Cluster-level parameters at 5.25 GHz indoor-to-outdoor and outdoor-to-indoor MIMO radio channels

Hentilä, Lassi; Alatossava, M.; Czink, Nicolai; Kyösti, Pekka

doi:10.1109/istmwc.2007.4299204

Cited by 6 publications

(1 citation statement)

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“…Reference [4] proposes the average number of clusters in indoor office environments, which can reflect the active number of clusters in different positions. Reference [5] shows the lifetimes of clusters in indoor-to-outdoor and outdoor-to-indoor environments, while Reference [6] reports them in an indoor hall environment. Moreover, how to continuously parameterize the clusters, and track and identify them in mobile channels have been considered in many studies.…”

Section: Introductionmentioning

confidence: 99%

Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations

Wang

Zhang

2019

Applied Sciences

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In wireless communication systems, channels evolve when user terminals move. To further understand channel variation, and especially the evolution of clusters in mobile channels, a set of experiments was designed. First, we performed pedestrian mobile measurements in an urban macro (UMa) scenario at 3.5 GHz, and the K-power means-Kalman filter (KPMKF) algorithm was used for clustering and tracking. By this process, the trajectory of different clusters could clearly be described during measurement. The birth and death rate of clusters per snapshot show that the change of one or two clusters in each snapshot takes more probabilities. In addition, the differences of the cluster lifetime between the clustering process with and without the Kalman filter (KF) algorithm are given to show the effect from the KF. Second, channel simulations were implemented based on the above observed results. The spatial-consistency feature was introduced to get closer to the measured channels, which is based on the primary module of International Mobile Telecommunications-2020 (IMT-2020) channel model. Comparisons among measurements and simulations with and without this feature show that adding this feature improves simulation accuracy. To explore a novel method to characterize clusters during linear movement, a gradient boosted decision-tree (GBDT) algorithm is introduced. It uses the above characteristics of clusters and channel impulse responses (CIRs) as the training and validating dataset. The root mean square error (RMSE) shows that this is promising.

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Section: Introductionmentioning

confidence: 99%