Detail publikace

Time-Dependent Propagation Analysis and Modeling of LPWAN Technologies

ŠTŮSEK, M. MOLTCHANOV, D. MAŠEK, P. ANDREEV, S. KOUCHERYAVY, Y. HOŠEK, J.

Originální název

Time-Dependent Propagation Analysis and Modeling of LPWAN Technologies

Anglický název

Time-Dependent Propagation Analysis and Modeling of LPWAN Technologies

Jazyk

en

Originální abstrakt

The modern low-power wide area network (LPWAN) technologies have been introduced as connectivity enablers with low complexity, extended communication range, and excellent signal penetration. On the other hand, they suffer from a substantial delay and low packet-delivery guarantees. As a result, numerous novel applications entering the market suffer from insufficient performance. To mitigate this, further optimization and adaptation of the LPWAN technologies to the needs of these new applications requires an in-depth understanding of the propagation environment dynamics. Motivated by that, in this paper, we thoroughly investigate time-dependent statistical characteristics of the reference signal receive power (RSRP) dynamics of Narrowband IoT (NB-IoT) technology. We demonstrate that even for a stationary user equipment (UE), RSRP is subject to drastic variations that are characterized by exponentially decaying autocorrelation function (ACF). We then demonstrate that first- and second-order statistical properties of the RSRP dynamics can be closely captured using the doubly-stochastic Markov model that retains the tractability of the conventional Markov models. The reported models are expected to serve as a building block for analytical and simulation-based system-level studies and optimization of LPWAN technologies.

Anglický abstrakt

The modern low-power wide area network (LPWAN) technologies have been introduced as connectivity enablers with low complexity, extended communication range, and excellent signal penetration. On the other hand, they suffer from a substantial delay and low packet-delivery guarantees. As a result, numerous novel applications entering the market suffer from insufficient performance. To mitigate this, further optimization and adaptation of the LPWAN technologies to the needs of these new applications requires an in-depth understanding of the propagation environment dynamics. Motivated by that, in this paper, we thoroughly investigate time-dependent statistical characteristics of the reference signal receive power (RSRP) dynamics of Narrowband IoT (NB-IoT) technology. We demonstrate that even for a stationary user equipment (UE), RSRP is subject to drastic variations that are characterized by exponentially decaying autocorrelation function (ACF). We then demonstrate that first- and second-order statistical properties of the RSRP dynamics can be closely captured using the doubly-stochastic Markov model that retains the tractability of the conventional Markov models. The reported models are expected to serve as a building block for analytical and simulation-based system-level studies and optimization of LPWAN technologies.

Dokumenty

BibTex


@inproceedings{BUT166062,
  author="Martin {Štůsek} and Pavel {Mašek} and Jiří {Hošek}",
  title="Time-Dependent Propagation Analysis and Modeling of LPWAN Technologies",
  annote="The modern low-power wide area network (LPWAN) technologies have been introduced as connectivity enablers with low complexity, extended communication range, and excellent signal penetration. On the other hand, they suffer from a substantial delay and low packet-delivery guarantees. As a result, numerous novel applications entering the market suffer from insufficient performance. To mitigate this, further optimization and adaptation of the LPWAN technologies to the needs of these new applications requires an in-depth understanding of the propagation environment dynamics. Motivated by that, in this paper, we thoroughly investigate time-dependent statistical characteristics of the reference signal receive power (RSRP) dynamics of Narrowband IoT (NB-IoT) technology. We demonstrate that even for a stationary user equipment (UE), RSRP is subject to drastic variations that are characterized by exponentially decaying autocorrelation function (ACF). We then demonstrate that first- and second-order statistical properties of the RSRP dynamics can be closely captured using the doubly-stochastic Markov model that retains the tractability of the conventional Markov models. The reported models are expected to serve as a building block for analytical and simulation-based system-level studies and optimization of LPWAN technologies.",
  booktitle="2020 IEEE Global Communications Conference",
  chapter="166062",
  doi="10.1109/GCWkshps50303.2020.9367525",
  howpublished="online",
  year="2020",
  month="december",
  pages="1--6",
  type="conference paper"
}