Multimodal Learning for Integrated Sensing and Communication Networks

Xiaonan Liu, Tharmalingam Ratnarajah, Mathini Sellathurai, Yonina C. Eldar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Integrated sensing and communication (ISAC) is a promising technique for beyond 5G networks. In ISAC networks, the sensed environmental data may be multimodal data, which may result in high computation and communication latency due to the large size of data modalities and limited computation capability of mobile devices. To solve the problem, in this paper, we propose multimodal learning in ISAC networks. Simulation results show that the proposed multimodal learning design significantly outperforms several benchmarks without considering multimodal data sensing and communication.

Original languageEnglish
Title of host publication32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
Pages1177-1181
Number of pages5
ISBN (Electronic)9789464593617
StatePublished - 2024
Event32nd European Signal Processing Conference, EUSIPCO 2024 - Lyon, France
Duration: 26 Aug 202430 Aug 2024

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference32nd European Signal Processing Conference, EUSIPCO 2024
Country/TerritoryFrance
CityLyon
Period26/08/2430/08/24

Keywords

  • Integrated sensing and communication
  • multimodal learning

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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