期刊
IEEE COMMUNICATIONS LETTERS
卷 23, 期 10, 页码 1773-1777出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2019.2931688
关键词
Content update; Markov decision process; deep reinforcement learning; cache hit rate; long-term reward
资金
- National Key RD Program [2018YFB1004800]
- National Natural Science Foundation of China [61872184, 61727802]
- Singapore Ministry of Education Academic Research Fund Tier 2 [MOE2016-T2-2-054]
- SUTD-ZJU Grant [ZJURP1500102]
This letter studies a basic wireless caching network, where a source server is connected to a cache-enabled base station (BS) that serves multiple requesting users. A critical problem is how to improve cache hit rate under dynamic content popularity. To solve this problem, the primary contribution of this letter is to develop a novel dynamic content update strategy with the aid of deep reinforcement learning. Considering that the BS is unaware of content popularities, the proposed strategy dynamically updates the BS cache according to the time-varying requests and the BS cached contents. Toward this end, we model the problem of cache update as a Markov decision process and put forth an efficient algorithm that builds upon the long short-term memory network and external memory to enhance the decision making ability of the BS. Simulation results show that the proposed algorithm can achieve not only a higher average reward than deep Q-network but also a higher cache hit rate than the existing replacement policies, such as the least recently used, first-in first-out, and deep Q-network-based algorithms.
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