Mobile Station Spatio-Temporal Multipath Clustering of an Estimated Wideband MIMO Double-Directional Channel of a Small Urban 4.5 GHz Macrocell (original) (raw)
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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.
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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.
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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.
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