Spatially Consistent Air-to-Ground Channel Modeling via Generative Neural Networks

Amedeo Giuliani, Rasoul Nikbakht, Giovanni Geraci, Seongjoon Kang, Angel Lozano, Sundeep Rangan

Research output: Contribution to journalArticlepeer-review

Abstract

This letter proposes a generative neural network architecture for spatially consistent air-to-ground channel modeling. The approach considers the trajectories of uncrewed aerial vehicles along typical urban paths, capturing spatial dependencies within received signal strength (RSS) sequences from multiple cellular base stations (gNBs). Through the incorporation of conditioning data, the model accurately discriminates between gNBs and drives the correlation matrix distance between real and generated sequences to minimal values. This enables evaluating performance and mobility management metrics with spatially (and by extension temporally) consistent RSS values, rather than independent snapshots. For some tasks underpinned by these metrics, say handovers, consistency is essential.

Original languageEnglish (US)
Pages (from-to)1158-1162
Number of pages5
JournalIEEE Wireless Communications Letters
Volume13
Issue number4
DOIs
StatePublished - Apr 1 2024

Keywords

  • 5G
  • Cellular network
  • channel model
  • drone
  • generative neural network
  • ray tracing
  • uncrewed aerial vehicle (UAV)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

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