Name
Title | ||
|---|---|---|
Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges. |
Abstract | ||
|---|---|---|
In the fifth generation (5G) of mobile broadband systems, radio resource management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean RRM architecture that capitalizes on recent advances in the field of machine... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MCOM.2018.1701031 | IEEE Communications Magazine |
Keywords | Field | DocType |
Computer architecture,Machine learning,5G mobile communication,Complexity theory,Task analysis,Radio communication,Resource management | Resource management,Radio resource management,Architecture,Radio access,Task analysis,Computer science,Computer network,Radio access network,Mobile broadband | Journal |
Volume | Issue | ISSN |
56 | 9 | 0163-6804 |
Citations | PageRank | References |
13 | 0.59 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Calabrese | 1 | 242 | 15.93 |
| Li Wang | 2 | 430 | 52.48 |
| Euhanna Ghadimi | 3 | 275 | 13.75 |
| Gunnar Peters | 4 | 21 | 4.39 |
| Lajos Hanzo | 5 | 10889 | 849.85 |
| Pablo Soldati | 6 | 364 | 30.34 |