Propagation-Loss Prediction Using Ray Tracing with a Random-Phase Technique

Satoshi TAKAHASHI  Yoshihide YAMADA  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E81-A   No.7   pp.1445-1451
Publication Date: 1998/07/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Fundamentals of Multi-dimensional Mobile Information Network)
Category: 
Keyword: 
radio propagation,  propagation-loss prediction,  ray tracing,  random-phase technique,  probability-based cell design,  

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Summary: 
For mobile telecommunication systems, it is important to accurately predict the propagation-path loss in terms of the estimation of the radiowave coverage area. The propagation-path loss has been estimated in a median obtained spatially from many received amplitudes (envelopes) within a region of several tens times as long as the wavelength, rather than in the envelopes themselves. Although ray tracing can obtain the envelopes and their median that reflect the site-dependent characteristics, the estimated median sometimes does not agree with the measured one. Therefore, the accuracy improvement has been expected. In this paper, an accuracy improvement is achieved by substituting a median with random phases for the median obtained spatially from many envelopes. The characteristic function method is used to obtain the cumulative distribution function and the median analytically where the phases are randomized. In a multipath environment, the phase-estimation error accompanying the location error of the ray tracing input influences the spatially obtained median. The phase-randomizing operation reduces the effects of the phase-estimation error on the median prediction. According to our estimation, improvements in accuracy of 4. 9 dB for the maximum prediction error and 2. 9 dB for the RMS prediction error were achieved. In addition, a probability-based cell-design method that takes the radiowave arrival probability and the interference probability into consideration is possible by using the percentiles obtained by the characteristic function method and the cell-design examples are shown in this paper.