Since our simulation study estimated supertrees from binary source trees, our correlation analysis also shows that optimizing Sum-RF is not likely to be the best strategy, except for dense scaffold datasets, and thus suggests that the use of RF distance metric within the ML supertree approach proposed by Steel and Rodrigo may not be appropriate.
This last terms may result in a different solution for the optimal block-size or block-sizes that optimize the sum-correlation function than the solution for the case with no CFO.
More specifically, we focus on choosing a set of jobs to reject and constructing a schedule for the remaining jobs so as to optimize the sum of the weighted completion times of the jobs scheduled plus the sum of the penalties of the jobs rejected.
In this case however, we have to optimize the sum rate and cannot optimize the rate for each user.
In the following discussion, we optimize the sum rate based on two different criteria in the distributed MIMO switching network.
Now, we optimize the sum-throughput of nodes in the CP by maximizing the probability of being in the non-collision transmission states.
(mathcal {OP}_{2a}): Optimize the sum rate by varying SC assignment (mathrm {delta }^{l}_{k}) when fixing power allocation (P_{mathrm {s}}^{l}) denoted as (tilde {P}_{mathrm {s}}^{l}).
Then, to provide the SUs with satisfactory quality of service (QoS), and optimize the sum rate of the MCR system as well, a constrained two-variable nonlinear optimization problem (OP) is formulated.
(mathcal {OP}_{2b}): Optimize the sum rate by varying power allocation (P_{mathrm {s}}^{l}) when fixing SC assignment (mathrm {delta }^{l}_{k}) denoted as (left ({delta ^{l}_{k}}right)^), respectively.
This means that we do not attempt to jointly optimize the sum rate of all users; notice, though, that for single-stream beamforming, it has been shown in [62, 63] that such an approach can still achieve the maximal sum-rate, provided the leakage constraints are appropriately selected.
