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 multi-user 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.
CITATION STYLE
Bilenne, O., Mertikopoulos, P., & Belmega, E. V. (2021). Derivative-Free Optimization over Multi-user MIMO Networks. In Communications in Computer and Information Science (Vol. 1354 CCIS, pp. 17–24). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87473-5_3
Mendeley helps you to discover research relevant for your work.