Distributed Learning Policies for Power Allocation in Multiple Access Channels

Details Distributed Learning Policies for Power Allocation in Multiple Access Channels Distributed Learning Policies for Power Allocation in Multiple Access Channels

2011-03-18

arxiv.org/abs/1103.3541v2

IEEE Journal on Selected Areas in Communication, vol. 30, no. 1, pp. 1-11, January 2012

Computer Science

Computer Science and Game Theory

11 pages, 8 figures. Revised manuscript structure and added more material and figures for the case of stochastically fluctuati

Scientific paper

10.1109/JSAC.2012.1201xx

We analyze the problem of distributed power allocation for orthogonal multiple access channels by considering a continuous non-cooperative game whose strategy space represents the users' distribution of transmission power over the network's channels. When the channels are static, we find that this game admits an exact potential function and this allows us to show that it has a unique equilibrium almost surely. Furthermore, using the game's potential property, we derive a modified version of the replicator dynamics of evolutionary game theory which applies to this continuous game, and we show that if the network's users employ a distributed learning scheme based on these dynamics, then they converge to equilibrium exponentially quickly. On the other hand, a major challenge occurs if the channels do not remain static but fluctuate stochastically over time, following a stationary ergodic process. In that case, the associated ergodic game still admits a unique equilibrium, but the learning analysis becomes much more complicated because the replicator dynamics are no longer deterministic. Nonetheless, by employing results from the theory of stochastic approximation, we show that users still converge to the game's unique equilibrium. Our analysis hinges on a game-theoretical result which is of independent interest: in finite player games which admit a (possibly nonlinear) convex potential function, the replicator dynamics (suitably modified to account for nonlinear payoffs) converge to an eps-neighborhood of an equilibrium at time of order O(log(1/eps)).

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