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The main objective of cross-layer scheduling and resource allocation algorithms over a wireless network is the establishment of effective policies able to optimize metrics related to spectral/energy efficiency and fairness, while satisfying prescribed QoS constraints.
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As discussed above, since there are tradeoffs among these optimization metrics, it is not possible to get all of them optimized simultaneously.
The exact partitioning of multipath is used to optimize performance metrics such as energy balance and network lifetime.
However, most equalizers for these systems optimize proxy metrics that are easy to use but are suboptimal in terms of BER.
Although these protocols attempt to optimize different metrics, in general, they select a path from source to destination prior to injecting packets to the network and then forward data through that predetermined route.
S-IDNC is researched in [4], where it is proved that finding the coding process that would minimize the average decoding delay is computationally NP-hard, leading the authors to propose new heuristic S-IDNC coding algorithms in order to optimize some metrics of interest for the overall coding/decoding process.
In such, this precoding scheme, the covariance matrices of the transmitted signals, or equivalently source and relay precoders, are designed to optimize performance metrics such as the mutual information (MI) between the source and the destination or the mean-squared error (MSE) of the detected symbols.
Most of the works focus on optimizing QoS metrics, such as throughput and delay.
You do all you can to impress prospective investors by honing the story, optimizing key metrics and making your company look as attractive as possible.
However, we argue that finding the best clusters optimizing such metrics should not be treated separately from resource scheduling, since for example the lack of resources from a clustered TP would render that TP useless in the serving CoMP set.
A traditional discriminative approach, not premised on any generative model, involves the computation of hyperplanes that partition the clusters of known class instances from one another (optimizing certain metrics involving in-class and between-class variance); new instances are assigned to the class into whose partition they fall.
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