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999 _c5335
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003 FR-PaOEC
005 20211006062618.0
006 a o d i
007 cr || |||m|n||
008 190601s2018 ||| o i|0| 0 eng d
035 _a(FR-PaOEC)
040 _aFR-PaOEC
_cES-MaONT
100 1 _aSurajudeen-Baikinde, Namzat T.
_93383
245 1 0 _aOn adaptive neuro-fuzzy model for path loss prediction in the VHF band /
_cNamzat T. Surajudeen-Baikinde ... [el al.]
260 _aGeneva :
_bInternational Telecommunication Union,
_c2018.
300 _a9 p.
336 _atexto (visual)
_2isbdcontent
337 _aelectrónico
_2isbdmedia
338 _arecurso en línea
_2rdacarrier
520 3 _aPath loss prediction models are essential in the planning of wireless systems, particularly in built-up environments. However, the efficacies of the empirical models depend on the local ambient characteristics of the propagation environments. This paper introduces artificial intelligence in path loss prediction in the VHF band by proposing an adaptive neuro-fuzzy (NF) model. The model uses five-layer optimized NF network based on back propagation gradient descent algorithm and least square errors estimate. Electromagnetic field strengths from the transmitter of the NTA Ilorin, which operates at a frequency of 203.25 MHz, were measured along four routes. The prediction results of the proposed model were compared to those obtained via the widely used empirical models. The performances of the models were evaluated using the Root Mean Square Error (RMSE), Spread Corrected RMSE (SC-RMSE), Mean Error (ME), and Standard Deviation Error (SDE), relative to the measured data. Across all the routes covered in this study, the proposed NF model produced the lowest RMSE and ME, while the SDE and the SC-RMSE were dependent on the terrain and clutter covers of the routes. Thus, the efficacy of the adaptive NF model was validated and can be used for effective coverage and interference planning.
650 0 _aTecnologías habilitadoras digitales
_918
653 4 _aScience and Technology
700 1 _aFaruk, Nasir
_93384
700 1 _aSalman, Muhammed
_93385
700 1 _aPopoola, Segun
_93386
700 1 _aOloyede, Abdulkarim
_93387
700 1 _aOlawoyin, Lukman A.
_93388
773 0 _04843
_tITU Journal: ICT Discoveries
_gVol. 2018, no. 1, p. 67-75
_q2018:1<67
_x2616-8375
856 4 0 _aitu-ilibrary.org
_uhttps://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I1-2018-8-PDF-E.pdf
_yAcceso al documento
_x0
_qpdf
942 _cART
_2udc