Throughput Optimization for Training-Based Large-Scale Virtual MIMO Systems

We consider large-scale virtual multiple-input multiple-output (MIMO) systems, in which a large number of user terminals communicate with a large number of cooperative bases stations (BSs). We focus on a training-based scheme and investigate the throughput maximization over various system parameters including pilot symbols, the time allocation coefficient a, the power allocation coefficient gamma, and the user number K. Our main contribution is to derive simple throughput expressions by utilizing the random matrix theory, based on which closed-form optimal solutions of (K, gamma, alpha) arc obtained. We show that, for a large but finite coherent time T, the optimal K for throughput optimization satisfies K > T/2 and converges to T/2 as the signal-to-noise ratio goes to infinity