Ultra-Reliable and Low Latency Communication in mmWave-Enabled Massive MIMO Networks

Ultra-reliability and low-latency are two key components in 5G networks. In this letter, we investigate the problem of ultra-reliable and low-latency communication (URLLC) in millimeter wave (mmWave)-enabled massive multiple-input multiple-output (MIMO) networks. The problem is cast as a network utility maximization subject to probabilistic latency and reliability constraints. To solve this problem, we resort to the Lyapunov technique whereby a utility-delay control approach is proposed, which adapts to channel variations and queue dynamics. Numerical results demonstrate that our proposed approach ensures reliable communication with a guaranteed probability of 99.99%, and reduces latency by 28.41% and 77.11% as compared to baselines with and without probabilistic latency constraints, respectively.Comment: Accepted May 12, 2017 by IEEE Communications Letters. Topic is Ultra-Reliable and Low Latency Communication in 5G mmWave Network

Identification of Structured LTI MIMO State-Space Models

The identification of structured state-space model has been intensively studied for a long time but still has not been adequately addressed. The main challenge is that the involved estimation problem is a non-convex (or bilinear) optimization problem. This paper is devoted to developing an identification method which aims to find the global optimal solution under mild computational burden. Key to the developed identification algorithm is to transform a bilinear estimation to a rank constrained optimization problem and further a difference of convex programming (DCP) problem. The initial condition for the DCP problem is obtained by solving its convex part of the optimization problem which happens to be a nuclear norm regularized optimization problem. Since the nuclear norm regularized optimization is the closest convex form of the low-rank constrained estimation problem, the obtained initial condition is always of high quality which provides the DCP problem a good starting point. The DCP problem is then solved by the sequential convex programming method. Finally, numerical examples are included to show the effectiveness of the developed identification algorithm.Comment: Accepted to IEEE Conference on Decision and Control (CDC) 201

Sequential Convex Programming Methods for Solving Nonlinear Optimization Problems with DC constraints

This paper investigates the relation between sequential convex programming (SCP) as, e.g., defined in [24] and DC (difference of two convex functions) programming. We first present an SCP algorithm for solving nonlinear optimization problems with DC constraints and prove its convergence. Then we combine the proposed algorithm with a relaxation technique to handle inconsistent linearizations. Numerical tests are performed to investigate the behaviour of the class of algorithms.Comment: 18 pages, 1 figur