Reduced-Complexity Cluster Modelling for the 3GPP Channel Model

The realistic performance of a multi-input multi-output (MIMO) communication system depends strongly on the spatial correlation properties introduced by clustering in the propagation environment. Simulating realistic correlated channels is essential to predict the performance of real MIMO systems. Since the modeling method of the correlated channels suggested by the Third Generation Partnership Project (3GPP) channel model can result in considerable implementation complexity for large networks, this paper presents a computationally efficient method to approximately calculate the spatial correlation matrix for channel models such as the 3GPP channel model, which are based on clusters of scatterers. This proposed approximation method is on the basis of using the Taylor series expansion to the steering vectors for uniform linear arrays (ULAs) and a moderate angle spread of the cluster. The approximation method is evaluated in terms of the mean square error (MSE) of the approximated correlation matrix, and by the cumulative distribution function (CDF) of the mutual information of the MIMO channel. This shows that the proposed approximation method is close for angle spread of the cluster within 10deg, with high efficiency and low complexity.