DSTC designs with high diversity and coding gains and efficient detection and code matrices optimization algorithms in cooperative MIMO networks are proposed in this thesis. Firstly, adaptive power allocation (PA) algorithms with different criteria for a cooperative MIMO system equipped with DSTC schemes are proposed and evaluated. Linear receive filter and maximum likelihood (ML) detection are considered with amplify-and-forward (AF) and decode-and-forward (DF) cooperation strategies. In the proposed algorithms, the elements in the PA matrices are optimized at the destination node and then transmitted back to the relay nodes via a feedback channel. Linear minimum mean square error (MMSE) receive filter expressions and the PA matrices depend on each other and are updated iteratively. Stochastic gradient (SG) algorithms are developed with reduced detection complexity. Secondly, an DSTC scheme is proposed for two-hop cooperative MIMO networks. An adjustable code matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. The effects of the limited feedback and the feedback errors are assessed. An upper bound on the pairwise error probability analysis is derived and indicates the advantage of employing the adjustable code matrices at the relay nodes. An alternative optimization algorithm for the adaptive DSTC scheme is also derived in order to eliminate the need for feedback. Thirdly, an adaptive delay-tolerant DSTC (DT-DSTC) scheme is proposed for two-hop cooperative MIMO networks. An ML receiver and adjustable code matrices are considered for different DSTC configuration schemes subject to a power constraint with a DF cooperation strategy. An upper bound on the pairwise error probability and rank criteria analysis are derived and indicates the advantage of the proposed coding algorithm. Adaptive DT-DSTC algorithms are extended to the cooperative MIMO systems using an AF strategy and opportunistic relaying algorithms in order to achieve a delay-tolerant coding scheme combined with the optimal power allocation strategies
