# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
## 2 System Model and Problem Formulation
### Problem Formulation
#### 2.1.2 Enhanced Gradient
Although the proficiency of RBMs in exploring the latent features in an unsupervised manner, its training is challenging as the stochastic gradient tends to high variance and diverging behavior, and the learning rate must be manually set according to the RBMs trained structure. The problem of having meaningless hidden neurons during the training of RBMs is intense. To overcome these issues, a novel algorithm is proposed that uses an adaptive learning rate alongside with an enhanced gradient. The enhanced gradient is used to speed up the whole learning of hidden neurons, contrary to the traditional gradient decent. Furthermore, performance comparisons with SVM and ANN algorithms are provided to demonstrate the validity and gains of the proposed algorithm, where the obtained results have revealed that the GBRBM AE outperforms the other algorithms with a powerful optimization capability. Conceptualization, O.A. ; Methodology, A.A.-G.; Formal analysis, A.Y.S. and H.A.-H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest. ## References

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