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49,80 €ISBN 978-3-8440-4954-1Softcover238 pages87 figures314 g21 x 14,8 cmEnglishThesis
December 2016
Ming Li
Utility-based Resource Management for Future Mobile Communications Considering QoE
This work addresses the mobile networks, such as LTE (Long Term Evolution). The key objective is to improve the system performance in means of user satisfaction. Two main potential bottleneck links are identified, i.e. the Radio Interface and the transport network link from base station to core network.
Therefore, first a utility based radio scheduling algorithm is proposed. The scheduling algorithm, which maximizes the aggregated QoE, is proven to be optimal analytically. In a second step the resource limitation on the second bottleneck link, the S1 interface in the transport network is considered in addition. The mathematical problem is formulated considering the resource on both bottleneck link and maximizing the aggregated user satisfaction. The formulated problem is proven to be a convex optimization problem, and then solved using the Lagrangian relaxation method. In addition, computational advantageous heuristics are developed and compared in simulations against legacy approaches and the optimal solution.
Therefore, first a utility based radio scheduling algorithm is proposed. The scheduling algorithm, which maximizes the aggregated QoE, is proven to be optimal analytically. In a second step the resource limitation on the second bottleneck link, the S1 interface in the transport network is considered in addition. The mathematical problem is formulated considering the resource on both bottleneck link and maximizing the aggregated user satisfaction. The formulated problem is proven to be a convex optimization problem, and then solved using the Lagrangian relaxation method. In addition, computational advantageous heuristics are developed and compared in simulations against legacy approaches and the optimal solution.
Keywords: Mobile Communication; LTE; Resource Management; Scheduling; Traffic Shaping; Utility; Convex Optimization; Lagrangian Relaxation; QoE; QoS
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