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New upper and lower bounds line of sight path loss model for mobile propagation in buildings
(2008-03-03)
Phaiboon, Supachai
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Phokharatkul, Pisit
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Somkuarnpanit, Suripon
This paper proposes a method to predict line-of-sight (LOS) path loss in buildings. We performed measurements in two different types of buildings at a frequency of 1.8 GHz and propose a new path loss model with its upper and lower bounds. The upper and lower bounds depend on max and min values of sampled path loss data. This makes our model limit path loss within the boundary lines. The model includes time-variant effects from the object movement from people in the building and cars in parking areas. These influence reasonably on wave propagation. The results have shown that the proposed model will be useful for the design of the indoor wireless communication systems. © 2007 Elsevier GmbH. All rights reserved.
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Mobile path loss prediction with image segmentation and classification
(2007-10-01)
Phaiboon, Supachai
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Phokharatkul, Pisit
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Kittithamavongs, Piti
This paper presents an intelligent radio wave propagation prediction model by using the 2-dimension aerial image which is taken from the actual area. An suburban area is used as examples. The prediction procedure is done in three steps. First, the image segmentation is employed to divide the area image into subgroups by using Maximum Likelihood algorithm. The second step uses the subgroup images from step 1 to determine the parameters for the fuzzy model that we use to classify the propagation areas. The final step is to plot the path loss contour on the image so the cellular cell site can be chosen. The research results show that the proposed segmentation provides an accuracy of 80-90% compared with the actual area. Therefore, cell site selection can be designed on the 2-dimension aerial map with the error less than 8 dB.
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Muti-layer fuzzy logic sets for mobile path loss in forests
(2007-08-08)
Phaiboon, Supachai
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Phokharatkul, Pisit
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Somkuarnpanit, Suripon
Mobile path loss prediction in forests using Multi-Layer fuzzy logic system (MLFS) is presented in this paper. The MLFS consists of a tree density decision layer which is a supervisory layer in order to select the next layers using fuzzy decision. The sub-predictions use a set of rule base that provide path loss prediction in each case of an environment. These crisp inputs are classified by the fuzzifier to fuzzy sets and then inferenced using fuzzy linguistic rule base into multi - output path loss slopes via de-fuzzifier. For this study, we classified the terrains into high-, medium-, low- density and grass area and used the simple linguistic rules for prediction of the path loss slopes. We performed measurements in different forest densities at a frequency of 1.8 GHz with base station antenna height in a range of 3, 4, and 5 m above ground while the receiving antenna height was fixed at 1.8 m above ground. The results have shown that fuzzy logic approach provides more accurate prediction of path loss slopes than that of conventional empirical mathematic models. The proposed models will be useful for the local wireless network and micro-cell design of mobile communication systems in forests. ©2006 IEEE.
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Fading analysis of vegetation density effects on path loss for 1.8 GHZ cellular mobile with low base station antenna height
(2006-12-01)
Phaiboon, Supachai
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Pitithammawong, Kitti
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Chulajata, Tatcha
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Somkuarnpanit, Suripon
This paper presents studies of propagation characteristics in a suburban forest based on a measurement campaign at a frequency of 1.8 GHz. We investigated how tree density affected the fast fading characteristic depending on tree density and base station antenna height in a range of 3, 4, and 5 m above ground while the receiving antenna height was fixed at 1.8 m above ground. Three categories were studied: high density area, low density areas and grass area. Measurement of the receiver signal over 120 s intervals for a stationary mobile were used to obtain the distribution functions for the fading. We found that the distributions followed a Rician distribution, whose K-factor depended on tree density and transmitter height.
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Mobile path loss prediction model for forest areas using MIMO fuzzy logic system
(2006-09-01)
Phaiboon, Supachai
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Phokharatkul, Pisit
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Kittitummawong, Piti
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Somkuarnpanit, Suripon
This paper proposes a method to predic mobile path loss in forests using MIMO fuzzy logic system. The multi-input was classified into seven input parameters defined as, X1 is number of trees/m<sup>2</sup> and X2 to X7 are tree structure parameters. These crisp inputs are classified by fuzzifier to fuzzy sets and then inferenced using fuzzy linguistic rule base into multi - output path loss slopes via de-fuzzifier. For this study, we classified the terrains into high-, medium-, low- density and grass area and used the simple linguistic rules for prediction the path loss slopes. We performed measurements in different forest densities at a frequency of 1.8 GHz with base station antenna height in a range of 3, 4, and 5 m above ground while the receiving antenna height was fixed at 1.8 m above ground. The results have shown that fuzzy logic approach provides more accurate prediction of path loss slopes than that of conventional empirical mathematic model. The proposed models will be useful for the local wireless network and micro-cell design of mobile communication systems in forests.
