Computationally Efficient Methods to Simulate

Spatially Correlated Clustered MIMO

Channels with Different Array Configurations


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



submitted to IEEE Trans. on Vehic. Tech., May 2005.


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. The effects of correlation can be studied using the clustered channel model, which requires computing the transmit and receive spatial correlation matrices for a correlated Rayleigh fading channel as a function of the cluster size and location. Unfortunately, the numerical computations required to compute each correlation matrix are significant, making an extensive study over various correlation parameters difficult. This paper proposes a numerically efficient way of generating correlation matrices for indoor clustered channel models. The two ingredients for the proposed method are an approximation for uniform linear and circular arrays to avoid numerical integrals, and a closed-form expression for the correlation coefficients that is derived for the Laplacian azimuth angle distribution. Simulations show that the approximate correlation model exhibits good fit for moderate angle spreads. Simulations and analysis confirm that the proposed model results in a one-hundred-fold complexity reduction versus comparable methods in the literature.