Optimization of Computational Resources for MIMO Detection

Abstract: 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 the multiple-input multiple-output (MIMO) technology where multiple antennas are placed at both the transmitting and receiving side of the communication link. One major implementation difficulty of the MIMO technology is the signal separation (detection) problem at the receiving side of the MIMO link. 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.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. The fundamental question is, can we save computational resources by performing optimal detection only when it is needed, and something simpler when it is not? This is the question that this thesis aims to answer. In doing so, we present a general framework for adaptively allocating computational resources to different (“simple” and“difficult”) detection problems. 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.