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Microwave line-of-sight path loss prediction on urban street by fuzzy logic model
(2005-12-01)
Phaiboon, Supachai
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Phokharatkul, Pisit
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Somkuarnpanit, Suripon
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Boonpiyathud, Sitchai
This paper proposes a method to model the path loss characteristics on urban streets in the microwave band. We applied the concept of fuzzy logic to predict path losses. The input fuzzy sets were classified into five sets, namely 1) Distance between transmitter and receiver, 2) Frequency, 3) Time of day, 4) Transmitting antenna height and 5) Receiving antenna height. These inputs are then inferenced into output path loss via linguistic rules which were trained by measurement. To check the proposed model, we compared the fuzzy prediction with the same and an another measurement. The results show that the fuzzy logic models provided a better prediction. © 2005 IEEE.
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Breakpoint distance los path loss model for indoor communication using anfis
(2005-12-01)
Phaiboon, Supachai
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Phokharatkul, Pisit
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Somkurnpanich, Suripon
Breakpoint distance LOS model for indoor wireless communication is presented in this paper. The model is based on the determination of the breakpoint distance and % of wall area between the transmitter and the receiver. The propagation path losses are predicted with adaptive neuro - fuzzy inference systems (ANFIS), trained with measurements at the frequency of 1.8 GHz. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical models are achieved.
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Upper-and lower-bound path-loss modeling for indoor line-of-sight environments
(2005-01-01)
Phaiboon, Supachai
;
Phokharatkul, Pisit
;
Somkuarnpanit, Suripon
;
Boonpiyathud, Sitchai
This paper proposes a method to predict line-of-sight (LOS) path loss in buildings. We performed measurements in two different type of buildings at a frequency of 1.8 GHz and propose upper-and-lower bounds path loss models which depend on max and min values of sample path loss data. This makes our models limit path loss within the boundary lines. The models include time-variant effects such as people moving and cars in parking areas with their influence on wave propagation that is very high. The results have shown that the proposed models will be useful for the system and cell design of indoor wireless communication systems. © 2005 IEEE.
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Mobile propagation path loss models for suburban areas using fuzzy logic approximation
(2004-12-01)
Phaiboon, Supachai
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Phokharutkul, Pisit
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Somkurnpanich, Suripon
This paper proposes method to predict path loss in cellular mobile communication systems using fuzzy logic. The propagation medium is classified into two propagation environment density defined as a input fuzzy set X1 = Suburban house density and X2 = Tree density. These crisp inputs are classified by fuzzifier to fuzzy sets and then inferenced using fuzzy linguistic rule base into path loss prediction output via de-fuzzifier. For this study, we classified the terrains into free space, flat area terrain, light tree terrain, heavy tree terrain, and village terrain and used the simple linguistic rules for prediction the path loss slope. The results have shown that fuzzy logic approach provides more accurate prediction of strength loss than that of conventional Hata's model. © 2004IEEE.
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An optical fiber feeder system and performance for cellualar microcell 800MHz CDMA systems
(2001-12-01)
Phaiboon, Supachai
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Somkuarnpanit, Suripon
This paper proposes an optical fiber feeder system for use in cellular microcell 800MHz CDMA systems, as well as its performance. The RF signal of the CDMA system is converted into the optical signal, which can be delivered in the optical fiber. We analyze the system performance in terms of CNR (carrier to noise ratio) and SER (symbol error rate) using theoretical modeling as well as numerically calculation of the CDMA system compared with the intensity modulation. It is found that the optical feeder system for the CDMA system has got higher CNR than that of the system with intensity modulation, especially at low power level e.g. CNR more than 80dB for received optical power of -40dBm. This would provide a consequence of relatively low error rate in signal detection.

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