Bargaining and the MISO interference channel
EURASIP Journal on Advances in Signal Processing - Special issue on game theory in signal processing and communications
The MIMO iterative waterfilling algorithm
IEEE Transactions on Signal Processing
Power allocation games for MIMO multiple access channels with coordination
IEEE Transactions on Wireless Communications
Transmission strategies in MIMO ad hoc networks
EURASIP Journal on Wireless Communications and Networking - Special issue on optimization techniques in wireless communications
Decentralized link adaptation for multi-link MIMO interference system
WONS'09 Proceedings of the Sixth international conference on Wireless On-Demand Network Systems and Services
Power games in MIMO interference systems
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
Competitive optimization of cognitive radio MIMO systems via game theory
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
MIMO cognitive radio: a game theoretical approach
IEEE Transactions on Signal Processing
Nash bargaining over MIMO interference systems
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
IEEE Transactions on Signal Processing
Optimal gateway selection in multi-domain wireless networks: a potential game perspective
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Welfare-maximizing correlated equilibria using Kantorovich polynomials with sparsity
Journal of Global Optimization
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We consider a multi-link MIMO interference system in which each link wishes to maximize its own mutual information by choosing its own signal vector, which leads to a multi-player game. We show the existence of a Nash equilibrium and obtain sufficient conditions for the uniqueness of equilibrium. We consider two decentralized link adjustment algorithms called best-response process (a.k.a. iterative water- filling) and gradient-play (an autonomous and non-cooperative version of the well-known gradient ascent algorithm). Under our uniqueness conditions, we establish the convergence of these algorithms to the unique equilibrium provided that the links use some inertia. To improve the efficiency of an equilibrium with respect to the total mutual information by imposing limits on the number of independent data streams, we present a stream control approach using linear transformation of the link covariance matrices. We then show how to decentralize our stream control approach by allowing the links to negotiate the limits on the number of independent data streams that they are willing to impose upon themselves. To achieve this, we introduce a variation of a learning algorithm called "adaptive play" that has desirable convergence properties in potential games with reduced computation.