Dynamic Provider Selection & Power Resource Management in Competitive Wireless Communication Markets
By a News Reporter-Staff News Editor at Investment Weekly News -- Investigators publish new report on Machine Learning. According to news reporting originating from Zografos, Greece, by VerticalNews correspondents, research stated, “In this paper the combined problem of Wireless Internet Service Provider (WISP) selection by the mobile customers and corresponding power allocation is treated, in order to meet user expectations and satisfaction in a competitive wireless communication market with several co-existing WISPs. Each WISP is characterized by a price and service-based reputation, formed based on its adopted pricing policy and its success to satisfy customers’ Quality of Service (QoS) prerequisites, the latter implicitly characterizing the specific WISP’s market penetration factor.”
Our news editors obtained a quote from the research from the National Technical University of Athens, “The customers who act as learning automata selecting the most appropriate WISP adopt a machine learning based mechanism. The optimal power allocation is concluded from the maximization problem of each user’s utility function, which is confronted as a non-cooperative game among users and its Nash equilibrium is determined. The output of the resource allocation problem feeds the learning system in order to build knowledge and conclude to the optimal provider selection. A two-stage iterative algorithm is proposed in order to realize the machine learning provider selection and the distributed resource allocation.”
According to the news editors, the research concluded: “The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated.”
For more information on this research see: Dynamic Provider Selection & Power Resource Management in Competitive Wireless Communication Markets. Mobile Networks & Applications , 2018;23(1):86-99. Mobile Networks & Applications can be contacted at: Springer, Van Godewijckstraat 30, 3311 Gz Dordrecht, Netherlands.
The news editors report that additional information may be obtained by contacting P. Vamvakas, National Technical University of Athens, Sch Elect & Comp Engn, Zografos 15780, Greece. Additional authors for this research include E.E. Tsiropoulou and S. Papavassiliou.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1007/s11036-017-0885-y. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-07), Researchers from National Technical University of Athens Detail New Studies and Findings in the Area of Machine Learning (Dynamic Provider Selection & Power Resource Management in Competitive Wireless Communication Markets), Investment Weekly News, 566, ISSN: 1945-8185, BUTTER® ID: 015423416
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