Resource Allocation for Wireless Networks: Basics, Techniques, and Applications
Resource Allocation for Wireless Networks: Basics, Techniques, and Applications
Capacity bounds for the Gaussian interference channel
IEEE Transactions on Information Theory
A new outer bound and the noisy-interference sum-rate capacity for Gaussian interference channels
IEEE Transactions on Information Theory
Correlated equilibrium in access control for wireless communications
NETWORKING'06 Proceedings of the 5th international IFIP-TC6 conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems
Uniform power allocation in MIMO channels: a game-theoretic approach
IEEE Transactions on Information Theory
On the Achievable Sum Rate for MIMO Interference Channels
IEEE Transactions on Information Theory
Distributed multiuser power control for digital subscriber lines
IEEE Journal on Selected Areas in Communications
Non-Cooperative Resource Competition Game by Virtual Referee in Multi-Cell OFDMA Networks
IEEE Journal on Selected Areas in Communications
Competition Versus Cooperation on the MISO Interference Channel
IEEE Journal on Selected Areas in Communications
Crystallized rate regions for MIMO transmission
EURASIP Journal on Wireless Communications and Networking - Special issue on signal processing-assisted protocols and algorithms for cooperating objects and wireless sensor networks
| Hi-index | 0.00 |
Treating the interference as noise in the n-user interference channel, the paper describes a novel approach to the rates region, composed by the time-sharing convex hull of 2n -1 corner points achieved through On/Off binary power control. The resulting rates region is denoted crystallized rates region. By treating the interference as noise, the n-user rates region frontiers has been found in the literature to be the convex hull of n hyper-surfaces. The rates region bounded by these hypersurfaces is not necessarily convex, and thereby a convex hull operation is imposed through the strategy of time-sharing. This paper simplifies this rates region in the n-dimensional space by having only an On/Off binary power control. This consequently leads to 2n - 1 corner points situated within the rates region. A time-sharing convex hull is imposed onto those corner points, forming the crystallized rates region. The paper focuses on game theoretic concepts to achieve that crystallized convex hull via correlated equilibrium. In game theory, the correlated equilibrium set is convex, and it consists of the time-sharing mixed strategies of the Nash equilibriums. In addition, the paper considers a mechanism design approach to carefully design a utility function, particularly the Vickrey-Clarke-Groves auction utility, where the solution point is situated on the correlated equilibrium set. Finally, the paper proposes a self learning algorithm, namely the regret-matching algorithm, that converges to the solution point on the correlated equilibrium set in a distributed fashion.