2018/09/02 by Manijeh Bashar, Bashar, Manijeh, Katsuyuki Haneda +5
Agricultural and Biological Sciences · Engineering · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Millimeter-Wave Propagation and Modeling #Plant Pathogens and Resistance #Power Line Communications and Noise #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1809.00386
openalex publication_date 2018/09/02 · openalex created_date 2022/12/11 · openalex updated_date 2026/07/28
The available geometry-based stochastic channel models (GSCMs) at\nmillimetre-wave (mmWave) frequencies do not necessarily retain spatial\nconsistency for simulated channels, which is essential for small cells with\nultra-dense users. In this paper, we work on cluster parameterization for the\nCOST 2100 channel model using mobile channel simulations at 61 GHz in Helsinki\nAirport. The paper considers a ray-tracer which has been optimized to match\nmeasurements, to obtain double-directional channels at mmWave frequencies. A\njoint clustering-tracking framework is used to determine cluster parameters for\nthe COST 2100 channel model. The KPowerMeans algorithm and the Kalman filter\nare exploited to identify the cluster positions and to predict and track\ncluster positions respectively. The results confirm that the joint\nclustering-and-tracking is a suitable tool for cluster identification and\ntracking for our ray-tracer results. The movement of cluster centroids, cluster\nlifetime and number of clusters per snapshot are investigated for this set of\nray-tracer results. Simulation results show that the multipath components\n(MPCs) are grouped into clusters at mmWave frequencies.\n