Complexity reduction of iterative minimum mean square error detection and decoding techniques

We propose a new differential recursive update algorithm, which significantly reduces the complexity of iterative minimum mean square error (MMSE) detection and decoding techniques for spread spectrum communication systems. Although the MMSE detector is a less complex suboptimum solution comparing to the optimum one, it is still complex for iterative systems because a large number of matrix inversions need to be computed over different users, symbols, and iterations. The new algorithm enables a smooth transition from the MMSE detection with full complexity to the simple matched filter solution over iterations, thereby achieving complexity reduction. We show by means of computer simulations that the complexity is drastically reduced by the newly proposed algorithm at the expense of increased memory requirements.