For the past decades, the demand in transferring large amounts of data rapidly and reliably has been increasing drastically. One of the more promising techniques that can provide the desired performance is multiple-input multiple-output (MIMO) technology where multiple antennas are placed at both the transmitting and receiving side of the communication link. This performance potential is extremely high when the dimensions of the MIMO system are increased to an extreme (in the number of hundreds or thousands of antennas). One major implementation difficulty of the MIMO technology is the signal separation (detection) problem at the receiving side of the MIMO link, which holds for medium-size MIMO systems and even more so for large-size systems. This is due to the fact that the transmitted signals interfere with each other and that separating them can be very difficult if the MIMO channel conditions are not beneficial, i.e., the channel is not well-conditioned.

The main problem of interest is to develop algorithms for practically feasible MIMO implementations without sacrificing the promising performance potential that such systems bring. These methods involve inevitably different levels of approximation. There are computationally cheap methods that come with low accuracy and there are computationally expensive methods that come with high accuracy. Some methods are more applicable in medium-size MIMO than in large-size MIMO and vice versa. Some simple methods for instance, which are typically inaccurate for medium-sized settings, can achieve optimal accuracy for certain large-sized settings that offer close-to-orthogonal spatial signatures. However, when the dimensions are overly increased, then even these (previously) simple methods become computationally burdensome. In different MIMO setups, the difficulty in detection shifts since methods with optimal accuracy are not the same. Therefore, devising one single algorithm which is well-suited for feasible MIMO implementations in all settings is not easy.

This thesis addresses the general MIMO detection problem in two ways. One part treats a development of new and more efficient detection techniques for the different MIMO settings. The techniques that are proposed in this thesis demonstrate unprecedented performance in many relevant cases. The other part revolves around utilizing already proposed detection algorithms and their advantages versus disadvantages in an adaptive manner. For well-conditioned channels, low-complexity detection methods are often sufficiently accurate. In such cases, performing computationally very expensive optimal detection would be a waste of computational power. This said, for MIMO detection in a coded system, there is always a trade-off between performance and complexity. Intuitively, computational resources should be utilized more efficiently by performing optimal detection only when it is needed, and something simpler when it is not. However, it is not clear whether this is true or not. In trying to answer this, a general framework for adaptive computational-resource allocation to different (“simple” and “difficult”) detection problems is proposed. This general framework is applicable to any MIMO detector and scenario of choice, and it is exemplified using one particular detection method for which specific allocation techniques are developed and evaluated.