Device to device (D2D) communications are emerging as an essential part of technological solutions to boost data rates in the next generation networks. Cognitive radio (CR) opportunistically utilises spectrum to boost...
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Device to device (D2D) communications are emerging as an essential part of technological solutions to boost data rates in the next generation networks. Cognitive radio (CR) opportunistically utilises spectrum to boost spectral efficiency. CR-assisted D2D networks will bring the benefits of both D2D as well as CR together in futuristic cellular networks. This study proposes to opportunistically use TV spectrum white spaces. A joint user selection, mode assignment, and power allocation in CR-assisted D2D networks can definitely yield higher data rates. The proposed study maximises data rate together with users' selection fulfilling various users' power, base station's transmit power, quality of service, and interference related thresholds. This problem is mixed integer non-linear programming and considered non-deterministic polynomial time (NP)-complete. Due to the discrete variables in the problem, finding an optimal solution with the help of an exhaustive search algorithm (ESA) becomes very challenging. The problem gets exponentially complex with the increasing number of user pairs. Thus, the need of another method becomes imperative that yields near optimal solution. Mesh adaptive direct search (mads) algorithm is considered for solution in the CR-assisted D2D network resource management problem. Simulation results using mads yield near optimal solution confirming the suitability of mads for CR-assisted D2D networks.
During alloy and process design, it is often desired to identify regions of design or process variables for which certain calculated functions have optimal values under various constraints, for example, compositions o...
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During alloy and process design, it is often desired to identify regions of design or process variables for which certain calculated functions have optimal values under various constraints, for example, compositions of minimum liquidus temperature in an N-component alloy;compositions where the amount of precipitate in a given phase is maximized or minimized during annealing or rolling;other calculated functions such as densities, vapor pressures and viscosities;or the overall cost. The present work reports on the development of software, linked to the FactSage thermodynamic and property database system, to perform such calculations. The software uses the Mesh Adaptive Direct Search algorithm (mads) designed to solve non-smooth optimization problems for which the objectives and constraints are typically outputs of computer simulations. Numerical results for several examples are presented. (C) 2011 Elsevier Ltd. All rights reserved.
Two parallel deterministic direct search algorithms are combined to find improved parameters for a system of differential equations designed to simulate the cell cycle of budding yeast. Comparing the model simulation...
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Two parallel deterministic direct search algorithms are combined to find improved parameters for a system of differential equations designed to simulate the cell cycle of budding yeast. Comparing the model simulation results to experimental data is difficult because most of the experimental data is qualitative rather than quantitative. An algorithm to convert simulation results to mutant phenotypes is presented. Vectors of the 143 parameters defining the differential equation model are rated by a discontinuous objective function. Parallel results on a 2200 processor supercomputer are presented for a global optimization algorithm, DIRECT, a local optimization algorithm, mads, and a hybrid of the two. A second formulation is presented that uses a system of smooth inequalities to evaluate the phenotype of a mutant. Preliminary results of this formulation are given.
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