On the Throughput of Large-but-Finite MIMO Networks using Schedulers

This paper studies the sum throughput of the {multi-user} multiple-input-single-output (MISO) networks in the cases with large but finite number of transmit antennas and users. Considering continuous and bursty communication scenarios with different users' data request probabilities, we derive quasi-closed-form expressions for the maximum achievable throughput of the networks using optimal schedulers. The results are obtained in various cases with different levels of interference cancellation. Also, we develop an efficient scheduling scheme using genetic algorithms (GAs), and evaluate the effect of different parameters, such as channel/precoding models, number of antennas/users, scheduling costs and power amplifiers' efficiency, on the system performance. Finally, we use the recent results on the achievable rates of finite block-length codes to analyze the system performance in the cases with short packets. As demonstrated, the proposed GA-based scheduler reaches (almost) the same throughput as in the exhaustive search-based optimal scheduler, with substantially less implementation complexity. Moreover, the power amplifiers' inefficiency and the scheduling delay affect the performance of the scheduling-based systems significantly

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

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%