Scalable Multi-Modal Learning for Cross-Link Channel Prediction in Massive IoT Networks

Kun Woo Cho, Marco Cominelli, Francesco Gringoli, Joerg Widmer, Kyle Jamieson

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

Abstract

Tomorrow's massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP's design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.

Original languageEnglish (US)
Title of host publicationMobiHoc 2023 - Proceedings of the 2023 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
PublisherAssociation for Computing Machinery
Pages221-229
Number of pages9
ISBN (Electronic)9781450399265
DOIs
StatePublished - Oct 23 2023
Event2023 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2023 - Washington, United States
Duration: Oct 23 2023Oct 26 2023

Publication series

NameProceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)

Conference

Conference2023 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2023
Country/TerritoryUnited States
CityWashington
Period10/23/2310/26/23

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Software

Keywords

  • channel prediction
  • massive-IoT networks
  • multi-modal learning

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