546 research outputs found

    Semidefinite approximation for mixed binary quadratically constrained quadratic programs

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    Motivated by applications in wireless communications, this paper develops semidefinite programming (SDP) relaxation techniques for some mixed binary quadratically constrained quadratic programs (MBQCQP) and analyzes their approximation performance. We consider both a minimization and a maximization model of this problem. For the minimization model, the objective is to find a minimum norm vector in NN-dimensional real or complex Euclidean space, such that MM concave quadratic constraints and a cardinality constraint are satisfied with both binary and continuous variables. {\color{blue}By employing a special randomized rounding procedure, we show that the ratio between the norm of the optimal solution of the minimization model and its SDP relaxation is upper bounded by \cO(Q^2(M-Q+1)+M^2) in the real case and by \cO(M(M-Q+1)) in the complex case.} For the maximization model, the goal is to find a maximum norm vector subject to a set of quadratic constraints and a cardinality constraint with both binary and continuous variables. We show that in this case the approximation ratio is bounded from below by \cO(\epsilon/\ln(M)) for both the real and the complex cases. Moreover, this ratio is tight up to a constant factor

    Joint Downlink Base Station Association and Power Control for Max-Min Fairness: Computation and Complexity

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    In a heterogeneous network (HetNet) with a large number of low power base stations (BSs), proper user-BS association and power control is crucial to achieving desirable system performance. In this paper, we systematically study the joint BS association and power allocation problem for a downlink cellular network under the max-min fairness criterion. First, we show that this problem is NP-hard. Second, we show that the upper bound of the optimal value can be easily computed, and propose a two-stage algorithm to find a high-quality suboptimal solution. Simulation results show that the proposed algorithm is near-optimal in the high-SNR regime. Third, we show that the problem under some additional mild assumptions can be solved to global optima in polynomial time by a semi-distributed algorithm. This result is based on a transformation of the original problem to an assignment problem with gains log(gij)\log(g_{ij}), where {gij}\{g_{ij}\} are the channel gains.Comment: 24 pages, 7 figures, a shorter version submitted to IEEE JSA