Performance evaluation of sub-space techniques for array processing in TDMA systems

Abstract

In studying the possibility of increasing wireless system capacity, we evaluate the performance of some algorithms that make use of sub-space techniques to estimate the covariance matrix, and compare these results to those obtained through traditional methods, such as the Direct Matrix Inversion -Maximum Signal-to-Noise Ratio and Maximal Ratio Combining. Illustrative simulation results demonstrate that the Minimum Mean Square Error - signal Sub-Space and the Weighted Sub-Space algorithms may lead to a better performance than fullrank conventional algorithms. Furthermore, a more elaborate system-level simulation in a TDMA IS-136 context is performed and it shows that such benefits also appear in a more practical scenario.