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We find that, relative to common competitors, the robust allocation schemes can result in significant decreases in the mean squared error when the fitted models are biased, at a minimal cost in efficiency when in fact the fitted models are correct.
The current study was conducted within a clinical-trials framework, including prospective registration, independent randomization and robust allocation concealment, intention-to-treat principles for our main analyses, and blinding of outcome assessors (Moher et al., 2010).
In terms of internal validity, the strengths of the SoLID trial are that it is prospective and randomised, with robust allocation concealment and analysis of cardiac MRI data in duplicate by two blinded independent analysts who will remain unaware of the allocation of participants for the duration of the trial.
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The focus of this paper is the design of a static heuristic that: (a) determines a robust resource allocation, i.e., a resource allocation that maximizes the allowable increase in workload until a run-time reallocation of resources is required to avoid a QoS violation, and (b) has a very low failure rate (i.e., the percentage of instances a heuristic fails).
We address the robust resource allocation problem with two steps, i.e., relay selection and power allocation separately.
The iterations are repeated until the robust power allocation process converges.
To reduce communication cost, an effective and robust resource allocation scheme is required.
In the sequel, we optimize the robust power allocation problem after obtaining the robust relay selection factor.
In this section, simulation results are presented to assess the performance of our proposed multi-objective robust resource allocation scheme.
Finally, from the derived approximate optimal solutions, the robust resource allocation schemes which balance UL and DL power consumption are proposed.
In [21], robust resource allocation problem for cooperative CRNs is optimized with the channel uncertainties between SU and SU, SU and PU.
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