Abstract
Reinforcement learning (RL) has been used in combination with cooperative caching to deal with growing traffic in mobile networks, but the performance of RL based caching policies depends heavily on network settings. This paper investigates the impact of access delays within network infrastructures and popularity and similarity properties of the contents requested on network performance. A deep Q-network based caching framework is established in both basic and extended cooperative edge networks. Our simulation results reveal explicit relationships between the performance and influential parameters, which can provide a guidance and benchmark for the design of effective caching polices with RL and cooperation technologies.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.