• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3194-3210
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• 2022

Wall obstruction is the main factor leading to the non-line of sight (NLoS) error of indoor localization based on received signal strength indicator (RSSI). Modeling and correcting the path loss of the signals through the wall will improve the accuracy of RSSI localization. Based on electromagnetic wave propagation theory, the reflection and transmission process of wireless signals propagation through the wall is analyzed. The path loss of signals through wall is deduced based on power loss and RSSI definition, and the theoretical model of path loss of signals through wall is proposed. In view of electromagnetic characteristic parameters of the theoretical model usually cannot be accurately obtained, the statistical model of NLoS error caused by the signals through the wall is presented based on the log-distance path loss model to solve the parameters. Combining the statistical model and theoretical model, a hybrid model of path loss of signals through wall is proposed. Based on the empirical values of electromagnetic characteristic parameters of the concrete wall, the effect of each electromagnetic characteristic parameters on path loss is analyzed, and the theoretical model of regional path loss of signals through the wall is established. The statistical model and hybrid model of regional path loss of signals through wall are established by RSSI observation experiments, respectively. The hybrid model can solve the problem of path loss when the material of wall is unknown. The results show that the hybrid model can better express the actual trend of the regional path loss and maintain the pass loss continuity of adjacent areas. The validity of the hybrid model is verified by inverse computation of the RSSI of the extended region, and the calculated RSSI is basically consistent with the measured RSSI. The hybrid model can be used to forecast regional path loss of signals through the wall.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.194-198
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• 2011

In this paper, we obtained the path loss characteristics in the 850 MHz for Russia area by using the free space path loss model and Okumura-Hata path loss model. In order to extract the additional path loss model parameter from the new Russian regional properties, the mean square error technique is used to obtain the correction factor. According to the obtained correction factor, the differences for the free space and Hata path loss model are 17, 6 dB in the 5 ~ 10 Km, 28, 14 dB in the 10 ~ 15 Km, and 35, 18 dB in the 15 ~ 20 Km. By applying the correction factors, the appropriate partial path loss models for the measured Russain area are proposed.

• ETRI Journal
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• v.33 no.2
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• pp.283-286
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• 2011

Path loss plays fundamental roles in system design, spectrum management, and performance evaluation. The traditional path loss model has a slight inconvenience; it depends on the unknown distance. In this letter, we explore the probability distribution function (PDF) of path loss in an indoor office environment by randomizing out the distance variable. It is shown that the resulting PDF is not Gaussian-like but is skewed to the right, and both the PDF and the moments are related to the size of the office instead of the unknown distance. To be specific, we incorporate the IEEE 802.15.4a channel parameters into our model and tabulate the cumulative distribution function with respect to different room sizes. Through a simple example, we show how our model helps a cognitive spectrum user to infer path loss information of primary users without necessarily knowing their transmitter-receiver distance.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2002.11a
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• pp.127-131
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• 2002

This paper presents the a measured path-loss characteristics in urban line-of-sight(LOS) and non line-of-sight(NLOS) environments for 3.4, 5.3, and 6.4 ㎓ band signals. A two-ray model is applied to analyse the path-loss characteristics in LOS areas. In LOS areas, an empirical break point, whose distance is shorter than a theorical break point, is founded. Further, a sudden power level drop occurs at a transition point from LOS region to NLOS area and different path-loss exponents are occured various cases. The power level drop due to comer loss and path-loss exponents both increase as the distance between the transmitter and the corner increases.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.223-226
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• 2004

This paper analyzes the accuracy of free space path loss and matched filter gain approximated by using a passband rectangular pulse for ultra wideband (UWB) radio systems. The example causal signal, a modulated Gaussian pulse with the same center frequency and frequency bandwidth of the passband rectangular pulse, is used to consider the accuracy. The path loss and matched filter gain of the modulated Gaussian pulse are simulated for the reference results. The UWB free space path loss is shown and is compared with that obtained from simulation and Friis' transmission formula. The UWB matched filter gain is shown and compared with simulation results. From the results, we can see that the UWB path loss formula is more accurate than the Friis' transmission formula. The results from the UWB free space path loss and matched filter gain formulas agree with the simulation. Then, these free space path loss and matched filter gain formulas approximated by using a passband rectangular pulse are appropriate for UWB system.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.9-14
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• 2014

