2010
DOI: 10.1109/tvt.2009.2038391
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Abstract: This paper presents novel adaptive space-time reduced-rank interference suppression least squares algorithms based on joint iterative optimization of parameter vectors. The proposed space-time reduced-rank scheme consists of a joint iterative optimization of a projection matrix that performs dimensionality reduction and an adaptive reduced-rank parameter vector that yields the symbol estimates. The proposed techniques do not require singular value decomposition (SVD) and automatically find the best set of basi… Show more

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Cited by 124 publications
(103 citation statements)
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“…The incorporation of the variable switching and mixing factors of Section 4 has the potential to improve the performance of the above algorithm by optimizing the weighting of the error terms of (26). Integration of the factors given by (18) and (21) yields…”
Section: Normalized Least-mean Square Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…The incorporation of the variable switching and mixing factors of Section 4 has the potential to improve the performance of the above algorithm by optimizing the weighting of the error terms of (26). Integration of the factors given by (18) and (21) yields…”
Section: Normalized Least-mean Square Algorithmmentioning
confidence: 99%
“…However, the stability of adaptive step-sizes and forgetting factors can be a concern unless they are constrained to lie within a predefined region [19]. Other alternative schemes include those based on processing the received data in subblocks [20][21][22] and subspace algorithms [23][24][25][26][27][28]. In addition, the fundamental problem of obtaining the unfaded symbols whilst suppressing MUI remains.…”
Section: Introductionmentioning
confidence: 99%
“…The joint optimization ofw k and S D has been shown to converge to the global minimum when the MSE is employed as the cost function [14]. The proposed scheme promotes an iterative exchange of information between the transformation matrix and the reduced-rank filter, which leads to improved convergence and tracking performance.…”
Section: A Adaptive Estimation Of Projection Matrix and Receivermentioning
confidence: 99%
“…EIG, MWF and AVF have faster convergence speed with a much smaller filter size, but their computational complexity is very high. A strategy based on the joint and iterative optimization (JIO) of a subspace projection matrix and a reduced-rank filter has been reported in [13], [14], whereas algorithms with switching mechanisms have been considered in [15] for DS-CDMA systems.…”
Section: Introductionmentioning
confidence: 99%
“…These three algorithms have a faster convergence speed as compared to the FR adaptive algorithms with a much smaller filter size, but their computational complexities are high. In addition, there is another proposed algorithm that jointly considers the projection matrix and RR filter [5]. However, this algorithm is relied on the optimization of quantities which do not necessarily decrease performance of the received bit error rate (BER).…”
Section: Introductionmentioning
confidence: 99%