1 research outputs found
Optimization of multidimensional equalizers based on MMSE criteria for multiuser detection
PhD ThesisThis thesis is about designing a multidimensional equalizer for uplink
interleaved division multiple access (IDMA) transmission. Multidimensional
equalizer can be classified into centralized and decentralized
multidimensional equalizer. Centralized multidimensional equalizer
(MDE) have been used to remove both inter-symbol interference
(ISI) and multiaccess interference (MAI) effects from the received signal.
In order to suppress MAI effects, code division multiple access
(CDMA) has been used with MDE to minimize the correlation between
users' signals. The MDE structure can be designed using linear
equalizer (MLE) or decision feedback equalizer (MDFE). Previous
studies on MDE employed adaptive algorithms to estimate filter co-effi cients during the training mode, i.e. the symbol equalization was
not optimal, for two users. In our work, we applied MDE on IDMA
receiver for multipath selective fading channels and also derived new
equations to obtain the optimal filter taps for both types of MDE
equalizers, i.e. MDFE and MLE, based on the minimum mean square
error (MMSE) criterion. The optimal filter taps are calculated for
more than two users. Moreover, we investigated the performance of
the optimal MDFE using both IDMA (MDFE-IDMA) and CDMA
(MDFE-CDMA) detectors.
Generally, the MDE equalizer suffers from residual MAI interference
effects at low signal-to-noise-ratios (SNR) due to the delay inherent
in the convergence of the crossover filter taps. Therefore, a new decentralized
multidimensional equalizer has been proposed to IDMA
detector. Within design of decentralized equalizer, the convergence
problem has been resolved by replacing the crossover filters with parallel
interference canceler (PIC) for removing MAI dispersion. The
proposed decentralized multidimensional equalizer shows a higher efficiency in removing MAI interference when compared with existing
receivers in the literature. However, this is achieved at the expense
of higher computational complexity compared to centralized multidimensional
equalization