Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning

@article{Ong2019CognitiveRN,
  title={Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning},
  author={K. Ong and Y. Zhang and D. Niyato},
  journal={2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)},
  year={2019},
  pages={1-5}
}
  • K. Ong, Y. Zhang, D. Niyato
  • Published 2019
  • Computer Science, Engineering, Mathematics
  • 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)
  • Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT), requiring increased decentralization and autonomous operation. To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. However, in complex and large-scale networks, the state and action spaces are usually large, and… CONTINUE READING

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