Matrix Exponential Learning: Distributed Optimization in MIMO systems

Abstract : We analyze the problem of finding the optimal signal covariance matrix for MIMO MAC by using an approach based on "exponential learning", a novel optimization method which applies more generally to (quasi-)convex problems defined over sets of positive-definite matrices (with or without trace constraints). If the channels are static, the system users converge to a power allocation profile which attains the sum capacity of the channel exponentially fast (in practice, within a few iterations); otherwise, if the channels fluctuate stochastically over time (following e.g. a stationary ergodic process), users converge to a power profile which attains their ergodic sum capacity instead. An important feature of the algorithm is that its speed can be controlled by tuning the users' learning rate; correspondingly, the algorithm converges within a few iterations even when the number of users and/or antennas per user in the system is large.
Complete list of metadatas
Contributor : Elena Veronica Belmega <>
Submitted on : Monday, October 15, 2012 - 1:34:20 PM
Last modification on : Thursday, November 8, 2018 - 2:28:04 PM


  • HAL Id : hal-00741823, version 1



Panayotis Mertikopoulos, Elena Veronica Belmega, Aris Moustakas. Matrix Exponential Learning: Distributed Optimization in MIMO systems. IEEE International Symposium on Information Theory (ISIT), Jul 2012, United States. pp.3028 -- 3032. ⟨hal-00741823⟩



Record views