Millimeter Wave Channel Characterization for a Large Waiting Hall by Measurements and Simulations

Wang, Zhongyu; Du, Fei; Fu, Zihao; Geng, Suiyan; Zhao, Xiongwen; Zhang, Rui; Liu, Yongsheng; Wang, Mengjun; Sun, Shaojun

doi:10.1109/apcc.2018.8633470

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Excess delay (ED) depends on the effect of environmental reflection and scattering; it is an important parameter to locate in complex environment [22]. Figure 6 shows excess delays in the LOS and NLOS channels for several prediction methods.…”

Section: Prediction Results and Comparisonmentioning

confidence: 99%

“…Detailed information on QuaDRiGa platform can be found in the previous work. Combining the characteristics of statistical random model and deterministic model, the scenario is set as 3GPP‐38.901 model at 28G Hz in the QuaDRiGa model [22]. In this work, scenarios of uniform distribution and interpolation sequence are chosen in generating statistical data.…”

Section: Grey Genetic Optimized Modelmentioning

confidence: 99%

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Millimetre wave channel modeling based on grey genetic optimization model

et al. 2021

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In this paper, grey genetic optimization model (GGOM) is proposed for predicting insufficient channel parameters without increasing the amount of measurement data. Based on the millimetre wave 28 GHz indoor measurement data for both LOS and NLOS scenarios, the GGOM model is compared with traditional back propagation (BP) and grey model (GM) to analyse channel parameters like delay spread, excess delay and azimuth spread. Results show that the fitness of GGOM is better than the grey model in improving the stability of system. It works well with insufficient data (size less than 30) in most cases as it is set regardless of the specific scene and measurement data. This is verified by QuaDRiGa platform by generating uniformly distributed and interpolated data between the experimental measurement data. GGOM fits best with the measurement data compared with other prediction methods in channel characterization. Moreover, the mean absolute percentage error (MAPE) for GGOM is the least compared with GM and BP methods. The proposed GGOM model has good performance in modeling insufficient data of propagation channel, practically. How to cite this article: Geng, S., et al.: Millimeter wave channel modeling based on grey genetic optimization model.

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Section: Prediction Results and Comparisonmentioning

confidence: 99%

Section: Grey Genetic Optimized Modelmentioning

confidence: 99%