Ultra Wideband OFDM Systems: Channel Estimation and Improved dtection Accounting for Estimation Inaccuracies

The aim of this thesis is to study the problem of iterative data detection in a realistic wireless communication system, where the receiver disposes only of an imperfect (and possibly poor) estimate of the unknown channel parameters. The application scenarios on which we focus are single- and multi-antenna OFDM systems working over ultra wideband (UWB) channels. First, we propose an efficient receiver jointly estimating the channel and the transmitted symbols in an iterative manner. This receiver is based on a wavelet representation of the unknown channel and exploits the sparseness property of UWB channels in the wavelet domain to reduce the receiver's computational complexity. Second, we rely on the statistics characterizing the quality of the channel estimation as a mean to integrate the imperfect channel knowledge into the design of iterative receivers. In this way, we formulate an improved maximum likelihood (ML) detection metric taking into account the presence of channel estimation errors. We propose a modified iterative detector based on maximum a posteriori (MAP) which mitigates the effect of channel uncertainty on the detector performance, by an appropriate use of this metric. The results are compared to those obtained by using a classical detector based on a mismatched ML metric, which uses the channel estimate as if it was the perfect channel. The influence of the constellation labeling is also experimentally studied. Furthermore, we calculate the achieved throughputs associated to both improved and mismatched ML detectors, in terms of maximal achievable outage rates. Our results may serve to evaluate the trade-off between the required quality of service (in terms of BER and achieved throughputs) and the system parameters (e.g., power allocated to pilot and data symbols, number of pilots per frame, number of decoding iterations, outage probability) in the presence of channel estimation errors. Finally, we propose an improved low-complexity iterative detector based on soft parallel interference cancellation and linear minimum mean-square error (MMSE) filtering. This receiver takes into account the presence of channel estimation errors in the formulation of the linear MMSE filter, as well as in the interference cancellation part. The important point is that the performance improvements reported in this thesis are obtained while imposing practically no additional complexity to the receiver.