Name
Title | ||
|---|---|---|
Multi-Agent Cooperative Alternating Q-Learning Caching in D2D-Enabled Cellular Networks. |
Abstract | ||
|---|---|---|
Edge caching has become an effective solution to cope with the challenges brought by the massive content delivery in cellular networks. In device- to-device (D2D) enabled caching cellular networks with time-varying user terminal (UT) movement and content popularity, we model these dynamic networks as a stochastic game to design a cooperative caching placement strategy. We consider the long-term caching placement reward of all UTs. Each UT becomes a learning agent and the caching placement strategy corresponds to the actions taken by the UTs. In an effort to solve the stochastic game problem, we propose a multi- agent cooperative alternating Q-learning (CAQL) caching placement algorithm. We discuss the convergence and complexity of CAQL, which can converge to a stable caching policy with low space complexity. Simulation results show that the proposed algorithm can effectively reduce the backhaul load and the average content access delay in dynamic environment. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GLOBECOM38437.2019.9014053 | GLOBECOM |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyuan Fang | 1 | 0 | 0.34 |
| Tiankui Zhang | 2 | 487 | 62.41 |
| Yuanwei Liu | 3 | 2162 | 131.65 |
| Zhimin Zeng | 4 | 174 | 35.45 |