Validating a novel automatic cluster tracking algorithm on synthetic time-variant MIMO channels (original) (raw)
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Cluster parameters for time-variant MIMO channel models
IET Seminar Digests, 2007
The next challenge for MIMO radio channel models is to simulate the time-variant nature of the channel correctly. Cluster-based MIMO channel models are well suited for this problem, however they currently lack an accurate parameterization of the time-variant cluster parameters. In this paper we identify and track clusters from three different measurements conducted in an indoor, a sub-urban, and a rural environment. The time-variant cluster parameters of interest are: (i) cluster movement, (ii) change of cluster spreads, (iii) cluster lifetimes, and birth and death rates of cluster. We find that clusters show significant movement in parameter space depending on the environment. The spreads of individual clusters change rather randomly over their lifetime, with a standard deviation up to 150% of their mean spread. The cluster lifetime is approximately exponentially distributed, however additionally one has to account for long-living clusters coming from the line-of-sight path or from major reflectors.
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.
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 .
A Novel Automatic Cluster Tracking Algorithm
2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, 2006
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). We perform a temporal tracking of cluster centroids in the multidimensional parameter domain, starting from cluster estimates obtained by a recently introduced framework which automatically clusters parametric MIMO channel data.
Path-based spectral clustering for decoding fast time-varying MIMO channels
2009
Abstract In this paper, we present a clustering technique for decoding fast time-varying multiple-input multiple-output (MIMO) channels. The proposed method builds upon previous work that exploited the symmetry of the constellation and the order of the data within a spectral clustering procedure. The novelty of this work is that by adjusting the different steps of the standard spectral clustering algorithm, it introduces the expected shape of the clusters into the clustering process.
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 in 3D MIMO Channel: Measurement-Based Results and Improvements
2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), 2015
In this paper, we perform 3-Dimensional (3D) clustering based on the Outdoor-to-Indoor (O2I) wideband 3D multiple-input-multiple-output (MIMO) channel measurement at 3.5 GHz. Clusters are identified by KPowerMeans algorithm. Based on analysis on clustering results, we modified the definition of Multiple component distance (MCD) to split the bounding of azimuth and elevation, which can obtain larger number of clusters and the clusters are more intra-compact and interseparated. Then, Calinski-Harabasz (CH) and Davies-Bouldin (DB) indices are used to further validate the proposed MCD. Finally, intra cluster and inter cluster statistics are both provided, which provides insights in 3D MIMO channel modeling.
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.