TY - GEN
T1 - ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO
AU - An, Qing
AU - Zafari, Mehdi
AU - Dick, Chris
AU - Segarra, Santiago
AU - Sabharwal, Ashutosh
AU - Doost-Mohammady, Rahman
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce a new adaptive MCS selection framework for massive MIMO systems that operates without any feedback from users by solely relying on instantaneous uplink channel estimates.Our proposed method can effectively operate in multi-user scenarios where user feedback imposes excessive delay and bandwidth overhead. To learn the mapping between the user channel matrices and the optimal MCS level of each user, we develop a Convolutional Neural Network (CNN)-Long Short-Term Memory Network (LSTM)-based model and compare the performance with the state-of-the-art methods.
AB - As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce a new adaptive MCS selection framework for massive MIMO systems that operates without any feedback from users by solely relying on instantaneous uplink channel estimates.Our proposed method can effectively operate in multi-user scenarios where user feedback imposes excessive delay and bandwidth overhead. To learn the mapping between the user channel matrices and the optimal MCS level of each user, we develop a Convolutional Neural Network (CNN)-Long Short-Term Memory Network (LSTM)-based model and compare the performance with the state-of-the-art methods.
KW - Adaptive MCS Selection
KW - Channel State Information
KW - Convolutional Neural Network
KW - Feedback Delay
KW - Long Short-Term Memory Net-work
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85190377706&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85190377706&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF59524.2023.10476866
DO - 10.1109/IEEECONF59524.2023.10476866
M3 - Conference contribution
AN - SCOPUS:85190377706
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 157
EP - 161
BT - Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023
A2 - Matthews, Michael B.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023
Y2 - 29 October 2023 through 1 November 2023
ER -