A low complexity algorithm to simulate the spatial covariance matrix for clustered MIMO channel models

The capacity and error rate performance of a multiple-input multiple-output (MIMO) communication system depend strongly on the spatial correlation properties introduced by clustering in the propagation environment. Simulating correlated channels, using, for example, the common correlated Rayleigh fading model, requires numerically complex calculations of the transmit and receive spatial correlation matrices as a function of the cluster size and location. The paper proposes a numerically efficient way of generating these correlation matrices for indoor clustered channel models. The method makes use of a uniform linear array approximation to avoid numerical integrals and derives a closed-form expression for the correlation coefficients, assuming a 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.