Asymptotic performance of ML channel estimators in WCDMA systems: randomized codes approach

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) with high spreading factors, where the transmitted signal contains a code division multiplexed pilot for channel estimation purposes. Assuming randomized training and code sequences, we derive and compare the asymptotic covariances of the training-only (TO), semi-blind conditional ML (CML) and semi-blind Gaussian ML (GML) channel estimators