Validating a novel automatic cluster tracking algorithm on synthetic time-variant MIMO channels (original) (raw)
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.
International Journal of Electrical and Computer Engineering (IJECE), 2023
Fifth-generation (5G) wireless systems increased the bandwidth, improved the speed, and shortened the latency of communications systems. Various channel models are developed to study 5G. These channel models reproduce the stochastic properties of multiple-input multiple-output (MIMO) antennas by generating wireless multipath components (MPCs). The MPCs with similar properties in delay, angles of departure, and angles of arrival form clusters. The multipaths and multipath clusters serve as datasets to understand the properties of 5G. These datasets generated by the Cooperation in Science and Technology 2100 (COST 2100), International Mobile Telecommunications-2020 (IMT-2020), Quasi Deterministic Radio Channel Generator (QuaDRiGa), and Wireless World Initiative New Radio II (WINNER II) channel models are tested for their homoscedasticity based on Johansen's procedure. Results show that the COST 2100, QuaDRiGa, and WINNER II datasets are heteroscedastic, while the IMT-2020 dataset is homoscedastic.
A spectral clustering algorithm for decoding fast time-varying BPSK MIMO channels
15th European Sig. Proc. …, 2007
Clustering techniques for equalization have been proposed by a number of authors in the last decade. However, most of these approaches focus only on time-invariant singleinput single-output (SISO) channels. In this paper we consider the case of fast time-varying multiple-input multipleoutput (MIMO) channels. The varying nature of the mixing matrix poses new problems that cannot be solved by conventional clustering techniques. By introducing the time scale into the clustering process we are able to untangle the clusters, which in this way behave like intertwined threads. Then, a spectral clustering algorithm is applied. Finally, the identified clusters are assigned to the transmitted symbols using only a few pilots. The geometry of the transmitted constellation is exploited within the spectral clustering algorithm in order to reduce the number of clusters. As shown in the paper, the proposed procedure saves a considerable amount of pilot symbols in comparison to other recently proposed techniques.
On mm-wave multipath clustering and channel modeling
2014
Efficient and realistic mm-wave channel models are of vital importance for the development of novel mm-wave wireless technologies. Though many of the current 60 GHz channel models are based on the useful concept of multi-path clusters, only a limited number of 60 GHz channel measurements have been reported in the literature for this purpose. Therefore, there is still a need for further measurement based analyses of multi-path clustering in the 60 GHz band. This paper presents clustering results for a double-directional 60 GHz MIMO channel model. Based on these results, we derive a model which is validated with measured data. Statistical cluster parameters are evaluated and compared with existing channel models. It is shown that the cluster angular characteristics are closely related to the room geometry and environment, making it infeasible to model the delay and angular domains independently. We also show that when using ray tracing to model the channel, it is insufficient to only consider walls, ceiling, floor and tables; finer structures such as ceiling lamps, chairs and bookshelves need to be taken into account as well.
Statistical Analysis of Multipath Clustering in an Indoor Office Environment
EURASIP Journal on Wireless Communications and Networking, 2011
A parametric directional-based MIMO channel model is presented which takes multipath clustering into account. The directional propagation path parameters include azimuth of arrival (AoA), azimuth of departure (AoD), delay, and power. MIMO measurements are carried out in an indoor office environment using the virtual antenna array method with a vector network analyzer. Propagation paths are extracted using a joint 5D ESPRIT algorithm and are automatically clustered with the Kpower-means algorithm. This work focuses on the statistical treatment of the propagation parameters within individual clusters (intracluster statistics) and the change in these parameters from one cluster to another (intercluster statistics). Motivated choices for the statistical distributions of the intracluster and intercluster parameters are made. To validate these choices, the parameters' goodness of fit to the proposed distributions is verified using a number of powerful statistical hypothesis tests. Additionally, parameter correlations are calculated and tested for their significance. Building on the concept of multipath clusters, this paper also provides a new notation of the MIMO channel matrix (named FActorization into a BLock-diagonal Expression or FABLE) which more visibly shows the clustered nature of propagation paths.