Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
On Long-Term Statistical Dependences in Channel Gains for Fixed Wireless Links in Factories
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group. (Signals & Systems)ORCID iD: 0000-0002-6689-3257
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
2016 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 7, p. 3078-3091Article in journal (Refereed) Published
Abstract [en]

The reliability and throughput in an industrial wireless sensor network can be improved by incorporating the predictions of channel gains when forming routing tables. Necessary conditions for such predictions to be useful are that statistical dependences exist between the channel gains and that those dependences extend over a long enough time to accomplish a rerouting. In this paper, we have studied such long-term dependences in channel gains for fixed wireless links in three factories. Long-term fading properties were modeled using a switched regime model, and Bayesian change point detection was used to split the channel gain measurements into segments. In this way, we translated the study of long-term dependences in channel gains into the study of dependences between fading distribution parameters describing the segments. We measured the strengths of the dependences using mutual information and found that the dependences exist in a majority of the examined links. The strongest dependence appeared between mean received power in adjacent segments, but we also found significant dependences between segment lengths. In addition to the study of statistical dependences, we present the summaries of the distribution of the fading parameters extracted from the segments, as well as the lengths of these segments.

Place, publisher, year, edition, pages
Uppsala, 2016. Vol. 64, no 7, p. 3078-3091
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-366290DOI: 10.1109/TCOMM.2016.2563431OAI: oai:DiVA.org:uu-366290DiVA, id: diva2:1264089
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-02-28Bibliographically approved
In thesis
1. Change Point Detection with Applications to Wireless Sensor Networks
Open this publication in new window or tab >>Change Point Detection with Applications to Wireless Sensor Networks
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis work we develop a new algorithm for detecting joint changes in statistical behavior of multiple, simultaneously recorded, signals. Such signal analysis is commonly known as multivariate change point (CP) detection (CPD) and is of interest in many scientific and engineering applications.

First we review some of the existing CPD algorithms, where special attention is given to the Bayesian methods. Traditionally, many of the previous works on Bayesian CPD have focused on sampling based methods using Markov Chain Monte Carlo (MCMC). More recent work has shown that it is possible to avoid the computationally expensive MCMC methods by using a technique that is reminiscent of the forward-backward algorithm used for hidden Markov models. We revisit that technique and extend it to a multivariate CPD scenario where subsets of the monitored signals are affected at each CP. The extended algorithm has excellent CPD accuracy, but unfortunately, this fully Bayesian approach quickly becomes intractable when the size of the data set increases.

For large data sets, we propose a two-stage algorithm which, instead of considering all possible combinations of joint CPs as in the fully Bayesian approach, only computes an approximate solution to the most likely combination. In the first stage, the time series are processed in parallel with a univariate CPD algorithm. In the second stage, a dynamic program (DP) is used to search for the combination of joint CPs that best explains the CPs detected by the first stage. The computational efficiency of the second stage is improved by incorporating a pruning condition which reduces the search space of the DP. 

To motivate the algorithm, we apply it to measurements of radio channels in factory environments. The analysis shows that certain subsets of radio channels often experiences simultaneous changes in channel gain.

In addition, a detailed statistical study of the radio channel measurements is presented, including empirical evidence that radio channels exhibit statistical dependencies over long time horizons which implies that it is possible to design predictors of future channel conditions.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 102
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1777
Keywords
Change Point Detection, Signal Processing, Dynamic Programming
National Category
Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-377640 (URN)978-91-513-0580-6 (ISBN)
Public defence
2019-05-10, Room 80101, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 08:30 (English)
Opponent
Supervisors
Available from: 2019-03-21 Created: 2019-02-23 Last updated: 2019-05-07

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Eriksson, MarkusOlofsson, Tomas
By organisation
Signals and Systems Group
In the same journal
IEEE Transactions on Communications
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 30 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf