Predicting MIMO Performance in Urban Microcells Using Ray Tracing to Characterize the Channel

We describe a method for estimating achievable data rates in urban microcells using multiple-input/multiple-output (MIMO) techniques. Specifically, we use site maps and a versatile ray-tracing tool to compute MIMO gain matrices as a function of terminal location; and we use these matrices to determine achievable rates for various MIMO transmission modes (spatial multiplexing, beamforming, and diversity). Numerical results are generated for specific paths in Boston and Manhattan, though our results are shown to be fairly insensitive to neighborhood or city. We also show that, in urban microcells, data rate prediction using site-specific ray tracing is more informative than using stochastic models; and that adaptive switching among MIMO transmission modes as a terminal moves along its trajectory can help sustain high data rates. A new mode-switching algorithm is proposed that requires switching rates lower than those for the optimal scheme by a factor greater than 10, with little loss in average data rate. We also propose a novel software algorithm for optimally placing microcell bases. For a Manhattan neighborhood of area 0.5 km², we find that full coverage can be obtained using only 5 bases, and that the highest total throughput is achieved using a frequency reuse factor of 1.