TY - GEN
T1 - Joint Cache Placement and Delivery Design using Reinforcement Learning for Cellular Networks
AU - Amidzadeh, Mohsen
AU - Al-Tous, Hanan
AU - Tirkkonen, Olav
AU - Zhang, Junshan
N1 - Funding Information:
ACKNOWLEDGEMENT This work was funded in part by the Academy of Finland (grant 319058). We thank Prof. Ville Kyrki and his team for giving advises for the simulation matters.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - We consider a reinforcement learning (RL) based joint cache placement and delivery (CPD) policy for cellular networks with limited caching capacity at both Base Stations (BSs) and User Equipments (UEs). The dynamics of file preferences of users is modeled by a Markov process. User requests are based on current preferences, and on the content of the user's cache. We assume probabilistic models for the cache placement at both the UEs and the BSs. When the network receives a request for an un-cached file, it fetches the file from the core network via a backhaul link. File delivery is based on network-level orthogonal multipoint multicasting transmissions. For this, all BSs caching a specific file transmit collaboratively in a dedicated resource. File reception depends on the state of the wireless channels. We design the CPD policy while taking into account the user Quality of Service and the backhaul load, and using an Actor-Critic RL framework with two neural networks. Simulation results are used to show the merits of the devised CPD policy.
AB - We consider a reinforcement learning (RL) based joint cache placement and delivery (CPD) policy for cellular networks with limited caching capacity at both Base Stations (BSs) and User Equipments (UEs). The dynamics of file preferences of users is modeled by a Markov process. User requests are based on current preferences, and on the content of the user's cache. We assume probabilistic models for the cache placement at both the UEs and the BSs. When the network receives a request for an un-cached file, it fetches the file from the core network via a backhaul link. File delivery is based on network-level orthogonal multipoint multicasting transmissions. For this, all BSs caching a specific file transmit collaboratively in a dedicated resource. File reception depends on the state of the wireless channels. We design the CPD policy while taking into account the user Quality of Service and the backhaul load, and using an Actor-Critic RL framework with two neural networks. Simulation results are used to show the merits of the devised CPD policy.
KW - Actor-Critic
KW - Wireless caching
KW - cache placement and delivery policy
KW - multipoint multicast transmission
KW - neural network
KW - reinforcement learning
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U2 - 10.1109/VTC2021-Spring51267.2021.9448674
DO - 10.1109/VTC2021-Spring51267.2021.9448674
M3 - Conference contribution
AN - SCOPUS:85112401805
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
Y2 - 25 April 2021 through 28 April 2021
ER -