Resource Allocation under Uncertainty : Applications in Mobile Communications

• Main Author: Johansson, Mathias
• Format: Doctoral Thesis
• Language: English
• Published: Uppsala universitet, Signaler och System 2004
• Subjects: Signalbehandling; resource allocation; uncertainty; probability theory as logic; scheduling; multiuser diversity; Jaynes; maximum entropy; Bayesian probability theory; Signal processing
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4559; http://nbn-resolving.de/urn:isbn:91-506-1770-2

This thesis is concerned with scheduling the use of resources, or allocating resources, so as to meet future demands for the entities produced by the resources. We consider applications in mobile communications such as scheduling users' transmissions so that the amount of transmitted information is maximized, and scenarios in the manufacturing industry where the task is to distribute work among production units so as to minimize the number of missed orders. The allocation decisions are complicated by a lack of information concerning the future demand and possibly also about the capacities of the available resources. We therefore resort to using probability theory and the maximum entropy principle as a means for making rational decisions under uncertainty. By using probabilities interpreted as a reasonable degree of belief, we find optimum decision rules for the manufacturing problem, bidding under uncertainty in a certain type of auctions, scheduling users in communications with uncertain channel qualities and uncertain arrival rates, quantization of channel information, partitioning bandwidth between interfering and non-interfering areas in cellular networks, hand-overs and admission control. Moreover, a new method for making optimum approximate Bayesian inference is introduced. We further discuss reasonable optimization criteria for the mentioned applications, and provide an introduction to the topic of probability theory as an extension to two-valued logic. It is argued that this view unifies a wide range of resource-allocation problems, and we discuss various directions for further research.