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Derivative-Free Optimization over Multi-User MIMO Networks

Abstract : In wireless communication, the full potential of multiple-input multiple-output (MIMO) arrays can only be realized through optimization of their transmission parameters. Distributed solutions dedicated to that end include iterative optimization algorithms involving the computation of the gradient of a given objective function, and its dissemination among the network users. In the context of large-scale MIMO, however, computing and conveying large arrays of function derivatives across a network has a prohibitive cost to communication standards. In this paper we show that multiuser MIMO networks can be optimized without using any derivative information. With focus on the throughput maximization problem in a MIMO multiple access channel, we propose a "derivative-free" optimization methodology relying on very little feedback information: a single function query at each iteration. Our approach integrates two complementary ingredients: exponential learning (a derivative-based expression of the mirror descent algorithm with entropic regularization), and a single-function-query gradient estimation technique derived from a classic approach to derivative-free optimization.
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Contributor : Olivier Bilenne <>
Submitted on : Wednesday, July 1, 2020 - 3:09:35 PM
Last modification on : Wednesday, August 5, 2020 - 4:13:20 AM


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  • HAL Id : hal-02881559, version 1



Olivier Bilenne, Panayotis Mertikopoulos, Elena Veronica Belmega. Derivative-Free Optimization over Multi-User MIMO Networks. 2020. ⟨hal-02881559⟩



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