A Low Complexity Algorithm to Simulate the Spatial Covariance Matrix for Clustered MIMO Channel Models


Antonio Forenza, David J. Love and Robert W. Heath Jr


to appear in Proc. of IEEE Vehicular Tech. Conf. , Milan, Italy, May 17 - 19, 2004.


The capacity and error rate performance of a multiple-input multiple-output (MIMO) communication system depends strongly on the spatial correlation properties introduced by clustering in the propagation environment. Simulating correlated channels, using the common correlated Rayleigh fading model for example, requires numerically complex calculations of the transmit and receive spatial correlation matrices as a function of the cluster size and location. This paper proposes a numerically efficient way of generating these correlation matrices for indoor clustered channel models. This method makes use of an uniform linear array approximation to avoid numerical integrals and a closed-form expression for the correlation coefficients that is derived for the Laplacian angle distribution. Simulations show that the approximate correlation model exhibits good fit for moderate angle spreads. Complexity calculations show that this approach takes about 1/200 of the time to compute the spatial correlation matrices compared to existing methods.

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