Deep Learning Based Resource Allocation: How Much Training Data is Needed?
Deep Learning Based Resource Allocation: How Much Training Data is Needed?
We consider artificial neural networks based energy-efficient power control for interference networks. The influence of different training set sizes and data augmentation is evaluated. It is shown that as few as 15,000 data points obtained from 300 channel realizations are sufficient to adequately predict almost globally optimal power allocations in a 4 user network. Moreover, we observe that, especially for larger scenarios, data augmentation is essential for successful training and far outweighs the effect of increasing the training data set size.
IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), p. 1
- University of Cassino Italy
- Braunschweig University of Technology Germany
- University of Cassino and Southern Lazio Italy
- University of Bremen Germany
Deep Learning, Neural Networks, Energy Efficiency, Data Augmentation, Resource Allocation, 621.3
Deep Learning, Neural Networks, Energy Efficiency, Data Augmentation, Resource Allocation, 621.3
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