This paper proposes a non-matrix inversion based algorithm to implement decorrelating detection (DD), namely quasi-decorrelating detector (QDD), which uses truncated matrix power expansion instead of inverted correlation matrix to overcome the problems associated with the inversion transformation in DD, such as noise enhancement, computational complexity and matrix singularity etc. Two alternative QDD implementation schemes are presented in this paper; one is to use multi-stage feed-forward filters and the other is to use an nth order single matrix filter (neither involves matrix inversion). In addition to significantly reduced computational complexity, if compared with DD, the QDD algorithm offers a unique flexibility to trade among multiple access interference (MAI) suppression, near-far resistance and noise enhancement according to varying system set-ups. The obtained results show that the QDD outperforms decorrelator in either additive white Gaussian noise (AWGN) or multi-path channel, if the number of feed-forward stages is chosen properly. This paper also studies the impact of correlation statistics of spreading codes on the QDD's performance with the help of a performance-determining factor derived in this paper, which offers a code-selection guideline for the optimal performance of the QDD algorithm.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering