Random matrix theory and wireless communications
Communications and Information Theory
Blind reduced-rank MMSE detector for DS-CDMA systems
EURASIP Journal on Applied Signal Processing
Reduced-rank adaptive filtering using Krylov subspace
EURASIP Journal on Applied Signal Processing
Robust and computationally efficient signal-dependent method for joint DOA and frequency estimation
EURASIP Journal on Advances in Signal Processing
Krylov-proportionate adaptive filtering techniques not limited to sparse systems
IEEE Transactions on Signal Processing
Consistent reduced-rank LMMSE estimation with a limited number of samples per observation dimension
IEEE Transactions on Signal Processing
Generalized consistent estimation on low-rank Krylov subspaces of arbitrarily high dimension
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Robust reduced-rank adaptive algorithm based on parallel subgradient projection and Krylov subspace
IEEE Transactions on Signal Processing
Asynchronous CDMA systems with random spreading-part II: design criteria
IEEE Transactions on Information Theory
Adaptive reduced-rank localization for multiple wideband acoustic sources
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
Hi-index | 754.97 |
The performance of reduced-rank linear filtering is studied for the suppression of multiple-access interference. A reduced-rank filter resides in a lower dimensional space, relative to the full-rank filter, which enables faster convergence and tracking. We evaluate the large system output signal-to-interference plus noise ratio (SINR) as a function of filter rank D for the multistage Wiener filter (MSWF) presented by Goldstein and Reed. The large system limit is defined by letting the number of users K and the number of dimensions N tend to infinity with K/N fixed. For the case where all users are received with the same power, the reduced-rank SINR converges to the full-rank SINR as a continued fraction. An important conclusion from this analysis is that the rank D needed to achieve a desired output SINR does not scale with system size. Numerical results show that D=8 is sufficient to achieve near-full-rank performance even under heavy loads (K/N=1). We also evaluate the large system output SINR for other reduced-rank methods, namely, principal components and cross-spectral, which are based on an eigendecomposition of the input covariance matrix, and partial despreading. For those methods, the large system limit lets D→∞ with D/N fixed. Our results show that for large systems, the MSWF allows a dramatic reduction in rank relative to the other techniques considered