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Spatial-Temporal Correlation Properties of the 3GPP Spatial Channel Model and the Kronecker MIMO Channel Model

  • Cheng-Xiang Wang
  • Xuemin Hong
  • Hanguang Wu
  • Wen Xu
Open Access
Research Article
Part of the following topical collections:
  1. Space-Time Channel Modeling for Wireless Communications

Abstract

The performance of multiple-input multiple-output (MIMO) systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM) in the Third Generation Partnership Project (3GPP) and the Kronecker-based stochastic model (KBSM) at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA) and angle of departure (AoD). The KBSM with the Gaussian-shaped power azimuth spectrum (PAS) is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.

Keywords

Stochastic Model Spatial Correlation System Level Spatial Separability Cluster Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Cheng-XiangWang et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Cheng-Xiang Wang
    • 1
  • Xuemin Hong
    • 1
  • Hanguang Wu
    • 2
  • Wen Xu
    • 2
  1. 1.Joint Research Institute in Signal and Image Processing, School of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghUK
  2. 2.Baseband Algorithms and Standardization LaboratoryBenQ MobileMunichGermany

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