Deep Reinforcement Learning-Based Edge Caching in Wireless Networks

Journal Article · Wed Jan 22 00:00:00 EST 2020 · IEEE Transactions on Cognitive Communications and Networking

DOI:https://doi.org/10.1109/tccn.2020.2968326· OSTI ID:1799121

Zhong, Chen ^[1]; Gursoy, M. Cenk ^[2]; Velipasalar, Senem ^[2]

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1. Syracuse Univ., NY (United States); Syracuse Univ., NY (United States)
2. Syracuse Univ., NY (United States)

With the purpose to offload data traffic in wireless networks, content caching techniques have recently been studied intensively. Using these techniques and caching a portion of the popular files at the local content servers, the users can be served with less delay. Most of the content replacement policies are based on the content popularity, that depends on the users' preferences. In practice, such information varies over time. Therefore, an approach to determine the file popularity patterns must be incorporated into caching policies. In this context, we study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. For centralized edge caching, we aim at maximizing the cache hit rate. In decentralized edge caching, we consider both the cache hit rate and transmission delay as performance metrics. These proposed frameworks are assumed to neither have any prior information on the file popularities nor know the potential variations in such information. Via simulation results, the superiority of the proposed frameworks is verified by comparing them with other policies, including least frequently used (LFU), least recently used (LRU), and first-in-first-out (FIFO) policies.

• Research Organization: Syracuse University, NY (United States)
• Sponsoring Organization: USDOE Advanced Research Projects Agency - Energy (ARPA-E)
• Grant/Contract Number: AR0000940
• OSTI ID: 1799121
• Journal Information: IEEE Transactions on Cognitive Communications and Networking, Journal Name: IEEE Transactions on Cognitive Communications and Networking Journal Issue: 1 Vol. 6; ISSN 2372-2045
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
Actor-critic algorithms
Base stations
Deep reinforcement learning
Delays
Edge caching
Feature extraction
Indexes
Reinforcement learning
Servers
Wireless networks