Children exposure to femtocell in indoor environments estimated by sparse low-rank tensor approximations
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The exposure of an 8-year-old child to a femtocell operating at 2600 MHz, both (child and source) freely located in random positions in an indoor environment, was assessed. In order to develop surrogate models of the exposure, stochastic dosimetry based on sparse low-rank tensor approximation method (sparse LRA) was used. The surrogate models were used for fastly estimating the specific absorption rate (SAR) in all the possible positions of femtocell and child. Results showed that, for all the possible positions in the room, the exposure values were significantly below the International Commission of Non-Ionizing Radiation Protection (ICNIRP) guidelines for general public and that the probability of reaching SAR values higher than 1% of the ICNIRP guidelines value was lower than 0.006. The variation of the distance between femtocell and child influenced greatly the exposure, resulting in quartile coefficient of dispersion values always higher than 48%.
KeywordsStochastic dosimetry RF-EMF exposure 4G LTE femtocell Sparse low-rank tensor approximation
This research is supported by The French National Research Program for Environmental and Occupational Health of Anses (EST-2016-2RF-04) Project AMPERE: Advanced MaPpingof residential ExposuRE to Rf-Emf sources.
- 2.World Health Organization (WHO) (2005) “Base stations and wireless networks: exposures and health consequences.,” in Proceedings of the international workshop on base stations and wireless networks: exposures and health consequencesGoogle Scholar
- 7.Chen HY, Wen SH (2016) Evaluation of E-field distribution and human exposure for a LTE femtocell in an office. Appl Comput Electromagn Soc J 31(4):455–467Google Scholar
- 8.P. Gajšek, B. Kos, and B. Valič (2009) “Energy absorption in adult male and child due to femtocell,” in Annual meeting of the bioelectromagnetics society (BEMS) and European bioelectromagnetics associationGoogle Scholar
- 11.Wiart J (2016) Radio-frequency human exposure assessment: from deterministic to stochastic methods. John Wiley & Sons, ISTEGoogle Scholar
- 21.Sudret B (2007) Uncertainty propagation and sensitivity analysis in mechanical models–contributions to structural reliability and stochastic spectral methods. Habilitationa diriger des recherches, Université Blaise Pascal, Clermont-Ferrand, France, Université Blaise Pascal, Clermont-Ferrand, FranceGoogle Scholar
- 25.International Commission on Non-Ionizing Radiation Protection (1998) ICNIRP guidelines for limiting exposure to time-varying electric, magnetic and electromagnetic fields. Health Phys 74:494–522Google Scholar
- 26.Gosselin M-C, Neufeld E, Moser H, Huber E, Farcito S, Gerber L, Jedensjö M, Hilber I, Di Gennaro F, Lloyd B, Cherubini E, Szczerba D, Kainz W, Kuster N (2014) Development of a new generation of high-resolution anatomical models for medical device evaluation: the virtual population 3.0. Phys Med Biol 59(18):5287–5303CrossRefGoogle Scholar
- 27.Blatman G (2009) Adaptive sparse polynomial chaos expansions foruncertainty propagation and sensitivity analysis. In: Université Blaise Pascal, Clermont-FerrandGoogle Scholar
- 30.Pinto Y and Wiart J (2017) Statistical analysis and surrogate modeling of indoor exposure induced from a WLAN Source, no. 2, pp. 806–810Google Scholar
- 32.Konakli K and Sudret B (2015), “Uncertainty quantification in high-dimensional spaces with low rank tensor approximations,” 1st Int Conf Uncertain Quantif Comput Sci Eng, no May, pp. 1–12Google Scholar