Distributed Optimization in Multi-User MIMO Systems with Imperfect and Delayed Information

Abstract : Starting from an entropy-driven reinforcement learning scheme for multi-agent environments, we develop a distributed algorithm for robust spectrum management in Gaussian multiple-input, multiple-output (MIMO) uplink channels. In continuous time, our approach to optimizing the transmitters' signal distribution relies on the method of matrix exponential learning, adjusted by an entropy-driven barrier term which generates a distributed, convergent algorithm in discrete time. As opposed to traditional water-filling methods, the algorithm's convergence speed can be controlled by tuning the users' learning rate; accordingly, entropy-driven learning algorithms in MIMO systems converge arbitrarily close to the optimum signal covariance profile within a few iterations (even for large numbers of users and/or antennas per user), and this convergence remains robust even in the presence of imperfect (or delayed) measurements and asynchronous user updates.
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[Research Report] RR-8426, INRIA. 2013, pp.19
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Soumis le : samedi 14 décembre 2013 - 17:54:38
Dernière modification le : jeudi 8 novembre 2018 - 14:28:02
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  • HAL Id : hal-00918762, version 1



Pierre Coucheney, Bruno Gaujal, Panayotis Mertikopoulos. Distributed Optimization in Multi-User MIMO Systems with Imperfect and Delayed Information. [Research Report] RR-8426, INRIA. 2013, pp.19. 〈hal-00918762〉



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