Algorithms for Space-Time Equalization of Wireless Channels

In this thesis we investigate receiver techniques for maximum likelihood (ML) joint channel/data estimation in flat fading multipleinput multiple-output (MIMO) channels. The performance of iterative least squares (LS) for channel estimation combined with sphere decoding (SD) for data detection is examined for block fading channels, demonstrating the data efficiency provided by the semi-blind approach. The case of continuous fading channels is addressed with the aid of recursive least squares (RLS). The observed relative robustness of the ML solution to channel variations is exploited in deriving a block QR-based RLS-SD scheme. For the multi-user MIMO scenario, the gains from exploiting temporal/spatial interference color are assessed. We also derive the optimal training sequence for ML channel estimation in the presence of co-channel interference (CCI). In the second part of the thesis we propose two new adaptive equalizers for direct sequence code division multiple access (DS-CDMA) systems operating over time-varying and frequency selective channels. The equalizers consist of a number of serially connected stages and detect users in an ordered manner, applying a decision feedback equalizer (DFE) at each stage. Both the equalizer filters and the order in which the users are extracted are updated in a RLS manner, efficiently realized through timeand order-update recursions.