A Novel Neural Point Field Framework for End-to-End Wireless Channel Modeling

Ge Cao, Zhen Peng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a novel neural wireless channel modeling methodology by leveraging a point-cloud-based neural network. During the training phase, the proposed method learns the interaction between wireless transceivers and their environment with wave propagation properties. After training, our work serves as a neural forward model for rapidly predicting the wireless channel for new transceiver locations. As such, it establishes an end-to-end pipeline for network planning and deployment optimization. A noteworthy aspect of our approach lies in the intriguing point-cloud-based neural framework, allowing seamless generalization to large-scale scenes.

Original languageEnglish (US)
Title of host publication2024 International Applied Computational Electromagnetics Society Symposium, ACES 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781733509671
StatePublished - 2024
Event2024 International Applied Computational Electromagnetics Society Symposium, ACES 2024 - Orlando, United States
Duration: May 19 2024May 22 2024

Publication series

Name2024 International Applied Computational Electromagnetics Society Symposium, ACES 2024

Conference

Conference2024 International Applied Computational Electromagnetics Society Symposium, ACES 2024
Country/TerritoryUnited States
CityOrlando
Period5/19/245/22/24

ASJC Scopus subject areas

  • Computational Mathematics
  • Mathematical Physics
  • Instrumentation
  • Radiation

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