Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Convex Optimization
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
A survey on networking games in telecommunications
Computers and Operations Research
Autonomous Spectrum Balancing for Digital Subscriber Lines
IEEE Transactions on Signal Processing
Simultaneous Water Filling in Mutually Interfering Systems
IEEE Transactions on Wireless Communications
Distributed multiuser power control for digital subscriber lines
IEEE Journal on Selected Areas in Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Distributed interference compensation for wireless networks
IEEE Journal on Selected Areas in Communications
Spectrum sharing for unlicensed bands
IEEE Journal on Selected Areas in Communications
Convergence of Iterative Waterfilling Algorithm for Gaussian Interference Channels
IEEE Journal on Selected Areas in Communications
Coopetition: practical methodology for efficient sharing of radio resources in wireless networks
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
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This paper considers the problem of how to allocate power among competing users sharing a frequency- selective interference channel. We model the interaction between these selfish users as a non-cooperative game. We study how a foresighted user, who knows the channel state information and response strategies of its competing users, should optimize its own transmission strategy. To characterize this multiuser interaction, the Stackelberg equilibrium is introduced. We start by analyzing in detail a simple two-user scenario, where the foresighted user can determine its optimal transmission strategy by solving a bi-level program which allows him to account for the myopic user's response strategies. Therefore, the competition among users is transformed into a cooperative competition (coopetition) since the foresighted user will avoid interfering the myopic user. Since the optimal solution is computationally prohibitive, we propose a low-complexity algorithm based on Lagrangian duality theory. Numerical simulations illustrate that, if a foresighted user has the necessary information about its competitor, the resulting coopetition will benefit both users. Possible methods to acquire the required information and to extend the formulation to more than two users are also discussed.