Identification de canal et égalisation aveugles et semi-aveugles pour les communications mobiles

de Carvalho, Elisabeth

Most of the present mobile communication standards include a training sequence to estimate the channel. Blind techniques allow the estimation of the channel without requiring training symbols, thus increasing bandwidth efficiency, but lack from robustness. The purpose of semi-blind equalization is to exploit the blind information as well as the information coming from the known symbols. Semi{blind techniques robustify the blind problem and allow the estimation of longer impulse responses than possible with a certain training sequence length; for a desired estimation quality, they also allow the use of shorter training sequences.
Furthermore, they offer better performance than blind and training methods.
We present identifiability conditions for semi-blind FIR multichannel estimation: semi-blind methods are able to estimate any channel, even when the position of the known symbols in the burst is arbitrary. Performance bounds for semi{blind multichannel estimation are provided through the analysis of Cramer-Rao bounds and a comparison of semi{blind techniques with blind and training sequence based techniques is done. A study on performance under constraints is proposed to characterize blind performance.
The proposed semi{blind methods are mainly based on Maximum-Likelihood which can incorporate the knowledge of input symbols. For grouped known symbols, suboptimal criteria appear as a linear combination of a training sequence based criterion and the blind ML criterion. In order to build powerful semi{blind ML methods, we also focus on the study of blind ML methods. At last, we present methods that combine a blind criterion with a training sequence based criterion. Receiver structures are also presented. The structure of the burst mode equalizers are studied and especially the structure of the ISI canceller that we call Non-Causal Decision Feedback Equalizer (NCDFE): an implementation of the NCDFE is proposed based on soft decisions. At last, performance bounds on Maximum Likelihood Sequence Estimation (MLSE) are given when the channel order is underestimated.

Systèmes de Communication
Eurecom Ref:
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