TY - GEN
T1 - Deep Learning Based Resource Allocation Method to Control System Capacity and Fairness for MU-MIMO THP
AU - Shimbo, Yukiko
AU - Suganuma, Hirofumi
AU - Tomeba, Hiromichi
AU - Onodera, Takashi
AU - Maehara, Fumiaki
N1 - Funding Information:
This work was supported by Sharp Corporation. The authors would like to thank Y. Hamaguchi of Sharp Corporation for his continuing support.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - This paper proposes a deep-learning-based resource allocation method to adaptively control system capacity and fairness for multi-user multiple-input and multiple-output (MU-MIMO). In the proposed method, Tomlinson-Harashima precoding (THP) is used to enhance the transmission rate. Additionally, channel resources are appropriately allocated based on user scheduling techniques, i.e., semiorthogonal user selection (SUS) for throughput maximization and proportional fairness (PF) for fairness among users. The primary feature of the proposed method is that it appropriately allocates channel resources by utilizing the user position information and target fairness index (FI) through deep learning. This makes it possible to meet various service requirements. Numerical simulations are used to demonstrate the effectiveness of the proposed method in terms of system capacity and fairness under different MIMO configurations and user distributions.
AB - This paper proposes a deep-learning-based resource allocation method to adaptively control system capacity and fairness for multi-user multiple-input and multiple-output (MU-MIMO). In the proposed method, Tomlinson-Harashima precoding (THP) is used to enhance the transmission rate. Additionally, channel resources are appropriately allocated based on user scheduling techniques, i.e., semiorthogonal user selection (SUS) for throughput maximization and proportional fairness (PF) for fairness among users. The primary feature of the proposed method is that it appropriately allocates channel resources by utilizing the user position information and target fairness index (FI) through deep learning. This makes it possible to meet various service requirements. Numerical simulations are used to demonstrate the effectiveness of the proposed method in terms of system capacity and fairness under different MIMO configurations and user distributions.
KW - Multi-user multiple-input and multiple-output (MU-MIMO)
KW - deep learning
KW - fairness index (FI)
KW - proportional fairness (PF)
KW - semiorthogonal user selection (SUS)
KW - system capacity
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U2 - 10.1109/VTC2021-Spring51267.2021.9449018
DO - 10.1109/VTC2021-Spring51267.2021.9449018
M3 - Conference contribution
AN - SCOPUS:85112444152
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
Y2 - 25 April 2021 through 28 April 2021
ER -