On the Throughput of Large-but-Finite MIMO Networks Using Schedulers
Artikel i vetenskaplig tidskrift, 2019

This paper studies the sum throughput of the multi-user multiple-input-single-output networks in the cases with a 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.

finite blocklength analysis


large-but-finite MIMO

genetic algorithm

Massive MIMO

machine learning


Behrooz Makki

Ericsson AB

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Tommy Svensson

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Mohamed-Slim Alouini

King Abdullah University of Science and Technology (KAUST)

IEEE Transactions on Wireless Communications

15361276 (ISSN) 15582248 (eISSN)

Vol. 18 1 152-166 8520925






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