Technical Program

Paper Detail

Paper IDL.1.4
Paper Title An Efficient Neural Network Architecture for Rate Maximization in Energy Harvesting Downlink Channels
Authors Heasung Kim, Taehyun Cho, Jungwoo Lee, Seoul National University, Korea (South); Wonjae Shin, Pusan National University, Korea (South); Harold Vincent Poor, Princeton University, United States
Session L.1: Application-Specific Learning
Presentation Lecture
Track Statistics and Learning Theory
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
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.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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