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Resource allocation of fog wireless access network based on deep reinforcement learning
  • Jingru Tan,
  • Wenbo Guan
Jingru Tan
Xi'an University of Posts and Telecommunications

Corresponding Author:[email protected]

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Wenbo Guan
Xidian University
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Aiming at the problem of huge energy consumption in the Fog Wireless Access Networks (F-RANs), the resource allocation scheme of the F-RAN architecture under the cooperation of renewable energy is studied in this paper. Firstly, the transmission model and Energy Harvesting (EH) model are established, the solar energy harvester is installed on each Fog Access Point (F-AP), and each F-AP is connected to the smart grid. Secondly, the optimization problem is established according to the constraints of Signal to Noise Ratio (SNR), available bandwidth and energy harvesting, so as to maximize the average throughput of F-RAN architecture with hybrid energy sources. Finally, the dynamic power allocation scheme in the network is studied by using Q-learning and Deep Q Network (DQN) respectively. Simulation results show that the proposed two algorithms can improve the average throughput of the whole network compared with other traditional algorithms.
31 Oct 2021Submitted to Engineering Reports
01 Nov 2021Submission Checks Completed
01 Nov 2021Assigned to Editor
06 Nov 2021Reviewer(s) Assigned
17 Dec 2021Editorial Decision: Revise Major
18 Dec 20211st Revision Received
20 Dec 2021Submission Checks Completed
20 Dec 2021Assigned to Editor
20 Dec 2021Reviewer(s) Assigned
06 Jan 2022Editorial Decision: Accept