Abstract: In this talk, we consider the energy efficiency maximization problem in downlink multi-input multi-output (MIMO) multi-cell systems, where all users suffer from inter-user interference. This is a challenging problem due to several reasons: 1) It is a nonconvex fractional programming problem; 2) The signal-to-interference-and-noise ratio (SINR) in MIMO systems no longer has a scalar form; 3) The power consumption may depend on the transmission rate. We tackle this problem by the successive pseudoconvex approximation approach. We argue that pseudoconvex optimization plays a fundamental role in designing novel iterative algorithms, not only because every locally optimal solution of a pseudoconvex optimization problem is also globally optimal, but also because a descent direction can be built upon the optimal point of a pseudoconvex optimization problem. The proposed algorithms have the following advantages: 1) fast convergence as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration, 2) easy implementation as each approximate problem is natural for parallel computation and its solution has a closed-form expression, and 3) guaranteed convergence to a stationary point or a Karush-Kuhn-Tucker point. The advantages of the proposed algorithm are also illustrated numerically.

Bio: Yang Yang received the B.S. degree in School of Information Science and Engineering, Southeast University, Nanjing, China, in 2009, and the Ph.D. degree in Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology. From Nov. 2013 to Nov. 2015 he had been a postdoctoral research associate at the Communication Systems Group, Darmstadt University of Technology, Darmstadt, Germany. From Dec. 2015 to Oct. 2017 he had been a senior standards and research scientist in Intel, Germany. He joined the University of Luxembourg as a research associate in Nov. 2017. His research interests are in parallel and distributed solution methods in convex optimization and nonlinear programming, with applications in communication networks and signal processing.