This paper investigates a method to distributively solve a Network Utility Maximization (NUM) problem with coupled variables and applies it to study power control in wireless sensor networks (WSNs). We present a dual ...
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This paper investigates a method to distributively solve a Network Utility Maximization (NUM) problem with coupled variables and applies it to study power control in wireless sensor networks (WSNs). We present a dual decomposition-based consistency price algorithm to solve the coupled problem. However, the consistency price algorithm suffers from slow convergence. We then propose a two-step method to address the given issue. The first step is to build up a global consensus problem by introducing slack variables to transform the NUM problem with globally coupled variables into a NUM problem with coupled constraints. The second step is to design a distributed algorithm that combines the first-order gradient/subgradient method and a local consensus algorithm to solve the global consensus problem. The proposed algorithm is a primaryalgorithm which has faster convergence speed than the consistency price algorithm which is a primary-dual algorithm. Experimental results have demonstrated the effectiveness of our proposed approach.
This letter proposes a method based on primary-dual iteration to address the array pattern synthesis problem of minimizing the peak sidelobe level (PSL) under the constraints of controlled mainlobe ripples and dynamic...
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This letter proposes a method based on primary-dual iteration to address the array pattern synthesis problem of minimizing the peak sidelobe level (PSL) under the constraints of controlled mainlobe ripples and dynamic range ratio (DRR) of current excitations. Especially, when DRR = 1, the phase-only beampattern synthesis problem is solved. Simulation examples are presented to verify the effectiveness of the proposed method in terms of PSL and DRR.
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