Local Average Signal Strength Estimation for Indoor Multipath Propagation

Obeidat, H. A.; Alabdullah, Ali A. S.; Ali, Nazar; Asif, Rameez; Obeidat, Omar; Bin-Melha, Mohammed S.; Shuaieb, Wafa; Abd‐Alhameed, Raed A.; Excell, Peter S.

doi:10.1109/access.2019.2918178

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…At each position, the Rx antenna was moved in a 4λ-size region along with the backrest to collect channel data with independent small-scale fading. This region size was selected to remove the effects of fast fading as suggested in [23], [24]. It is worth noting that, the aircraft was parked and no passengers were in the cabin, so the environment can be viewed as quasistationary during the measurement campaigns.…”

Section: ) Measurement Campaignsmentioning

confidence: 99%

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

et al. 2019

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Wireless communications in aircraft cabin environments have drawn widespread attention with the increase of application requirements. To ensure reliable and stable in-cabin communications, the investigation of channel parameters such as path loss is necessary. In this paper, four machine learning methods, including back propagation neural network (BPNN), support vector regression (SVR), random forest, and AdaBoost, are used to build path loss prediction models for an MD-82 aircraft cabin. Firstly, machine-learning-based models are designed to predict the path loss values at different locations at a fixed frequency. It is shown that these models fit the measured data well, e.g., at 2.4 GHz central frequency the root mean square errors (RMSEs) of BPNN, SVR, random forest, and AdaBoost predictors are 1.90 dB, 2.20 dB, 1.76 dB, and 2.12 dB. Subsequent research is engaged to forecast path loss at a new frequency based on available information at known frequencies. Additionally, to solve the data limitation problem at the new frequency, we propose a path loss prediction scheme combining empirical models and machinelearning-based models. This scheme uses estimated values generated by the empirical model according to prior information to expand the training set. To verify the performance of this scheme, measured samples at 2.4 GHz and 3.52 GHz, as well as samples generated by the empirical model are employed as the training set for the path loss prediction at 5.8 GHz. The RMSEs of BPNN, SVR, random forest, and AdaBoost models are 2.49 dB, 2.78 dB, 2.54 dB, and 3.76 dB. In contrast, without samples generated by the empirical model, the RMSEs of those models are 3.84 dB, 4.94 dB, 6.57 dB, and 6.77 dB. Results show that the proposed data expansion scheme improves prediction performance when there are few measurement samples at the new frequency. INDEX TERMS Aircraft cabin, data expansion, machine learning, path loss prediction, propagation characteristics.

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Section: ) Measurement Campaignsmentioning

confidence: 99%

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

et al. 2019

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“…Owing to the loss due to multipath propagation in the indoor environment, a local average power method is needed to obtain precise measurement data. Equation ( 1) is used for applying the local average power method, and the three parameters are the averaging length (L), number of samples (N), and interval between samples (d) [4,5]. N varies according to the changes in L and d. Te value of d was fxed at 1.5 cm, and the results according to the change in L were analyzed.…”

Section: Measurement Methodmentioning

confidence: 99%

“…In an indoor multipath propagation environment, the local average power-signal strength has been estimated using power accumulation and the 10λ linear average at 900 MHz and 2 GHz frequencies [4]. In addition, by simulating the 2.4 GHz frequency in an indoor multipath propagation environment, researchers estimated the local average powersignal strength based on the size, spacing, and arrangement [5]. Although path loss models have been presented for 8, 9, 10, and 11 GHz in indoor corridors and ofce environments [6], there is no mention of a local average power measurement method or the coefcient of determination using linear regression analysis.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Analysis of Local Average Power According to Averaging Length Changes of 3, 6, 10, and 17 GHz in an Indoor Corridor Environment

Lee

Cho

2023

International Journal of Antennas and Propagation

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This study measures and analyzes the local average power for line-of-sight (LOS) and non-line-of-sight (NLOS) paths according to the averaging length in an indoor corridor environment. The indoor corridor comprises multiple offices, laboratory spaces, and lecture rooms. We selected 3, 6, 10, and 17 GHz measurement frequency bands. The measurement system consists of a signal generator, a low-noise amplifier, transmission and receiving antenna, and spectrum analyzer. To obtain an accurate prediction model of propagation due to the multipath effect, we determined the measurement method based on the measurement interval and number of measurements according to changes in the averaging length. 2, 4, 6, 8, and 10 lambdas (λ) were selected for the number of measurements by frequency, and 1.5 cm was set as the measurement interval. We used the close-in (CI) path loss model for the analysis according to changes in the averaging length. The coefficient of determination (R-squared) was applied using a linear regression equation to verify the measurement accuracy. Based on parameter n of the CI path loss model, no large differences were observed in the averaging length at each measurement frequency. However, at 2λ, owing to the multipath effect, R-squared was approximately 0.4–0.7 for the LOS path and 0.6–0.8 for the NLOS path. At 10λ, R-squared was approximately 0.7–0.8 for the LOS path and 0.8–0.9 for the NLOS path. This indicated that as the number of measurements increased by increasing the averaging length, the accuracy of the measurement results improved. The study findings will help determine an optimal averaging length, thus ensuring reliable indoor propagation measurement and contributing to the ITU-R standard.

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“…), dimensions, and fenestration can result in strong attenuation of RF signals into and out of buildings. When the variability of these RF signals becomes huge due to multipath effects [9] and sources of interference [10], this can result in users to suffer from poor radio coverage for mobile phones and low signal levels of global positioning system. Moreover, the dispersive properties of the walls can also result in distortion of the RF signals due to the strong frequencydependent response associated with internal resonances.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic wave propagation through composite building materials in urban environments at mid‐band 5G frequencies

Sum

Soong

et al. 2022

IET Microwaves Antenna & Prop

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In an urban built city environment, the performance of electromagnetic (EM) wave propagation through building materials poses a new set of challenges to radio frequency engineers in the prediction of the 5G‐network coverage planning. We propose the prospects on the use of composite building materials enhanced with different volumetric fractions of iron (III) oxide (Fe2O3) inclusions as a reddish‐brown colouring admixture in modern‐day concrete. Using a non‐destructive microwave measurement technique, a two‐factor 6 × 10 factorial experiment and a randomized controlled statistical study from 3.40 to 3.60 GHz is conducted on 15‐cm thick building material prototypes enhanced with 2‐wt% to 10‐wt% micro‐sized Fe2O3 inclusions to evaluate their efficacy on EM wave propagation. Transmission coefficients (S21) data are analysed statistically between treatment and control groups, yielding up to 2.28 dB mean S21 improvement in performance and a fractional bandwidth of 100% by the 2‐wt% Fe2O3 treatment group. Electromagnetic characterisations of the fabricated mortar samples with different Fe2O3 inclusions are performed using Nicholson‐Ross‐Weir model followed by the evaluation of dielectric and propagation losses. Our findings support the use of 2‐wt% Fe2O3 as the optimal volumetric fraction which improves the microwave transparency, thus creating potential EM benefits for the fifth‐generation (5G) wireless communication systems.

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Local Average Signal Strength Estimation for Indoor Multipath Propagation

Cited by 7 publications

References 27 publications

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

Measurement and Analysis of Local Average Power According to Averaging Length Changes of 3, 6, 10, and 17 GHz in an Indoor Corridor Environment

Electromagnetic wave propagation through composite building materials in urban environments at mid‐band 5G frequencies

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