Riemannian-Geometric Optimization Methods for MIMO Multiple Access Channels

Abstract : We analyze the problem of finding the optimal signal covariance matrix for MIMO multiple access channels 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.
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https://hal.archives-ouvertes.fr/hal-01382304
Contributor : Panayotis Mertikopoulos <>
Submitted on : Sunday, October 16, 2016 - 3:29:24 PM
Last modification on : Thursday, November 8, 2018 - 2:28:02 PM

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Panayotis Mertikopoulos, Aris L. Moustakas. Riemannian-Geometric Optimization Methods for MIMO Multiple Access Channels. ISIT '13: Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013, Unknown, Unknown Region. ⟨hal-01382304⟩

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