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Similarly, the long-term average utility maximization is formulated as an optimal infinite-horizon control problem.
The joint constrained optimization problem for power minimization and capacity maximization is formulated and solved in Section 5.
Throughput maximization is formulated as a linear programming problem and the optimal solutions are provided in each case.
For instance, in [14], the power control problem for rate maximization is formulated as a convex problem and its solution is found via geometric programming (GP) for wireless networks.
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In [24], a multiple-antenna base station used the NOMA approach to serve two multiple-antenna users simultaneously, where the problem of throughput maximization was formulated and two algorithms were proposed to solve the optimization problem.
The throughput maximization problem is formulated as a multiconstraint optimization problem and then expressed as a weighted throughput maximization problem under power and QoS weight constraints that nicely relate the fairness.
In [24], a weighted sum-rate maximization problem is formulated for CoMP system powered by hybrid energy sources.
The corresponding weighted Laplacian energy maximization problem is formulated as flight route addition problem to facilitate practical operations.
In the first game, the suppliers' profit maximization problem is formulated by applying supply function bidding mechanism.
The short-term throughput, i.e., the amount of data transmitted during a finite time period, maximization problem is formulated in [14,15].
After an orthogonal subcarrier allocation, the secondary rate maximization problem is formulated as a nonlinear problem and an iterative algorithm is proposed using waterfilling and dual decomposition methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com