Distributed learning for resource allocation under uncertainty

Panayotis Mertikopoulos (1) , Elena Veronica Belmega (2) , Luca Sanguinetti (3, 4)

1 POLARIS - Performance analysis and optimization of LARge Infrastructures and Systems
2 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
3 LANEAS - Large Networks and Systems Group
4 Dipartimento di Ingegneria dell'Informazione [Pisa]

In this paper, we present a distributed matrix exponential learning (MXL) algorithm for a wide range of distributed optimization problems and games that arise in signal processing and data networks. To analyze it, we introduce a novel stability concept that guarantees the existence of a unique equilibrium solution; under this condition, we show that the algorithm converges even in the presence of highly defective feedback that is subject to measurement noise, errors, etc. For illustration purposes, we apply the proposed method to the problem of energy efficiency (EE) maximization in multi-user, multiple-antenna wireless networks with imperfect channel state information (CSI), showing that users quickly achieve a per capita EE gain between 100% and 400%, even under very high uncertainty.

Mots clés: Matrix exponential learning; Stochastic optimization; Game theory; Variational stability; Uncertainty

Domaines: Optimisation et contrôle [math.OC]

Panayotis Mertikopoulos, Elena Veronica Belmega, Luca Sanguinetti. Distributed learning for resource allocation under uncertainty. GlobalSIP 2016 - IEEE Global Conference on Signal and Information Processing, Dec 2016, Washington, United States. ⟨10.1109/globalsip.2016.7905899⟩. ⟨hal-01382284⟩