In this paper, firstly, we propose several convexification and.c.ncavification transformations to convert a strictly monotone function into a convex or concave function, then we propose several convexification and.c.n...
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In this paper, firstly, we propose several convexification and.c.ncavification transformations to convert a strictly monotone function into a convex or concave function, then we propose several convexification and.c.ncavification transformations to convert a non-convex and non-concave objective function into a convex or concave function in the programmingproblems with convex or concave constraint functions, and propose several convexification and.c.ncavification transformations to convert a non-monotone objective function into a convex or concave function in some programmingproblems with strictly monotone constraint functions. Finally, we prove that the original programmingproblemcan be converted into an equivalent concave minimization problem, or reverse convex programmingproblem or canonical d.c. programming problem. Then the global optimal solution of the original problemcan be obtained by solving the converted.c.ncave minimization problem, or reverse convex programmingproblem or canonical d.c. programming problem using the existing algorithms about them.
In this paper, we investigate the Energy Efficiency (EE)- Spectrum Efficiency (SE) tradeoff issue in an OFdM-based.c.gnitive radio (cR) network. A multi-objective resource allocation problem is formulated, where we tr...
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ISBN:
(纸本)9781479935130
In this paper, we investigate the Energy Efficiency (EE)- Spectrum Efficiency (SE) tradeoff issue in an OFdM-based.c.gnitive radio (cR) network. A multi-objective resource allocation problem is formulated, where we try to maximize the EE and the SE simultaneously. The Pareto optimal set of the formulatedproblem is characterized by analyzing the relationship between the EE and the SE. To find a unique globally optimal solution, we proposed a unified EE-SE tradeoff metric, based on which the original optimization task is transformed into a single-objective problem that has a d.c. (difference of two convex functions/sets) structure. Then an efficient barrier method is developed, where we speeds up the time-consuming computation of Newton step by exploiting the structure of the d.c. programming problem. Simulation results validate the effectiveness and efficiency of the proposed algorithm. Our general problem formulation sheds some insights on how to design an energy- and spectrum-efficient cR system.
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