NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Graphical congestion games with linear latencies
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures
A note on approximate Nash equilibria
Theoretical Computer Science
Efficient MAC in cognitive radio systems: a game-theoretic approach
IEEE Transactions on Wireless Communications
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Opportunistic spectrum access with multiple users: learning under competition
INFOCOM'10 Proceedings of the 29th conference on Information communications
Game theory for cognitive radio networks: An overview
Computer Networks: The International Journal of Computer and Telecommunications Networking
Competitive spectrum sharing in cognitive radio networks: a dynamic game approach
IEEE Transactions on Wireless Communications
Atomic congestion games on graphs and their applications in networking
IEEE/ACM Transactions on Networking (TON)
Route-switching games in cognitive radio networks
Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing
| Hi-index | 0.00 |
A key feature of wireless communications is the spatial reuse. However, the spatial aspect is not yet well understood for the purpose of designing efficient spectrum sharing mechanisms. In this paper, we propose a framework of spatial spectrum access games on directed interference graphs, which can model quite general interference relationship with spatial reuse in wireless networks. We show that a pure strategy equilibrium exists for the two classes of games: (1) any spatial spectrum access games on directed acyclic graphs, and (2) any games satisfying the congestion property on directed trees and directed forests. Under mild technical conditions, the spatial spectrum access games with random backoff and Aloha channel contention mechanisms on undirected graphs also have a pure Nash equilibrium. We then propose a distributed learning algorithm, which only utilizes users' local observations to adaptively adjust the spectrum access strategies. We show that the distributed learning algorithm can converge to an approximate mixed-strategy Nash equilibrium for any spatial spectrum access games. Numerical results demonstrate that the distributed learning algorithm achieves up to 100% performance improvement over a random access algorithm.