By predicting changes in service requirements and the evolution of network states, anticipatory networking aims to improve the performance of decision making and optimization algorithms. In this paper, we propose a reliability-aware scheduling model which exploits information about the upcoming channel states of users to proactively optimize resource allocation. Along with the optimal formulation, proven to be NP- hard, a heuristic is also presented. The evaluation of the proposed optimization model and its corresponding heuristic is carried out considering both perfect and imperfect prediction of the users' channel states. Numerical results show that both models are robust against errors and that the decrease in reliability, when the length of the prediction window is equal to 25 ms, is less than 10%.


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    Title :

    Minimizing the Impact of Prediction Errors during Anticipatory Resource Allocation


    Contributors:


    Publication date :

    2018-06-01


    Size :

    198030 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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