Abstract
Carrier aggregation is a key feature of next generation wireless networks to deliver high-bandwidth links. This paper studies carrier aggregation for autonomous networks operating in shared spectrum. In our model, networks decide how many and which channels to aggregate in multiple frequency bands, hence extending the distributed channel allocation framework. Moreover, our model takes into the account physical layer issues, such as the out-of-channel interference in adjacent frequency channels and the cost associated with inter-band carrier aggregation. We propose learning algorithms that converge to Nash equilibria in a reasonable number of iterations under the assumption of incomplete and imperfect information.
| Original language | English |
|---|---|
| Title of host publication | 2013 IEEE Global Communications Conference (GLOBECOM) |
| Pages | 1233-1239 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 1 Dec 2013 |
Keywords
- 4G mobile communication
- adjacent channel interference
- channel allocation
- convergence
- game theory
- learning (artificial intelligence)
- Long Term Evolution
- next generation networks
- probability
- radio spectrum management
- repeated game
- learning algorithms
- Nash equilibrium
- next generation wireless networks
- autonomous networks
- shared spectrum
- distributed channel allocation
- out-of-channel interference
- adjacent frequency channels
- interband carrier aggregation
- Benchmark testing
- Interference
- Games
- Mood
- Convergence
- Sensors
- Carrier aggregation
- learning