Name
Abstract | ||
|---|---|---|
Coded caching is a promising approache to support low latency transmission over broadcast wireless networks. The process of selecting the nodes that forward coded messages can be considered as a set cover problem. However, existing research efforts don't focuse on solving the set cover problem. This paper investigates the problem of the cover problem for coded caching in wireless networks. First, we propose a novel coded caching method using deep neural networks. Then, we establish a mathematical model for cover problem of the coded caching system. Then, we propose a deep learning approach to solve it. Different from previous works, our proposed deep neural architecture uses sequence-to-sequence model to learn the solutions. Finally, numerical results are given to demonstrate the proposed coded caching method have lower load than traditional coded caching method, and that proposed method for solving the cover problem can effectively implement coded caching with lower computational complexity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GLOBECOM38437.2019.9013459 | IEEE Global Communications Conference |
DocType | ISSN | Citations |
Conference | 2334-0983 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengming Zhang | 1 | 10 | 2.14 |
| Yaru Zheng | 2 | 0 | 0.34 |
| Chunguo Li | 3 | 379 | 53.04 |
| Yongming Huang | 4 | 0 | 0.34 |
| Luxi Yang | 5 | 1180 | 118.08 |