This thesis studies offline reinforcement learning as an optimization technique for downlink link adaptation, which is one of many control loops in Radio access networks. The work studies the impact of the quality of pre-collected datasets, in terms of how much the data covers the state-action space and whether it is collected by an expert policy or not. The data quality is evaluated by training three different algorithms: Deep Q-networks, Critic regularized regression, and Monotonic advantage re-weighted imitation learning. The performance is measured for each combination of algorithm and dataset, and their need for hyperparameter tuning and sample efficiency is studied. The results showed Critic regularized regression to be the most robust because it could learn well from any of the datasets that were used in the study and did not require extensive hyperparameter tuning. Deep Q-networks required careful hyperparameter tuning, but paired with the expert data it managed to reach rewards equally as high as the agents trained with Critic Regularized Regression. Monotonic advantage re-weighted imitation learning needed data from an expert policy to reach a high reward. In summary, offline reinforcement learning can perform with success in a telecommunication use case such as downlink link adaptation. Critic regularized regression was the preferred algorithm because it could perform great with all the three different datasets presented in the thesis. / Denna avhandling studerar offline reinforcement learning som en optimeringsteknik för nedlänks länkanpassning, vilket är en av många kontrollcyklar i radio access networks. Arbetet undersöker inverkan av kvaliteten på förinsamlade dataset, i form av hur mycket datan täcker state-action rymden och om den samlats in av en expertpolicy eller inte. Datakvaliteten utvärderas genom att träna tre olika algoritmer: Deep Q-nätverk, Critic regularized regression och Monotonic advantage re-weighted imitation learning. Prestanda mäts för varje kombination av algoritm och dataset, och deras behov av hyperparameterinställning och effektiv användning av data studeras. Resultaten visade att Critic regularized regression var mest robust, eftersom att den lyckades lära sig mycket från alla dataseten som användes i studien och inte krävde omfattande hyperparameterinställning. Deep Q-nätverk krävde noggrann hyperparameterinställning och tillsammans med expertdata lyckades den nå högst prestanda av alla agenter i studien. Monotonic advantage re-weighted imitation learning behövde data från en expertpolicy för att lyckas lära sig problemet. Det datasetet som var mest framgångsrikt var expertdatan. Sammanfattningsvis kan offline reinforcement learning vara framgångsrik inom telekommunikation, specifikt nedlänks länkanpassning. Critic regularized regression var den föredragna algoritmen för att den var stabil och kunde prestera bra med alla tre olika dataseten som presenterades i avhandlingen.

Magnetic resonance images (MRI) are acquired by sampling the current of induced electromotiveforce (EMF). EMF is induced due to flux of the net magnetic field from coherent nuclear spins with intrinsic magnetic dipole moments. The spins are excited by (non-ionizing) radio frequency electromagnetic radiation in conjunction with stationary and gradient magnetic fields. These images reveal detailed internal morphological structures as well as enable functional assessment of the body that can help diagnose a wide range of medical conditions. The aim of this project was to unwrap phase contrast cine magnetic resonance images, targeting the great vessels. The maximum encoded velocity (venc) is limited to the angular phase range [-π, π] radians. This may result in aliasing if the venc is set too low by the MRI personnel. Aliased images yield inaccurate cardiac stroke volume measurements and therefore require acquisition retakes. The retakes might be avoided if the images could be unwrapped in post-processing instead. Using computer vision, the angular phase of flow measurements as well as the angular phase of retrospectively wrapped image sets were unwrapped. The performances of three algorithms were assessed, Laplacian algorithm, sequential tree-reweighted message passing and iterative graph cuts. The associated energy formulation was also evaluated. Iterative graph cuts was shown to be the most robust with respect to the number of wraps and the energies correlated with the errors. This thesis shows that there is potential to reduce the number of acquisition retakes, although the MRI personnel still need to verify that the unwrapping performances are satisfactory. Given the promising results of iterative graph cuts, next it would be valuable to investigate the performance of a globally optimal surface estimation algorithm.