Channel Prediction with Location Uncertainty for Ad Hoc Networks

Markus Frohle*, Themistoklis Charalambous, Ido Nevat, Henk Wymeersch

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)


Multiagent systems (MAS) rely on positioning technologies to determine their physical location, and on wireless communication technologies to exchange information. Both positioning and communication are affected by uncertainties, which should be accounted for. This paper considers an agent placement problem to optimize end-to-end communication quality in a MAS in the presence of uncertainties. Using Gaussian processes, operating on the input space of location distributions, we are able to model, learn, and predict the wireless channel. Predictions, in the form of distributions, are fed into the communication optimization problems. This approach inherently avoids regions of the workspace with high position uncertainty and leads to better average communication performance. We illustrate the benefits of our approach via extensive simulations, based on real wireless channel measurements. Finally, we demonstrate the improved channel learning and prediction performance, as well as the increased robustness in agent placement.

Original languageEnglish
Pages (from-to)349-361
Number of pages13
JournalIEEE Transactions on Signal and Information Processing over Networks
Issue number2
Publication statusPublished - 1 Jun 2018
MoE publication typeA1 Journal article-refereed


  • Ad-hoc networks
  • bit error rate
  • channel prediction
  • Gaussian processes
  • multi-agent systems

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