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Blind Source Separation for Wireless Networks: A Tool for Topology Sensing

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Cognitive Radio-Oriented Wireless Networks (CrownCom 2020)

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Abstract

In this work, a tool for topology sensing of a non-collaborative wireless network using power profiles captured by radio-frequency (RF) sensors is proposed. Assuming that the features of the network (i.e., the number of nodes, medium access control (MAC) and routing protocols) are unknown and that the sensors observe signal mixtures because of the wireless medium, blind source separation (BSS) is used to separate the traffic profiles. Successively, the topology of the network is inferred by detecting causal relationships between the separated streams. According to the numerical results, the proposed tool senses the topology with promising accuracy when operating in mild shadowing conditions, even with a relatively low number of radio-frequency (RF) sensors.

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Notes

  1. 1.

    The BSS requires a coarse estimate of the network nodes position, therefore it can be performed by the sensor network before the tasks summarized in Fig. 1 [14, 15].

  2. 2.

    Usually, the shadowing parameter is expressed as the standard deviation of the received power in deciBel, i.e., \({\sigma _\text {S}}(\text {dB})=\frac{10}{\ln {10}}\sigma _\text {S}\).

  3. 3.

    The noise term in the ED is a central chi-squared r.v. with a number of degrees of freedom, \(N_\text {DOF}\), proportional to the time-bandwidth product. When \(N_\text {DOF}\) is large, the noise term can be considered constant. As detailed in Sect. 5 we consider a system with bandwidth \(W=20\,\)MHz (i.e., WiFi channel) and an integration time \(T_\text {i}=10\,\upmu \)s, hence \(N_\text {DOF}=2WT_{\text {i}}=400\).

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Authors and Affiliations

  1. Alma Mater Studiorum – University of Bologna, Via dell’Università 50, Cesena, Italy

    Enrico Testi, Elia Favarelli & Andrea Giorgetti

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  1. Enrico Testi
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  2. Elia Favarelli
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  3. Andrea Giorgetti
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Correspondence to Enrico Testi .

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Editors and Affiliations

  1. Department of Mobile Systems and Analytics (MOSAIC), Simula Metropolitan Center for Digital Engineering (SimulaMet), Oslo, Norway

    Giuseppe Caso

  2. DIET Department, Sapienza University of Rome, Rome, Italy

    Luca De Nardis

  3. Faculty of Electrical Engineering and Information Technology, Ss. Cyril and Methodius University, Skopje, North Macedonia

    Liljana Gavrilovska

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Testi, E., Favarelli, E., Giorgetti, A. (2021). Blind Source Separation for Wireless Networks: A Tool for Topology Sensing. In: Caso, G., De Nardis, L., Gavrilovska, L. (eds) Cognitive Radio-Oriented Wireless Networks. CrownCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 374. Springer, Cham. https://doi.org/10.1007/978-3-030-73423-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-73423-7_3

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