Lattice-based subspace decomposition for DS-CDMA detection

Graeme Woodward; Michael L. Honig; Branka S. Vucetic

Lattice-based subspace decomposition for DS-CDMA detection

Graeme Woodward^*, Michael L. Honig, Branka S. Vucetic

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Lattice filtering is applied to the MMSE (Minimum Mean Squared Error) DS-CDMA receiver. The lattice performs Cholesky decomposition on the sampled input covariance matrix. This identifies information about signal and noise subspaces, which can be exploited to decrease complexity of the MMSE receiver using LS (Least Squares) adaptive algorithms. Two reduced complexity techniques are considered: (1) filter truncation and (2) linear combining of multiple truncated sub-filters. In both cases, the dimension of the (sub)filters must be larger than that of the user subspace. Numerical results presented compare performance of these techniques using LMS and LS algorithms.

Original language	English (US)
Title of host publication	IEEE International Symposium on Spread Spectrum Techniques & Applications
Publisher	IEEE
Pages	164-168
Number of pages	5
Volume	1
State	Published - Jan 1 1998
Event	Proceedings of the 1998 IEEE 5th International Symposium on Spread Spectrum Techniques & Applications, IEEE ISSSTA. Part 3 (of 3) - Sun City, S Afr Duration: Sep 2 1998 → Sep 4 1998

Other

Other	Proceedings of the 1998 IEEE 5th International Symposium on Spread Spectrum Techniques & Applications, IEEE ISSSTA. Part 3 (of 3)
City	Sun City, S Afr
Period	9/2/98 → 9/4/98

ASJC Scopus subject areas

General Engineering

Cite this

@inproceedings{9c7591f04f1e4c5a874b14919b59534a,

title = "Lattice-based subspace decomposition for DS-CDMA detection",

abstract = "Lattice filtering is applied to the MMSE (Minimum Mean Squared Error) DS-CDMA receiver. The lattice performs Cholesky decomposition on the sampled input covariance matrix. This identifies information about signal and noise subspaces, which can be exploited to decrease complexity of the MMSE receiver using LS (Least Squares) adaptive algorithms. Two reduced complexity techniques are considered: (1) filter truncation and (2) linear combining of multiple truncated sub-filters. In both cases, the dimension of the (sub)filters must be larger than that of the user subspace. Numerical results presented compare performance of these techniques using LMS and LS algorithms.",

author = "Graeme Woodward and Honig, {Michael L.} and Vucetic, {Branka S.}",

year = "1998",

month = jan,

day = "1",

language = "English (US)",

volume = "1",

pages = "164--168",

booktitle = "IEEE International Symposium on Spread Spectrum Techniques & Applications",

publisher = "IEEE",

note = "Proceedings of the 1998 IEEE 5th International Symposium on Spread Spectrum Techniques & Applications, IEEE ISSSTA. Part 3 (of 3) ; Conference date: 02-09-1998 Through 04-09-1998",

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TY - GEN

T1 - Lattice-based subspace decomposition for DS-CDMA detection

AU - Woodward, Graeme

AU - Honig, Michael L.

AU - Vucetic, Branka S.

PY - 1998/1/1

Y1 - 1998/1/1

N2 - Lattice filtering is applied to the MMSE (Minimum Mean Squared Error) DS-CDMA receiver. The lattice performs Cholesky decomposition on the sampled input covariance matrix. This identifies information about signal and noise subspaces, which can be exploited to decrease complexity of the MMSE receiver using LS (Least Squares) adaptive algorithms. Two reduced complexity techniques are considered: (1) filter truncation and (2) linear combining of multiple truncated sub-filters. In both cases, the dimension of the (sub)filters must be larger than that of the user subspace. Numerical results presented compare performance of these techniques using LMS and LS algorithms.

AB - Lattice filtering is applied to the MMSE (Minimum Mean Squared Error) DS-CDMA receiver. The lattice performs Cholesky decomposition on the sampled input covariance matrix. This identifies information about signal and noise subspaces, which can be exploited to decrease complexity of the MMSE receiver using LS (Least Squares) adaptive algorithms. Two reduced complexity techniques are considered: (1) filter truncation and (2) linear combining of multiple truncated sub-filters. In both cases, the dimension of the (sub)filters must be larger than that of the user subspace. Numerical results presented compare performance of these techniques using LMS and LS algorithms.

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M3 - Conference contribution

AN - SCOPUS:0031645319

VL - 1

SP - 164

EP - 168

BT - IEEE International Symposium on Spread Spectrum Techniques & Applications

PB - IEEE

T2 - Proceedings of the 1998 IEEE 5th International Symposium on Spread Spectrum Techniques & Applications, IEEE ISSSTA. Part 3 (of 3)

Y2 - 2 September 1998 through 4 September 1998

ER -

Lattice-based subspace decomposition for DS-CDMA detection

Abstract

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ASJC Scopus subject areas

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