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

Pierre Coucheney (1) , Bruno Gaujal (2) , Panayotis Mertikopoulos (2)

1 PRISM - Parallélisme, Réseaux, Systèmes, Modélisation
2 MESCAL - Middleware efficiently scalable

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.

Domaines: Réseaux et télécommunications [cs.NI]

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⟩