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Thus, we develop a mode adaptation approach at the FBSs to achieve better performance.
We also showed that a simple localized binary choice at each FBS can provide good performance for both the macrocellular and femtocellular users, and nearly achieve the upper bound on the performance of the mode adaptation approach.
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Here, we develop a MIMO mode adaptation technique combining these two approaches to adapt the precoding mode at the FBSs and improve the system performance.
With localized mode adaptation, the tuning factor is chosen independently at each FBS, while in the global approach, the same tuning factor is applied for all the FBSs.
The second adaptation approach tracks the operating channel quality, so that the most efficient modulation mode can be invoked while guaranteeing a target QoS.
We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications.
We have employed a supervised adaptation approach.
The simplest form of mode adaptation at the FBSs is to apply binary MA.
In the HEQ-MA with mean and variance adaptation approach, we used an SNR-dependent covariance model adaptation technique.
We show that allowing for mode adaptation at each FBS improves the system performance when compared with using the same mode across the system, and a simple binary choice at each FBS can nearly achieve the optimum mode-adaptation performance.
The authors advocate a multifaceted adaptation approach that uses engineering and existing ecosystems.
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