Conference Paper

Automatic Data Segmentation based on Statistical Hypothesis Testing for Stochastic Channel Modeling

DOI: 10.1109/PIMRC.2010.5671917 Conference: Proceedings of the IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2010, 26-29 September 2010, Istanbul, Turkey
Source: DBLP


In order to extract the statistical characteristics of propagation channels, multiple impulse responses of the channels are measured and saved e.g. in terms of cycles or bursts. In the case where measurements are performed in time-variant environments, the received signals need to be divided into multiple segments. It is essential to perform the data segmentation reasonably in order to guarantee that each segment contains the observations of the same stationary process. In this contribution, we first show experimentally the impact of the number of data bursts per segment on the clustering results. Then a novel Kolmogorov-Smirnov hypothesis-testing-based approach is proposed for automatically determining the number of bursts in each segment. The applicability of this approach is evaluated using indoor channel measurement data. The results obtained show that the number of bursts in a segment follows lognormal distributions with parameters dependent on the environments and the mobilities of the transmitter, receiver and the scatterers during the measurement campaigns.

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    ABSTRACT: In this paper, a channel modeling method based on random-propagation-graph is elaborated, validated, and applied to characterizing time-variant channels observed in typical environments for high-speed railway wireless communications. The advantage of the proposed method is that the frequency-tempo-spatial channel coefficients, as well as the multi-dimensional channel impulse responses in delay, Doppler frequency, direction of arrival (i.e. azimuth and elevation of arrival) and direction of departure are calculated analytically for specific environments. The validation of the proposed method is performed by comparing the statistics of two large-scale parameters obtained with those described in the well-established standards. Finally, stochastic geometry-based models in the same format as the well-known spatial channel model enhanced (SCME) are generated by using the proposed method for the high-speed scenarios in the rural, urban, and suburban environments.
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