Name
Title | ||
|---|---|---|
Learning Radio Resource Management in 5G Networks: Framework, Opportunities and Challenges. |
Abstract | ||
|---|---|---|
In the fifth generation (5G) of mobile broadband systems, Radio Resources Management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and with the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean 5G RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture relies on a single general-purpose learning framework conceived for RRM directly using the data gathered in the network. The complexity of RRM is shifted to the design of the framework, whilst the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios and future directions on applications of machine learning to RRM are discussed. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Networking and Internet Architecture | Resource management,Radio resource management,Architecture,Radio access,Computer science,Computer network,Mobile broadband |
DocType | Volume | Citations |
Journal | abs/1611.10253 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Davide Calabrese | 1 | 0 | 0.68 |
| Li Wang | 2 | 34 | 27.42 |
| Euhanna Ghadimi | 3 | 275 | 13.75 |
| Gunnar Peters | 4 | 21 | 4.39 |
| Pablo Soldati | 5 | 364 | 30.34 |