Channel Estimation and Equalization for Doubly-Selective Channels Using Basis Expansion Models

The nature of the wireless channels places fundamental limitations on the performance of wireless communication systems. In addition to the frequency-selectivity characteristics caused by multipath propagation, the high-rate wireless and mobile links often exhibit time-selectivity characteristics caused by the user's mobility, so-called doubly-selective wireless channels. The quality of channel acquisition has a major impact on the overall system performance. Therefore, reliable estimation of doubly-selective channels is well motivated. Equalization is used at the receiver to compensate for intersymbol interference created by multipath propagation and improve received signal quality. Equalizers should be adaptive since the channel is time-varying. In this dissertation, channel estimation and equalization for doubly-selective channels are considered in Chapter 2 (under single input single output models) and Chapter 3 (under multiple input multiple output models), where the time-varying channel is assumed to be well described by basis expansion models (BEM). Our focus is on time-multiplexed training for channel estimation where the training symbols are periodically inserted and use all transmitted power during their transmission. The linear equalization and decision feedback equalization (DFE) of doubly-selective channels modeled via BEMs are introduced in Chapter 4. There has been much interest in designing time-variant serial finite impulse response (FIR) linear and DFE equalizers using complex exponential (CE-) BEMs for equalizers in addition to using CE-BEM for modeling the channel itself. In this dissertation we show that the Kalman filter formulation of the linear equalizer and an alternative formulation of the FIR DFE based on a CE-BEM channel model yields the same or an improved BER at a lower computational cost, without incurring the approximation error inherent in CE-BEM modeling of equalizers. In Chapter 5, an adaptive channel estimation scheme, exploiting the oversampled complex exponential basis expansion model (CE-BEM), is presented for doubly-selective channels where we track the BEM coefficients via a multiple model approach in this dissertation. We propose to use a multiple model framework where several candidate Doppler spread values are used to cover the range from zero to an upper bound, which leads to multiple CE-BEM channel models, each corresponding to an assumed value of the Doppler spread. Subsequently, the well known interacting multiple model (IMM) algorithm is used for symbol detection based on multiple state-space models corresponding to the multiple estimated channels. Orthogonal Frequency-Division Multiplexing (OFDM), a digital multi-carrier modulation scheme, has developed into a popular scheme for wideband wireless communication due to its high spectral efficiency and simple equalization. We extend the optimum time-multiplexed training based channel estimation introduced in Chapter 2 to OFDM systems under doubly-selective channels in Chapter 6. Compared to the traditional frequency-domain training design, the main advantages of time-domain training for OFDM system is that the information symbols are not contaminated by the training symbols as in the frequency-domain training case.