Adaptive reduced-rank interference suppression based on the multistage Wiener filter

TLDR

The paper proposes adaptive reduced‑rank interference suppression algorithms using the multistage Wiener filter, including batch and recursive parameter estimation that avoids eigen‑decomposition. The authors develop MSWF‑based reduced‑rank algorithms, estimating filter parameters with batch and recursive methods that avoid eigen‑decomposition, and evaluate their performance through simulations in heavily loaded DS‑CDMA systems. The reduced‑rank algorithms attain near full‑rank performance with a rank far below the signal subspace dimension and need significantly fewer training samples than other reduced‑ or full‑rank methods.

Abstract

A class of adaptive reduced-rank interference suppression algorithms is presented based on the multistage Wiener filter (MSWF). The performance is examined in the context of direct-sequence (DS) code division multiple access (CDMA). Unlike the principal components method for reduced-rank filtering, the algorithms presented can achieve near full-rank performance with a filter rank much less than the dimension of the signal subspace. We present batch and recursive algorithms for estimating the filter parameters, which do not require an eigen-decomposition. The algorithm performance in a heavily loaded DS-CDMA system is characterized via computer simulation. The results show that the reduced-rank algorithms require significantly fewer training samples than other reduced- and full-rank algorithms.