User Scheduling in Massive MIMO

User Scheduling in Massive MIMO: A Joint Deep Learning and Genetic Algorithm Approach

Mohammadkarimi, M. (TU Delft Signal Processing Systems)
Darabi, Mostafa (University of British Colombia)
Maham, Behrouz (Nazarbayev University)

2022

Due to the limited number of radio frequency (RF) chains in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) receivers using analog beamforming/hybrid beamforming, there is a restriction in scheduling the number of users in each transmission time interval. Therefore, fast and low-complexity user scheduling methods based on the instantaneous channel state information (CSI) are needed. In this paper, we propose novel user scheduling methods based on deep learning (DL) to reduce the size of the search space by using the learning capability of a deep neural network (DNN). We formulate the user scheduling combinatorial optimization problem as a regression problem followed by a user separation procedure through decision boundaries that are learned by a trained DNN. The decision boundaries are used to separate the users into two subsets. Then, one of the subsets is selected to be searched to find the users that maximize the sum-rate capacity. The proposed method can achieve a very low outage probability with a few number of searches. In order to achieve ergodic capacity with lower computation complexity, the proposed method is employed in combination with the genetic algorithm (GA) algorithm to take advantage of intelligent initial population selection. Our simulation results show that the proposed user scheduling methods can offer remarkably low complexity.

User scheduling
massive MIMO communications
hybrid beamforming
deep learning
Genetic algorithm

http://resolver.tudelft.nl/uuid:96355ef2-38cc-473b-8c21-cfd0fdcbf673

https://doi.org/10.1109/VTC2022-Spring54318.2022.9860903

IEEE, Piscataway

2023-07-01

978-1-6654-8244-8

Proceedings of the 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

2022 IEEE 95th Vehicular Technology Conference, 2022-06-19 → 2022-06-22, Helsinki, Finland

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 M. Mohammadkarimi, Mostafa Darabi, Behrouz Maham