Conjectural Equilibrium in Multiagent Learning
Machine Learning
Convex Optimization
Analysis of iterative waterfilling algorithm for multiuser power control in digital subscriber lines
EURASIP Journal on Applied Signal Processing
A stackelberg game for power control and channel allocation in cognitive radio networks
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
Stochastic learning solution for distributed discrete power control game in wireless data networks
IEEE/ACM Transactions on Networking (TON)
Power Control in Wireless Cellular Networks
Foundations and Trends® in Networking
Repeated open spectrum sharing game with cheat-proof strategies
IEEE Transactions on Wireless Communications
A survey on networking games in telecommunications
Computers and Operations Research
Autonomous Spectrum Balancing for Digital Subscriber Lines
IEEE Transactions on Signal Processing
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
Near-optimal reinforcement learning framework for energy-aware sensor 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
Non-Cooperative Power Control for Wireless Ad Hoc Networks with Repeated Games
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Competition Versus Cooperation on the MISO Interference Channel
IEEE Journal on Selected Areas in Communications
Competitive Design of Multiuser MIMO Systems Based on Game Theory: A Unified View
IEEE Journal on Selected Areas in Communications
Architecting noncooperative networks
IEEE Journal on Selected Areas in Communications
Dynamic spectrum management with the competitive market model
IEEE Transactions on Signal Processing
An architectural view of game theoretic control
ACM SIGMETRICS Performance Evaluation Review
A Radio Resource Management Framework for Multi-User Multi-Cell OFDMA Networks Based on Game Theory
Wireless Personal Communications: An International Journal
| Hi-index | 35.69 |
This paper considers a noncooperative game in which competing users sharing a frequency-selective interference channel selfishly optimize their power allocation in order to improve their achievable rates. Previously, it was shown that a user having the knowledge of its opponents' channel state information can make foresighted decisions and substantially improve its performance compared with the case in which it deploys the conventional iterative water-filling algorithm, which does not exploit such knowledge. This paper discusses how a foresighted user can acquire this knowledge by modeling its experienced interference as a function ofits own power allocation. To characterize the outcome of the multiuser interaction, the conjectural equilibrium is introduced, and the existence of this equilibrium for the investigated water-filling game is proven. Importantly, we show that both the Nash equilibrium and the Stackelberg equilibrium are special cases of the conjectural equilibrium. We also develop practical algorithms to form accurate beliefs and select desirable power allocation strategies. Numerical simulations indicate that a foresighted user without any a priori knowledge of its competitors' private information can effectively learn how the other users will respond to its actions, and induce the entire system to an operating point that improves both its own achievable rate as well as the rates of the other participants in the water-filling game.