ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning

Shamik Sarkar*, Dongning Guo, Danijela Cabric

*Corresponding author for this work

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

Abstract

Spatia1 prediction of the radio propagation environment (henceforth 'radio environment' for brevity) of a transmitter can assist and improve various aspects of wireless networks. The majority of research in this domain can be categorized as 'reactive' spatial prediction, where the predictions are made based on a small set of measurements from an active transmitter whose radio environment is to be predicted. Emerging spectrum-sharing paradigms would benefit from 'proactive' spatial prediction of the radio environment, where the spatial predictions must be done for a transmitter for which no measurement has been collected. This paper proposes a novel, supervised deep learning-based framework, ProSpire, that enables spectrum sharing by leveraging the idea of proactive spatial prediction. We carefully address several challenges in ProSpire, such as designing a framework that conveniently collects training data for learning, performing the predictions in a fast manner, enabling operations without an area map, and ensuring that the predictions do not lead to undesired interference. ProSpire relies on the crowdsourcing of transmitters and receivers during their normal operations to address some of the aforementioned challenges. The core component of ProSpire is a deep learning-based image-to-image translation method, which we call RSSu-net. We generate several diverse datasets using ray tracing software and numerically evaluate ProSpire. Our evaluations show that RSSu-net performs reasonably well in terms of signal strength prediction, $\approx$ 5dB mean absolute error, which is comparable to the average error of other relevant methods. Importantly, due to the merits of RSSu-net, ProSpire creates proactive boundaries around transmitters such that they can be activated with $\approx$ 97% probability of not causing interference. In this regard, the performance of RSSu-net is 19% better than that of other comparable methods.

Original languageEnglish (US)
Title of host publication2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023
PublisherIEEE Computer Society
Pages177-185
Number of pages9
ISBN (Electronic)9798350300529
DOIs
StatePublished - 2023
Event20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023 - Madrid, Spain
Duration: Sep 11 2023Sep 14 2023

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
Volume2023-September
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023
Country/TerritorySpain
CityMadrid
Period9/11/239/14/23

Keywords

  • Deep Learning
  • Spatial Prediction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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