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

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 full-rank 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.