4,019 research outputs found

    Optimal Cell Clustering and Activation for Energy Saving in Load-Coupled Wireless Networks

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    Optimizing activation and deactivation of base station transmissions provides an instrument for improving energy efficiency in cellular networks. In this paper, we study optimal cell clustering and scheduling of activation duration for each cluster, with the objective of minimizing the sum energy, subject to a time constraint of delivering the users' traffic demand. The cells within a cluster are simultaneously in transmission and napping modes, with cluster activation and deactivation, respectively. Our optimization framework accounts for the coupling relation among cells due to the mutual interference. Thus, the users' achievable rates in a cell depend on the cluster composition. On the theoretical side, we provide mathematical formulation and structural characterization for the energy-efficient cell clustering and scheduling optimization problem, and prove its NP hardness. On the algorithmic side, we first show how column generation facilitates problem solving, and then present our notion of local enumeration as a flexible and effective means for dealing with the trade-off between optimality and the combinatorial nature of cluster formation, as well as for the purpose of gauging the deviation from optimality. Numerical results demonstrate that our solutions achieve more than 60% energy saving over existing schemes, and that the solutions we obtain are within a few percent of deviation from global optimum.Comment: Revision, IEEE Transactions on Wireless Communication

    Fast quantum subroutines for the simplex method

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    We propose quantum subroutines for the simplex method that avoid classical computation of the basis inverse. For an m×nm \times n constraint matrix with at most dcd_c nonzero elements per column, at most dd nonzero elements per column or row of the basis, basis condition number κ\kappa, and optimality tolerance ϵ\epsilon, we show that pricing can be performed in O~(1ϵκdn(dcn+dm))\tilde{O}(\frac{1}{\epsilon}\kappa d \sqrt{n}(d_c n + d m)) time, where the O~\tilde{O} notation hides polylogarithmic factors. If the ratio n/mn/m is larger than a certain threshold, the running time of the quantum subroutine can be reduced to O~(1ϵκd1.5dcnm)\tilde{O}(\frac{1}{\epsilon}\kappa d^{1.5} \sqrt{d_c} n \sqrt{m}). The steepest edge pivoting rule also admits a quantum implementation, increasing the running time by a factor κ2\kappa^2. Classically, pricing requires O(dc0.7m1.9+m2+o(1)+dcn)O(d_c^{0.7} m^{1.9} + m^{2 + o(1)} + d_c n) time in the worst case using the fastest known algorithm for sparse matrix multiplication, and O(dc0.7m1.9+m2+o(1)+m2n)O(d_c^{0.7} m^{1.9} + m^{2 + o(1)} + m^2n) with steepest edge. Furthermore, we show that the ratio test can be performed in O~(tδκd2m1.5)\tilde{O}(\frac{t}{\delta} \kappa d^2 m^{1.5}) time, where t,δt, \delta determine a feasibility tolerance; classically, this requires O(m2)O(m^2) time in the worst case. For well-conditioned sparse problems the quantum subroutines scale better in mm and nn, and may therefore have a worst-case asymptotic advantage. An important feature of our paper is that this asymptotic speedup does not depend on the data being available in some "quantum form": the input of our quantum subroutines is the natural classical description of the problem, and the output is the index of the variables that should leave or enter the basis.Comment: Added discussion on condition number and infeasibilitie

    Efficient Methods for Automated Multi-Issue Negotiation: Negotiating over a Two-Part Tariff

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    In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win-win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win-win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy
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