Name
Abstract | ||
|---|---|---|
Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning. This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc. The majority of overview works in the field of cognitive radio networks deal with the notions of observation and adaptations, which are not a distinguished cognitive radio networking aspect. Therefore, this paper provides insight into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SURV.2013.030713.00113 | IEEE Communications Surveys and Tutorials |
Keywords | Field | DocType |
cognitive radio,inference mechanisms,learning (artificial intelligence),resource allocation,telecommunication computing,AI field,artificial intelligence field,cognitive radio networks,learning,optimal resource usage,reasoning,wireless networking paradigm,Game theory,Knowledge,Learning,Policy based reasoning,Reasoning,Reinforcement learning | Wireless network,Wireless,Computer science,Computer network,Knowledge management,Resource allocation,Human–computer interaction,Cognitive model,Artificial intelligence, situated approach,Cognition,Cognitive radio,Cognitive network | Journal |
Volume | Issue | ISSN |
15 | 4 | 1553-877X |
Citations | PageRank | References |
32 | 0.86 | 37 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gavrilovska, L. | 1 | 42 | 3.74 |
| Atanasovski, V. | 2 | 38 | 2.40 |
| Irene Macaluso | 3 | 32 | 0.86 |
| Luiz A. DaSilva | 4 | 32 | 0.86 |