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Wireless technology recognition based on RSSI distribution at sub-nyquist sampling rate for constrained devices

Wei Liu UGent, Merima Kulin UGent, Tarik Kazaz UGent, Adnan Shahid UGent, Ingrid Moerman UGent and Eli De Poorter UGent (2017) SENSORS. 17(9). p.1-23
abstract
Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
keyword
IBCN
journal title
SENSORS
volume
17
issue
9
article number
2081
pages
1 - 23
Web of Science type
Article
Web of Science id
000411484700151
ISSN
1424-8220
DOI
10.3390/s17092081
language
English
UGent publication?
yes
classification
A1
id
8539090
handle
http://hdl.handle.net/1854/LU-8539090
date created
2017-11-27 10:17:30
date last changed
2017-12-04 10:01:31
@article{8539090,
  abstract     = {Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92\% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.},
  articleno    = {2081},
  author       = {Liu, Wei and Kulin, Merima and Kazaz, Tarik and Shahid, Adnan and Moerman, Ingrid and De Poorter, Eli},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keyword      = {IBCN},
  language     = {eng},
  number       = {9},
  pages        = {2081:1--2081:23},
  title        = {Wireless technology recognition based on RSSI distribution at sub-nyquist sampling rate for constrained devices},
  url          = {http://dx.doi.org/10.3390/s17092081},
  volume       = {17},
  year         = {2017},
}

Chicago
Liu, Wei, Merima Kulin, Tarik Kazaz, Adnan Shahid, Ingrid Moerman, and Eli De Poorter. 2017. “Wireless Technology Recognition Based on RSSI Distribution at Sub-nyquist Sampling Rate for Constrained Devices.” Sensors 17 (9): 1–23.
APA
Liu, Wei, Kulin, M., Kazaz, T., Shahid, A., Moerman, I., & De Poorter, E. (2017). Wireless technology recognition based on RSSI distribution at sub-nyquist sampling rate for constrained devices. SENSORS, 17(9), 1–23.
Vancouver
1.
Liu W, Kulin M, Kazaz T, Shahid A, Moerman I, De Poorter E. Wireless technology recognition based on RSSI distribution at sub-nyquist sampling rate for constrained devices. SENSORS. 2017;17(9):1–23.
MLA
Liu, Wei, Merima Kulin, Tarik Kazaz, et al. “Wireless Technology Recognition Based on RSSI Distribution at Sub-nyquist Sampling Rate for Constrained Devices.” SENSORS 17.9 (2017): 1–23. Print.