Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 26, Issue 10, Pages 1451-1455Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2019.2932193
Keywords
Caching; F-RAN (Fog RAN); machine learning; reinforcement learning; X-haul
Categories
Funding
- National Research Foundation through the Ministry of Science, ICT, and Future Planning (MSIP), Korean Government [2017R1A2B3012316]
- European Research Council (ERC) under the European Union [725731]
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We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Uncached requested files can he transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this letter proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL.
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