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To optimize the joint subcarrier and power allocation with low complexity, we divide the optimization problem into two simpler subproblems.
This paper develops a parallel dynamic programming algorithm to optimize the joint operation of a multi-reservoir system.
The null space of the relative Jacobian is used to optimize the joint trajectories by minimizing the system compliance factor.
Notice that this is different from the approach employed in cooperative sensor network, where one must design waveforms so as to optimize the joint performance of the system [15 17].
To improve the system performance compared to time sharing, we can optimize the joint probability distribution and symbol positions of the superimposed modulations or consider the general case of superposition coding.
It is therefore important to optimize the joint use of the three sources of soil data that can be used as inputs of DSM models, namely spatial sets of measured sites, soil maps and soil sensing products.
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For optimizing the joint optimization problem over K vectors in SOCP framework using an interior point method, the arithmetic complexity is ({mathrm {O}}left ({{K^{3}}K_{theta }^{3}} right)), where K θ is the number of potential directions [7,19].
This latter was predicted numerically by optimizing the joint congruence.
For superposition modulation, optimizing the joint distribution of probability P UX provides often significant shaping gains.
If we increase the complexity by optimizing the joint probability distribution P UX, we obtain 35% more bit rate for user 2 comparing to superposition modulation with uniform P UX.
The designer-interdictor bi-level problem, therefore, optimizes the joint location and protection decisions with respect to the conditional value-at-risk.
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