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\begin{document}

\title{Resource allocation of fog wireless access network based on deep
reinforcement learning}



\author[1]{Jingru Tan}%
\author[2]{Wenbo Guan}%
\affil[1]{Xi'an University of Posts and Telecommunications}%
\affil[2]{Xidian University}%


\vspace{-1em}



  
  \date{May 16, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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