Rate of convergence for minimum power assignment algorithms in cellular radio systems
Wireless Networks - Special issue transmitter power control
From External to Internal Regret
The Journal of Machine Learning Research
Oblivious interference scheduling
Proceedings of the 28th ACM symposium on Principles of distributed computing
Online capacity maximization in wireless networks
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Distributed algorithms for approximating wireless network capacity
INFOCOM'10 Proceedings of the 29th conference on Information communications
Distributed contention resolution in wireless networks
DISC'10 Proceedings of the 24th international conference on Distributed computing
Literature Survey on Power Control Algorithms for Mobile Ad-hoc Network
Wireless Personal Communications: An International Journal
Wireless capacity with oblivious power in general metrics
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
A framework for uplink power control in cellular radio systems
IEEE Journal on Selected Areas in Communications
Wireless capacity with arbitrary gain matrix
ALGOSENSORS'11 Proceedings of the 7th international conference on Algorithms for Sensor Systems, Wireless Ad Hoc Networks and Autonomous Mobile Entities
Distributed connectivity of wireless networks
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
Approximation algorithms for wireless link scheduling with flexible data rates
ESA'12 Proceedings of the 20th Annual European conference on Algorithms
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We study two (classes of) distributed algorithms for power control in a general model of wireless networks. There are n wireless communication requests or links that experience interference and noise. To be successful a link must satisfy an SINR constraint. The goal is to find a set of powers such that all links are successful simultaneously. A classic algorithm for this problem is the fixed-point iteration due to Foschini and Miljanic [8], for which we prove the first bounds on worst-case running times - after roughly O(n log n) rounds all SINR constraints are nearly satisfied. When we try to satisfy each constraint exactly, however, convergence time is infinite. For this case, we design a novel framework for power control using regret learning algorithms and iterative discretization. While the exact convergence times must rely on a variety of parameters, we show that roughly a polynomial number of rounds suffices to make every link successful during at least a constant fraction of all previous rounds.