ML approaches to channel estimation for pilot-aided multirate DS/CDMA systems

This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) where the transmitted sign...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2002-03, Vol.50 (3), p.696-709
Main Authors: Mestre, X., Fonollosa, J.R.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Applied sciences
Asymptotic properties
Bit rate
Channel estimation
Channels
Code division multiple access
Code division multiplexing
Detection, estimation, filtering, equalization, prediction
Estimators
Exact sciences and technology
Information, signal and communications theory
Maximum likelihood estimation
Modulation coding
Multiaccess communication
Multiplexing
Performance analysis
Signal and communications theory
Signal, noise
Spreading
Telecommunications and information theory
Training
Wideband
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Description
Summary:This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) where the transmitted signal contains a code division multiplexed pilot for channel estimation. First, the asymptotic covariances of the training-only, semi-blind conditional maximum likelihood (CML) and semi-blind Gaussian maximum likelihood (GML) channel estimators are derived. Then, these formulas are further simplified assuming randomized spreading and training sequences under the approximation of high spreading, factors and high number of codes. The results provide a useful tool to describe the performance of the channel estimators as a function of basic system parameters such as number of codes, spreading factors, or traffic to training power ratio.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.984767