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

Pierre Coucheney 1 Bruno Gaujal 2 Panayotis Mertikopoulos 2 
2 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
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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Submitted on : Saturday, December 14, 2013 - 5:54:38 PM
Last modification on : Tuesday, August 2, 2022 - 4:24:26 AM
Long-term archiving on: : Tuesday, March 18, 2014 - 2:45:24 PM


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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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