Name
Abstract | ||
|---|---|---|
Fog radio access networks have been proposed as a promising architecture to support diverse scenarios, which provide edge computing capabilities. Meanwhile, terminal awareness can enable wireless networks respond proactively and intelligently. However, due to the acquisition of raw data and the execution of computation tasks, centralized approaches for terminal awareness can put heavy burdens on fronthaul and cloud. To alleviate these burdens and infer the terminal type precisely, a hierarchical approach integrating multiple non-linear Support Vector Machine (SVM) classifiers is proposed. The core idea is that the raw data processing and the training of classifiers are shifted to fog access points (F-APs), while the server in the cloud is only responsible for training task allocation and training data dissemination. Further, the training task allocation is formulated as an integer problem to minimize the total training time, which is solved by a branch and bound based method and a low complexity heuristic method. Simulation result shows that the heuristic method can achieve optimal performance, and the impacts of the heterogeneity in F-AP computing capabilities on the training time and the task allocation are demonstrated and analyzed. |
Year | Venue | Field |
|---|---|---|
2017 | ICCC | Edge computing,Wireless network,Data modeling,Heuristic,Task analysis,Computer science,Support vector machine,Server,Computer network,Cloud computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoxia Huang | 1 | 6 | 2.20 |
| Yaohua Sun | 2 | 153 | 9.72 |
| Chaoliang Zhang | 3 | 0 | 0.34 |
| Mugen Peng | 4 | 2779 | 200.37 |