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the_weighted_sum_rate_maximization_in_mimo_interference_networks-_minimax_lagrangian_duality_and_algorithm

Abstract: We take a new approach to the weighted sum-rate maximization in multiple-input multiple-output (MIMO) interference networks, by formulating an equivalent max-min problem. This reformulation has significant implications: the Lagrangian duality of the equivalent max-min problem provides an elegant way to establish the sum-rate duality between an interference network and its reciprocal, and more importantly, suggests a novel iterative minimax algorithm with monotonic convergence for the weighted sum-rate maximization. The design and the convergence proof of the algorithm use only general convex analysis. They apply and extend to other max-min problems with similar structure, and thus provide a general class of algorithms for such optimization problems. This paper presents a promising step and lends hope for establishing a general method based on the minimax Lagrangian duality for developing efficient resource allocation and interference management algorithms for general MIMO interference networks.

Bio: Lijun Chen is an Assistant Professor of Computer Science and Telecommunications at University of Colorado at Boulder. He received a Ph.D. from California Institute of Technology in 2007, and was a Research Scientist in Computing + Mathematical Science at the same institute before joining Colorado. He was a co-recipient of the Best Paper Award at the IEEE International Conference on Mobile Ad-hoc and Sensor Systems in 2007. His current research interests are in communication networks, power networks, parsimonious recovery and low-rank solutions, and optimization, game theory and their engineering application.

the_weighted_sum_rate_maximization_in_mimo_interference_networks-_minimax_lagrangian_duality_and_algorithm.txt · Last modified: 2016/09/01 19:15 (external edit)