Steady-state analysis of constrained normalized adaptive filters for MAI reduction by energy conservation arguments

Authors:
R. L. G. Cavalcante;I. Yamada
Affiliations:
Department of Communications and Integrated Systems, Tokyo Institute of Technology, 2-12-1-S3-60 Ookayama, Meguro-ku, Tokyo 152-8550, Japan;Department of Communications and Integrated Systems, Tokyo Institute of Technology, 2-12-1-S3-60 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
Venue:
Signal Processing
Year:
2008

Citing 11
Cited 1

Adaptive filter theory (2nd ed.)

Adaptive filter theory (2nd ed.)
Multiuser Detection

Multiuser Detection
Wireless Communication

Wireless Communication
Constrained Adaptive Linear Multiuser Detection Schemes

Journal of VLSI Signal Processing Systems
Efficient Blind MAI Suppression in DS/CDMA Systems by Embedded Constraint Parallel Projection Techniques

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Adaptive tracking of linear time-variant systems by extended RLSalgorithms

IEEE Transactions on Signal Processing
Multiaccess Interference Suppression in Orthogonal Space–Time Block Coded MIMO Systems by Adaptive Projected Subgradient Method

IEEE Transactions on Signal Processing
A unified approach to the steady-state and tracking analyses ofadaptive filters

IEEE Transactions on Signal Processing
Data-aided and blind stochastic gradient algorithms for widely linear MMSE MAI suppression for DS-CDMA

IEEE Transactions on Signal Processing
Blind multiuser detection: a subspace approach

IEEE Transactions on Information Theory
Blind adaptive multiuser detection

IEEE Transactions on Information Theory

An adaptive projected subgradient approach to learning in diffusion networks

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

We derive the steady-state performance of a common class of adaptive filters for multiple access interference (MAI) reduction in code division multiple access (CDMA) systems. The adaptive filters under study utilize estimates of the desired user's amplitude and are divided into three groups of least-mean-square (LMS) algorithms differing by the choice of the normalization factor. The steady-state performance is deduced from energy-conservation relations that include a possibly erroneous estimate of the desired user's amplitude. The analyses show that blind algorithms using information about the desired user's amplitude achieve similar performance to that of nonblind algorithms. In addition, geometric considerations reveal the conditions under which the choice of the normalization factor is expected to have great impact on the convergence properties of the algorithms. Numerical simulations show good agreement with theory.