14,298 research outputs found
Distributed stochastic optimization via matrix exponential learning
In this paper, we investigate a distributed learning scheme for a broad class
of stochastic optimization problems and games that arise in signal processing
and wireless communications. The proposed algorithm relies on the method of
matrix exponential learning (MXL) and only requires locally computable gradient
observations that are possibly imperfect and/or obsolete. To analyze it, we
introduce the notion of a stable Nash equilibrium and we show that the
algorithm is globally convergent to such equilibria - or locally convergent
when an equilibrium is only locally stable. We also derive an explicit linear
bound for the algorithm's convergence speed, which remains valid under
measurement errors and uncertainty of arbitrarily high variance. To validate
our theoretical analysis, we test the algorithm in realistic
multi-carrier/multiple-antenna wireless scenarios where several users seek to
maximize their energy efficiency. Our results show that learning allows users
to attain a net increase between 100% and 500% in energy efficiency, even under
very high uncertainty.Comment: 31 pages, 3 figure
Dynamic Multi-Arm Bandit Game Based Multi-Agents Spectrum Sharing Strategy Design
For a wireless avionics communication system, a Multi-arm bandit game is
mathematically formulated, which includes channel states, strategies, and
rewards. The simple case includes only two agents sharing the spectrum which is
fully studied in terms of maximizing the cumulative reward over a finite time
horizon. An Upper Confidence Bound (UCB) algorithm is used to achieve the
optimal solutions for the stochastic Multi-Arm Bandit (MAB) problem. Also, the
MAB problem can also be solved from the Markov game framework perspective.
Meanwhile, Thompson Sampling (TS) is also used as benchmark to evaluate the
proposed approach performance. Numerical results are also provided regarding
minimizing the expectation of the regret and choosing the best parameter for
the upper confidence bound
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