Efficient Pilot Allocation for URLLC Traffic in 5G Industrial IoT Networks

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

In this paper we address the problem of resource allocation for alarm traffic in industrial Internet of Things networks using massive MIMO. We formulate the general problem of how to allocate pilot signals to alarm traffic such that delivery is guaranteed, while also minimising the number of pilots reserved for alarms, thus maximising the channel resources available for other traffic, such as industrial control traffic. We present an algorithm that fulfils these requirements, and evaluate its performance both analytically and through a simulation study. In the average case our algorithm can deliver alarms within two time slots (of duration equal to the 5G transmission time interval) using fewer than two pilots per slot, and even in the worst case uses at most eight pilots in any given slot, with delivery guaranteed in less than four slots.

Details

Authors
Organisations
External organisations
  • Warsaw University of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Communication Systems
Original languageSwedish
Title of host publicationInternational Workshop on Resilient Networks Design and Modelling (RNDM)
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Number of pages7
Publication statusAccepted/In press - 2019
Publication categoryResearch
Peer-reviewedYes
EventInternational Workshop on Resilient Networks Design and Modelling, RNDM 2019 - Nicosia, Cyprus
Duration: 2019 Oct 142019 Oct 16

Workshop

WorkshopInternational Workshop on Resilient Networks Design and Modelling, RNDM 2019
CountryCyprus
CityNicosia
Period2019/10/142019/10/16

Related projects

Maria Kihl & Emma Fitzgerald

Swedish Government Agency for Innovation Systems (Vinnova)

2018/08/272020/12/31

Project: Other

Christian Gehrmann, Maria Kihl, Martin Hell, Emma Fitzgerald & Mohsen Toorani

Swedish Foundation for Strategic Research, SSF

2018/04/012023/03/31

Project: Other

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