The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms non-learning schemes in terms of network throughput.

2018/10/03 · Abstract—In an RF-powered backscatter cognitive radio net- work, multiple secondary users communicate with a secondary.

Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks. T. Anh, N. Luong, D. Niyato, Y. Liang, and D. Kim.

Bibliographic details on Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks.

Deep reinforcement learning for time scheduling in RF-powered backscatter cognitive radio networks. TT Anh, NC Luong, D Niyato, YC Liang, DI Kim. 2019 IEEE ...

... Powered Cognitive Radio Network with Ambient Backscatter ... Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks.

Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e. g., decisions or actions, given their ...

2023/09/21 · [10] T. T. Anh et al., “Deep reinforcement learning for time scheduling in rf-powered backscatter cognitive radio networks,” in Proc. 2019 ...

Deep reinforcement learning is proposed to overcome the mentioned shortcomings and allow a wireless gateway to derive an optimal policy to maximize network ...

2022/09/04 · To solve the stochastic optimization problem, we then propose to employ, evaluate, and assess a Deep Reinforcement Learning (DRL) algorithm with ...