Mechanism design and implementation theoretic perspective for interference coupled wireless systems
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RESEARCH
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Mechanism design and implementation theoretic perspective for interference coupled wireless systems Holger Boche1* , Siddharth Naik2 and Tansu Alpcan3
Abstract This article investigates the properties of social choice functions (SCFs) that represent resource allocation strategies for interference coupled wireless systems. The resources can be physical layer parameters such as power vectors or spatial streams. Strategy proofness and efficiency properties of SCFs are used to capture the properties of non-manipulability and Pareto optimality of resource allocation strategies, respectively. This article introduces and investigates the concepts of (strong) intuitive fairness and non-participation in interference coupled systems. The analysis indicates certain inherent limitations when designing strategy proof and efficient resource allocation strategies, if additional desirable and intuitive properties are imposed. These restrictions are investigated in an analytical mechanism design framework for interference coupled wireless systems. The article also investigates the permissible SCFs, which can be implemented by a mechanism in either Nash equilibrium or dominant strategy for utility functions representing interference coupled wireless systems. Among other results, it is shown that a strategy proof and efficient resource allocation strategy cannot simultaneously satisfy continuity and the often encountered property of non-participation. Introduction From the evolution of wireless infrastructure from second generation to third generation, there has been a gradual transition from voice centric to data centric applications. Many of these applications are quality of service (QoS) based. A QoS application typically requires users to report their channel qualities to a central controller. The vendors manufacturing end user equipment have an incentive to report a higher channel quality, than the true channel quality experienced by the user. Such a misrepresentation of the channel quality is motivated by the vendor’s intention of over provisioning for its users. There can be other instances, where the users have an incentive to misrepresent their measured channel quality or interference temperature. The result of solving a resource allocation problem with misrepresented utilities is that the outcome might not always be the one desired by the central controller, e.g., base station, operator. Such a misrepresentation of utilities can have an undesirable effect on the *Correspondence: [email protected] 1 Department of Theoretical Information Technology, Technical University of Munich, Arcisstrasse 21, 80331 Munich, Germany Full list of author information is available at the end of the article
resource allocation. Being in a position to tackle such a misrepresentation helps in better formulation of the optimization problem for radio resource allocation in wireless networks. Expecting the resource allocation strategy to be strategy proof could be one possible solution to the central controller’s dilemma of solving an o
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