Throughput Analysis of Large-but-Finite MIMO Networks using Schedulers

Abstract

We study the sum throughput of multiple-input-multiple-output (MIMO) networks in the cases with large but finite number of transmit and receive data terminals. We develop an efficient scheduling scheme using genetic algorithms (GAs), and evaluate the effect of various parameters, such as channel/precoding models, number of antennas/users, scheduling costs and power amplifiers efficiency, on the system performance. Also, considering continuous and bursty communication scenarios with different users\u27 data request probabilities, we derive closed-form expressions for the maximum achievable throughput of the MIMO networks using optimal schedulers. As we show, our proposed GA-based scheduler reaches (almost) the same throughput as in the exhaustive search-based optimal scheduler, with substantially less implementation complexity. Also, the power amplifiers inefficiency affect the network throughput significantly. For instance, consider a MIMO setup with a 40-antenna base station, 60 users, total consumed power of 26 dB, continuous communications and the typical parameter settings of the power amplifiers. Then, the network throughput decreases by 50% when the power amplifiers efficiency reduces from 75% to 25%