The path loss data was re-calculated according to the distance between the base station and a mobile station in the mobile telecommunications network. In this paper, the averaged path loss data was plotted with the conventional path loss models(free space, plane earth, Hata model ${\ldots}$). The standard deviations for the 2 Km, 1 Km, 0.5 Km-interval averaged path loss were 2.29 dB, 3.39 dB, 4.75 dB, respectively. Additionally, the derivative values for the 2 Km, 1 Km, 0.5 Km-interval averaged path loss were evaluated to find the positions with more than 1 times or 2times of the standard deviation. The situations with the sharply fluctuated path loss were calculated to 5 positions in the 2 Km interval, to 7 positions in the 1 Km interval, to 19 positions in the 0.5 Km interval, respectively. And, the exact distances between the base station and a mobile station were found with the sharply fluctuated path loss.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.1-12
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• 2024

Predicting path loss is one of the important factors for wireless network design, such as selecting the installation location of base stations in cellular networks. In the past, path loss values were measured through numerous field tests to determine the optimal installation location of the base station, which has the disadvantage of taking a lot of time to measure. To solve this problem, in this study, we propose a path loss prediction method based on machine learning (ML). In particular, an ensemble learning approach is applied to improve the path loss prediction performance. Bootstrap dataset was utilized to obtain models with different hyperparameter configurations, and the final model was built by ensembling these models. We evaluated and compared the performance of the proposed ensemble-based path loss prediction method with various ML-based methods using publicly available path loss datasets. The experimental results show that the proposed method outperforms the existing methods and can predict the path loss values accurately.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.511-521
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• 2010

This paper presents a new algorithm to determine accurate bus-wise transmission loss allocation utilizing path-integrals dictated by the transaction strategy. For any transaction strategy, the total sum of the allocated transmission losses of all buses is equal to the actual loss given by the AC power-flow calculation considering the distributed slack. In this paper, the bus-wise allocation of the transmission loss is calculated by integrating the differential loss along a path determined by the transaction strategy. The proposed algorithm is also compared with Galiana's method, which is the well-known transmission loss allocation algorithm based on integration. The performance of the proposed algorithm is evaluated by case studies carried out on the WSCC 9-bus, IEEE 14-bus, New England 39-bus, and IEEE 118-bus systems. The simulation results show that the proposed algorithm is fast and accurate with a large step size.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.8
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• pp.437-444
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• 2014

In this paper, we propose a path loss model with the multiple antennas and doppler shift for high speed railroad communication. Path loss model is very important in order to design consider diverse characteristic in high-speed train communication. Currently wireless communication systems use the multiple antennas in order to improve the channel capacity or diversity gain. However, until recently, many researches on path loss model only consider geographical environment between the transmitter and the receiver. There is no study about path loss model considering diversity effect and doppler shift. In order to make average residuals considering doppler shift we use tuned free space path loss model which is utilized for measurement results at high speed railroad. The environment of high speed rail is mostly at viaduct and flatland over than 50 percent. And in order to make average residuals considering multiple antenna we use theoretical estimation of diversity gain with MRC scheme. proposed model predict loss of received signal by estimating average residuals between diversity effect and doppler shift.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1249-1253
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• 2005

This paper presents a method to model the path loss characteristics in microwave urban line-of-sight (LOS) propagation. We propose new upper- and lower-bound models for the LOS path loss using fuzzy linear regression (FLR). The spread of upper- and lower-bound of FLR depends on max and min value of a sample path loss data while the conventional upper- and lower-bound models, the spread of the bound intervals are fixed and do not depend on the sample path loss data. Comparison of our models to conventional upper- and lower-bound models indicate that improvements in accuracy over the conventional models are achieved.