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Deep Learning-based Implicit CSI Feedback in Massive MIMO
By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks.
Scalable Bayesian Optimization with Sparse Gaussian Process Models
This thesis focuses on Bayesian optimization with the improvements coming from two aspects:(i) the use of derivative information to accelerate the optimization convergence; and (ii) the consideration of scalable GPs for handling massive data.
Sparse Spectrum Gaussian Process for Bayesian Optimization
Since the balance between predictive mean and the predictive variance is the key determinant to the success of Bayesian optimization, the current sparse spectrum methods are less suitable for it.
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