Cluster parameters for time-variant MIMO channel models (original) (raw)
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Reduced-complexity cluster modeling for time-variant wideband MIMO channels
Physical Communication, 2008
This paper presents a reduced-complexity cluster modeling method for channel models based on the 3GPP channel model to simulate the time variation of spatially correlated wideband MIMO channels. The main novelty is that, when modeling the time-variant wideband MIMO channels, instead of tracking the changes in the angles of arrival (AoAs) of all the multi-path components (MPCs) defined, we only track the change in the center AoA for each of the clusters. Hence for moderate angle spreads (ASs) of clusters and a constant uniform distribution of the offsets of the MPCs within each cluster, tracking the time-variant center AoAs of the clusters allows us to develop a computationally efficient approximation method to calculate the instantaneous channel matrix and spatial correlation matrix for time-variant wideband MIMO channels. The evaluation of the proposed method is by using the extended correlation matrix distance (CMD) metric to compare the CMDs predicted by the approximate and exact calculation under different time-variant scenarios. The simulation results show that the approximation method works well when the velocity of the movement is up to 50 m/s and provided the ASs of the clusters are within 10 .
Validating a novel automatic cluster tracking algorithm on synthetic time-variant MIMO channels
2000
On the way to answer the controversial question "What is a cluster?", we introduce a novel cluster tracking mechanism which is based on the multi-path component distance (MCD). Starting from cluster estimates obtained by a recently introduced framework which automatically clusters parametric MIMO channel data, we are tracking cluster centroids in the multidimensional parameter domain. To validate our algorithm, we
EURASIP Journal on Wireless Communications and Networking, 2009
Multipath clusters in a wireless channel could act as additional channels for spatial multiplexing MIMO systems. However, identifying them in order to come up with better cluster channel models has been a hurdle due to how they are defined. This paper considers the identification of these clusters at the mobile station through a middle ground approach-combining a globally optimized automatic clustering approach and manual clustering of the physical scatterers. By including the scattering verification in the cluster identification, better insight into their behavior in wireless channels would be known, especially the physical realism and eventually a more satisfactorily accurate cluster channel model could be proposed. The results show that overlapping clusters make up the majority of the observed channel, which stems from automatic clustering, whereas only a few clusters have clear delineation of their dispersion. In addition, it is difficult to judge the physical realism of overlapping clusters. This further points to a need for the physical interpretation and verification of clustering results, which is an initial step taken in this paper. From the identification results, scattering mechanisms of the clusters are presented and also their selected first and second order statistics.
Clustering of MIMO Channel Parameters - Performance Comparison
VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, 2009
Novel channel models as from COST 273 and IST-WINNER projects are models to evaluate the performance of multi-antenna concepts under link-level and system-level. For consistent performance evaluation the channel models needed to be parameterized by multipath parameters based on measurements. It seems these parameters can be grouped into geometrically co-located paths, so called clusters. The reliability and reproducibility of the estimated parameter groups, depend inter alia on the decision criterions, initialization and the chosen cluster algorithm itself. In this paper the focus is to analyse the performance of different clustering algorithms and initialization stages. Furthermore an improved initialization approach is presented.
A Time-Varying Clustering Algorithm for Channel Modeling of Vehicular MIMO Communications
2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020
Currently, the research of channel modeling pays more attention to time-varying channels, e.g., vehicle-to-vehicle (V2V) communications. Meanwhile, it is found from many measurements of wireless channels that the multipath components (MPCs) are usually distributed in groups, which is considered as the clustered MPCs. This paper thus proposes a novel clustering algorithm for the time-varying channels, which clusters the dynamic MPCs by using the evolution patterns over time. Through the evaluation based on the realistic V2V measurement data, the proposed algorithm achieves relatively better performance compared with the conventional methods.
A cluster-based analysis of outdoor-to-indoor office MIMO measurements at 5.2 GHz
2006
In this paper, we present a cluster based analysis of an outdoor-to-indoor Multiple-Input Multiple-Output (MIMO) measurement campaign, and extract model parameters for the COST273 channel model. The measurements were performed at 5.2 GHz for 159 measurement locations in an office building. Multipath component (MPC) parameters have been extracted for these positions using a high-resolution algorithm. We analyze the clustering of MPCs, i.e., grouping together of MPCs with similar DOAs, DODs, and delays. We compare cluster identification by visual inspection to automatic identification by the recently proposed algorithm of Czink et al. In the paper we include results on the intercluster properties such as the distribution of the number of clusters and the cluster powers, as well as intracluster properties such as the angle and delay spreads within the clusters. In particular, we extract parameters for the COST 273 channel model, a standardized generic model for MIMO propagation channels.
On Geometry-Based Statistical Channel Models for MIMO Wireless Communications
The use of wideband Multiple Input Multiple Output (MIMO) communication systems is currently subject to considerable interest. One reason for this is the latest development of 3rd Generation mobile communication systems and beyond, such as the wideband technology: Wideband Code Division Multiple Access (WCDMA), which provides 5 MHz wide radio channels. For the design and simulation of these mobile radio systems taking into account MIMO wireless propagation (e.g. like the wideband-CDMA), channel models are needed that provide the required spatial and temporal information necessary for studying such systems, i.e., the basic modeling parameters in the space-time domains, e.g., the root mean square (rms) delay spread (DS) is directly connected to the capacity of a specific communication system and gives a rough implication on the complexity of a receiver. In this thesis a channel modeling based on the clustering approach is proposed and used for analysis in the space-time domains for st...
A Study of Dynamic Multipath Clusters at 60 GHz in a Large Indoor Environment
2018
The available geometry-based stochastic channel models (GSCMs) at millimetre-wave (mmWave) frequencies do not necessarily retain spatial consistency for simulated channels, which is essential for small cells with ultra-dense users. In this paper, we work on cluster parameterization for the COST 2100 channel model using mobile channel simulations at 61 GHz in Helsinki Airport. The paper considers a ray-tracer which has been optimized to match measurements, to obtain double-directional channels at mmWave frequencies. A joint clustering-tracking framework is used to determine cluster parameters for the COST 2100 channel model. The KPowerMeans algorithm and the Kalman filter are exploited to identify the cluster positions and to predict and track cluster positions respectively. The results confirm that the joint clustering-and-tracking is a suitable tool for cluster identification and tracking for our ray-tracer results. The movement of cluster centroids, cluster lifetime and number of clusters per snapshot are investigated for this set of ray-tracer results. Simulation results show that the multipath components (MPCs) are grouped into clusters at mmWave frequencies. cc Index terms-Cluster identification, Kalman filter, KPowerMeans, millimetre wave, multi path components.