Publications
Detailed Information
An Efficient Neural Network Architecture for Rate Maximization in Energy Harvesting Downlink Channels
Cited 0 time in
Web of Science
Cited 0 time in Scopus
- Authors
- Issue Date
- 2020-07
- Publisher
- IEEE
- Citation
- 2020 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), pp.2498-2503
- Abstract
- This paper deals with the power allocation problem for achieving the upper bound of sum-rate region in energy harvesting downlink channels. We prove that the optimal power allocation policy that maximizes the sum-rate is an increasing function for harvested energy, channel gains, and remaining battery, regardless of the number of users in the downlink channels. We use this proof as a mathematical basis for the construction of a shallow neural network that can fully reflect the increasing property of the optimal policy. This scheme helps us to avoid using big neural networks which requires huge computational resources and causes overfitting. Through experiments, we reveal the inefficiencies and risks of deep neural network that are not optimized enough for the desired policy, and shows that our approach learns a robust policy even with the severe randomness of environments.
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.