This Is AuburnElectronic Theses and Dissertations

Doubly-Selective Channel Estimation and Equalization Using Superimposed Training and Basis Expansion Models




He, Shuangchi

Type of Degree



Electrical and Computer Engineering


Owing to multipath propagation and Doppler spread, typical wireless channels are both frequency- and time-selective (doubly-selective). In this dissertation, we concentrate on channel estimation and equalization over doubly-selective channels, by exploiting both superimposed training and basis expansion models (BEM). In contrast to the conventional time-multiplexed (TM) training schemes, at the transmitter, a periodic training sequence is arithmetically added at low power to the information sequence in superimposed training schemes. There is no loss in data transmission rate, but some useful power has to be allocated to superimposed training. We also employ various BEM's to describe the temporal variations of the doubly-selective channel so that the estimation of a time-varying process can be reduced to estimating fewer invariant BEM coefficients. Firstly, a channel estimator is presented using superimposed training and the first-order statistics of the observations, based on various BEM's, where information sequences act as interference in channel estimation. By using user-specific training sequences, the estimator can be extended to multiple-user systems. We next analyze the information-induced self-interference of this estimator. The performance analysis and the parameter optimizations are investigated. We propose two schemes to alleviate the self-interference in channel estimation. Using the channel estimates by the first-order statistics-based estimator as an initial guess, a deterministic maximum likelihood (DML) approach is used to jointly estimate the channel and the information sequence. Exploiting the channel estimates and the detected information data from the previous iteration, the self-interference can be significantly reduced at the present iteration. We also propose a data-dependent superimposed training scheme. The training sequence is designed based on the current information sequence so that the self-interference can be entirely eliminated at the receiver. However, total elimination of the interference may lead to information loss. We then modify the scheme to the partially-data-dependent (PDD) training, striking a compromise between interference cancelation and information integrity. Using superimposed training and a BEM, direct equalization of doubly-selective channels is also considered, without estimating the channel first. The direct equalizer is also extended to a multiple-user scenario, which can be used in a wireless ad hoc network. The proposed approaches are illustrated by computer simulation examples, and compared with conventional TM training-based approaches. When self-interference is sufficiently suppressed by our proposed schemes, the performance of superimposed training-based approaches are competitive with the ones using the conventional TM training, without incurring any data-rate loss.