| Autor: |
Ashvin Srinivasan, Mohsen Amidzadeh, Junshan Zhang, Olav Tirkkonen |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Intelligent and Converged Networks, Vol 5, Iss 2, Pp 81-99 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2708-6240 |
| DOI: |
10.23919/ICN.2024.0007 |
| Popis: |
We explore the use of caching both at the network edge and within User Equipment (UE) to alleviate traffic load of wireless networks. We develop a joint cache placement and delivery policy that maximizes the Quality of Service (QoS) while simultaneously minimizing backhaul load and UE power consumption, in the presence of an unknown time-variant file popularity. With file requests in a time slot being affected by download success in the previous slot, the caching system becomes a non-stationary Partial Observable Markov Decision Process (POMDP). We solve the problem in a deep reinforcement learning framework based on the Advantageous Actor-Critic (A2C) algorithm, comparing Feed Forward Neural Networks (FFNN) with a Long Short-Term Memory (LSTM) approach specifically designed to exploit the correlation of file popularity distribution across time slots. Simulation results show that using LSTM-based A2C outperforms FFNN-based A2C in terms of sample efficiency and optimality, demonstrating superior performance for the non-stationary POMDP problem. For caching at the UEs, we provide a distributed algorithm that reaches the objectives dictated by the agent controlling the network, with minimum energy consumption at the UEs, and minimum communication overhead. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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