Adaptive Precoding and Resource Allocation in Cognitive Radio Networks

In this thesis, we develop efficient resource allocation and adaptive precoding schemes for two scenarios: multiuser MIMO-OFDM and multiuser MIMO based CR networks. The aim of the adaptive precoding is to squeeze more efficiency in the low SNR regime. In the context of the multiuser MIMO-OFDM CR network, we have developed resource allocation and adaptive precoding schemes for both the downlink (DL) and uplink (UL). The proposed schemes are characterized by both computational and spectral efficiencies. The adaptive precoder operates based on generating countable degrees of freedom (DoF) by combining the spaces of the block interference channel. The resource allocation has been formulated as a sum-rate maximization problem subject to the upper-limit of total power and interference at primary user constraints.
The variables of the problem were matrix of the precoding and integer indicator of the subcarrier mapping. The formulated optimization problem is a mixed integer programming having a combinatorial complexity which is hard to solve, and therefore we separated it into a two-phase procedure to elaborate computational efficiency: Adaptive precoding (DoF assignment) and subcarrier mapping. Concerning the multiuser MIMO CR based network, we develop an adaptive non-iterative linear precoder, namely Adaptive Minimum mean square error Block diagonalization (AMB). The proposed AMB precoder employs the proposed DoF concept which we call it here precoding diversity. In this context, DoFs of the proposed precoder are generated by space combining and channel path combining methods. We have also developed adaptive Zero-Forcing Block diagonalization (AZB) engaging the precoding diversity concept. The proposed AMB precoder illustrate notable gains over the state-of-the-art.