficiency and performance limits of these systems. Results reveal that mMIMO effectively supports more terrestrial users, influenced by channel characteristics and user scheduling strategies, providing key insights for future 3D aerial-terrestrial networks.

Paper Structure

This paper contains 17 sections, 4 equations, 4 figures.

Table of Contents

  1. Introduction
  2. CSI Measurement
  3. Measurement System
  4. Setup
  5. Channel Capture
  6. Measurement Scenario
  7. Scheduling Algorithms in mMIMO with Zero-Forcing
  8. Random User Selection
  9. Semi-orgthogonal User Selection
  10. Zero Forcing
  11. Evaluation Metrics
  12. SINR
  13. Spectral Efficiency
  14. Results
  15. SE performance with random users in both layers
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Measurement scenario marked by the location of BS and two trajectories that are indicated by normalized receive power.
  • Figure 2: SE for different numbers of total users in the system. Figure (a) shows the summed SE and figure (b) shows the averaged individual SE for the number of users ranging from 0 to 64 where we indicate three regions with different behavior of SE.
  • Figure 3: Summed SE as a function of numbers of ground and aerial users when using SUS and ZF combining.
  • Figure 4: Individual SE as a function of numbers of ground and aerial users.