Abstract
Accuracy and complexity are two crucial aspects of the applicability of a channel model for wideband multiple input multiple output (MIMO) systems. For small number of antenna element pairs, correlation-based models have lower computational complexity while the geometry-based stochastic models (GBSMs) can provide more accurate modeling of real radio propagation. This paper investigates several potential simplifications of the GBSM to reduce the complexity with minimal impact on accuracy. In addition, we develop a set of broadband metrics which enable a thorough investigation of the differences between the GBSMs and the simplified models. The impact of various random variables which are employed by the original GBSM on the system level simulation are also studied. Both simulation results and a measurement campaign show that complexity can be reduced significantly with a negligible loss of accuracy in the proposed metrics. As an example, in the presented scenarios, the computational time can be reduced by up to 57% while keeping the relative deviation of 5% outage capacity within 5%.
Highlights
The pioneering work by Winters [1], Telatar [2], Foschini and Gans [3] ignited enormous interest in multiple input multiple output (MIMO) systems as they have the potential to provide remarkable spectral efficiencies when the channel exhibits rich scattering
We have studied the simplification of the geometry-based stochastic models (GBSMs) by several approaches
The double-directional channel model developed under the IST-WINNER project is employed as the baseline model
Summary
The pioneering work by Winters [1], Telatar [2], Foschini and Gans [3] ignited enormous interest in multiple input multiple output (MIMO) systems as they have the potential to provide remarkable spectral efficiencies when the channel exhibits rich scattering. An overview of the state-of-theart channel models is provided in [9] These channel models can be divided into two major categories: (a) the correlation based models, for example, the Kronecker model [10] and the Weichselberger model [11]; and (b) the parametric or geometry-based stochastic models (GBSMs), for example, the COST 259 directional channel model (DCM) [12], the COST 273 channel model [13], the 3rd Generation Partnership Project (3GPP) spatial channel model (SCM) [14], and the WINNER channel model [15, 16], and so forth.
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